ARTICLE | doi:10.20944/preprints202307.1397.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Harmonization; Surface Reflectance; Landsat-7; Landsat-8; Sentinel-2; Mediterranean basin
Online: 20 July 2023 (10:49:30 CEST)
In the Mediterranean area, vegetation dynamics and phenology analyzed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on one hand, allowing to obtain longer historical series of reflectance data, while on the other hand, making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most used harmonization coefficients proposed in the literature, revealing significant differences. The root mean square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values lead to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area.
ARTICLE | doi:10.20944/preprints202007.0065.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: NDVI; EVI; Wheat; Yield forecast; Landsat 8
Online: 5 July 2020 (11:14:40 CEST)
Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in Jász-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.
ARTICLE | doi:10.20944/preprints202207.0048.v1
Subject: Environmental And Earth Sciences, Oceanography Keywords: ocean color; sun glint; atmospheric correction; Landsat 8
Online: 4 July 2022 (09:57:15 CEST)
Sun glint, i.e., direct solar radiation reflected from a water surface, negatively affects the accuracy of ocean color retrieval schemes if entering the field-of-view of the observing instrument. Herein, a simple and robust method to quantify the sun glint contribution to top-of-atmosphere (TOA) reflectances in the visible (VIS) and near-infrared (NIR) is proposed, exploiting concomitant observations of the sun glint’s morphology in the shortwave infrared (SWIR) characterized by reflectance contrasts typically higher than those resulting from other in-water or atmospheric processes. The proposed method, termed Glint Removal through Contrast Minimization (GRCM), requires high spatial resolution (ca. 10–50 m) imagery to resolve the sun glint’s characteristic morphology, meeting additional criteria on radiometric resolution and temporal delay between the individual band’s acquisitions. It has been applied with good success to a selection of Landsat 8 (L8) Operational Land Imager (OLI) scenes encompassing a wide range of environmental conditions in terms of observation geometry and glint intensity, as well as aerosol and Rayleigh optical depth. The method proposed herein is entirely image based and does not require ancillary information on the sea surface roughness or related parameters (e.g., surface wind), neither the presence of clear water areas in the image under consideration. Limitations of the proposed method are discussed, and its potential for sensors other than OLI and applications beyond glint removal is sketched.
ARTICLE | doi:10.20944/preprints202204.0250.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: soil salinity; EC; Landsat 8 and Sentinel-2A
Online: 27 April 2022 (05:40:14 CEST)
Soil salinity is a severe soil degradation problem mainly faced in arid and semi-arid regions. About 11 million ha of land in the arid, semi-arid, and desert parts of Ethiopia is salt-affected, especially in the Awash River basin, including Afambo irrigated area. Remote sensing approaches are significant tools for accurately predicting and modeling accurately predicting and modeling soil salinity in various world regions. This study aims to analyze and model soil salinity status in the case of Afambo irrigated areas using Landsat-8 and sentinel-2A, Afar region, Ethiopia, by applying remote sensing with field measurements. Thirty-two soil samples were collected from the topsoil (0-30 cm); out of these, 25 soil samples with various EC ranges were selected for modeling, and the remaining 7 samples were utilized to validate the model. Landsat-8 and Sentinel-2A images acquired in the same month were used to extract soil salinity indices. Linear regression analyses correlated the EC data with corresponding soil salinity spectral index values derived from satellite images. The best-performing model was selected for salinity mapping. The soil salinity indices extracted from both Landsat-8 and Sentinel-2A bands estimated soil salinity with high acceptable accuracy of R2 values of SI, 0.78 and 0.81, respectively. The model results in three salinity classes with varying degree of salinity, namely, highly saline, moderately saline, and slightly saline, which covers 15.1%, 39.8% and 45.1% of the total area for Landsat-8, respectively and 26.1%, 32%, and 41.9% for sentinel 2A, respectively. Generally, the results revealed that the expansion rate of salt-affected soils has been increasing. From this study, it is possible to infer that if the present irrigation practice continues, it is expected that total the cultivated lands will become sterile within a short period. Thus, it needs to be monitored regularly to secure up-to-date knowledge of their extent to improve management practices and take appropriate actions.
ARTICLE | doi:10.20944/preprints202106.0727.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Boreal Forest; LiDAR; Landsat 8; Surface Reflectance; Alaska
Online: 30 June 2021 (09:51:47 CEST)
Forests are critical in regulating the world’s climate and they maintain overall Earth’s energy balance. The variability in forest canopy structure, topography and underneath vegetation background condition creates uncertainty in the estimation and modelling of Earth’s surface radiation particularly for boreal regions in high latitude. We studied seasonal variation in surface reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska by fusing together Landsat 8 surface reflectance and LiDAR-derived canopy matrices. Our study shows that canopy structure and topography interplay and influence surface reflectance in a complex way particularly during the snow season. Topographic aspect and elevation control vegetation growth, type and structure. The southern slope is featured with more deciduous and taller trees having greater rugosity than the northern slope. Higher elevation is associated with taller trees for both vegetation types, particularly in the southern slope. In general, surface reflectance shows similar relationships with canopy cover, height and rugosity, mainly due to close relationships between these parameters. Surface reflectance decreases with canopy cover, tree height, and rugosity especially for evergreen forest. Deciduous forest shows larger variability of surface reflectance, particularly in March, mainly due to the mixing effect of snow and vegetation. The relationship between vegetation structure and surface reflectance is greatly impacted by topography. The negative relationship between elevation and surface reflectance may be due to taller and denser vegetation distribution in higher elevation. Surface reflectance in the southern slope is slightly larger than the northern slope for both deciduous and evergreen forest. The shadow effect from topography and tree crowns on surface reflectance play a different role for deciduous and evergreen forests. For deciduous forest, topographic shadow effect on surface reflectance is stronger than from tree shadowing in all seasons. For evergreen forest, shadow effects from topography and tree crowns on surface reflectance are both equally dominant, however tree shadow effect is more significant in March than in May and August. The generalized additive models (GAM) based on non-linear relationships between response (surface reflectance) and predictor (canopy structures and topography) variables confirms such observations. Our study not only provides accurate quantification of surface radiation budget but also helps in parametrization of climate change models.
ARTICLE | doi:10.20944/preprints201804.0203.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: soil salinity; arid; semi-arid; Landsat 8 OLI
Online: 16 April 2018 (10:18:42 CEST)
Soil salinity, whether natural or human induced, is a major geo-hazard in arid and semi-arid landscapes. In agricultural lands, it negatively affects plant growth, crop yields, whereas in semi-arid and arid non-agricultural areas it affects urban structures due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation Accurately mapping soil salinity through remote sensing techniques has been an active area of research in the past few decades particularly for agricultural lands. Most of this research has focused on the utilization and development of salinity indices for properly mapping and identifying saline agricultural soils. This research study develops a soil salinity index and model using Landsat 8 OLI image data from the near infra-red and shortwave infra-red spectral information with emphasis on soil salinity mapping and assessment in non-agricultural desert arid and semi-arid surfaces. The developed index when integrated into a semi-empirical model outperformed in its soil salinity mapping overall accuracy (60%) in comparison to other salinity indices (~50%). The newly developed index further outperformed other indices in its accuracy in mapping and identifying high saline soils (67%) and excessively high saline soils (90%).
ARTICLE | doi:10.20944/preprints202009.0212.v1
Subject: Environmental And Earth Sciences, Oceanography Keywords: VNREDSat-1/NAOMI; Landsat-8/OLI; Suspended particulate matter; algorithm
Online: 9 September 2020 (13:49:49 CEST)
VNREDSat-1 is the first Vietnamese satellite allowing the survey of environmental parameters such as vegetation and water coverages, or surface water quality at medium spatial resolution (from 2.5 to 10 meters depending on the considered channel). The NAOMI sensor on board VNREDSat-1 has the required spectral bands to assess the suspended particulate matter concentration, SPM. Because recent studies have shown that the remote sensing reflectance, Rrs(), at the blue (450 – 520 nm), green (530 – 600 nm), and red (620 – 690 nm) spectral bands can be assessed from NAOMI with a good accuracy, the present study is dedicated to the development and validation of an algorithm (hereafter referred to as V1SPM) to assess SPM from Rrs() over inland and coastal waters of Vietnam. For that purpose, an in situ data set of hyper-spectral Rrs() and SPM (from 0.47 to 240.14 g.m-3) measurements collected at 205 coastal and inland stations has been gathered. Among the different approaches, including 4 historical algorithms, the polynomial algorithms involving the red-to-green reflectance ratio presents the best performance on the validation data set (MAPD of 18,7%). Compared to the use of a single spectral band, the band ratio allows to reduce the scatter around the polynomial fit as well as the impact of imperfect atmospheric corrections. Due to the lack of matchup data points with VNREDSat-1, the full VNREDSat-1 processing chain (RED-NIR and V1SPM) aiming at estimate SPM from the top-of-atmosphere signal has been applied to the Landsat-8/OLI match-up data points with relatively low to moderate SPM concentration (3.33-15.25 g.m-3) showing a MAPD of 15,8%. An illustration of the use of this VNREDSat-1 processing chain during a flooding event occurring in Vietnam is provided.
ARTICLE | doi:10.20944/preprints201805.0186.v1
Subject: Biology And Life Sciences, Insect Science Keywords: rice landscape; natural enemies; location; population dynamics; variography; LANDSAT 8
Online: 14 May 2018 (10:13:50 CEST)
Relationships among the population abundance of four predator groups for rice insect pests, namely: carabid beetles, staphylinid beetles, green mirid bugs, and spiders in three landscape categories were evaluated. Both rice plots and the associated bund margins of these rice plots found among three Bangladesh landscape categories were sampled by sweep net. The results revealed that the abundance significantly varied across landscapes. The rice landscape of one location harbored higher numbers of a specific predator than other location in other regions of Bangladesh. The results also showed a dependency on the width of the rice bund margins of the rice plots, where spiders populations increased with increased bund widths, but the population abundance of these predators did not depend on the diversity of the number of weed species found on the rice bund margins. The relative abundance of predator populations also significantly differed among the three landscapes, with the green mirid bug having the highest number among the four predators. This study indicates that predators of rice insect pests are highly landscape specific. In order to design integrated pest management systems for different Bangladeshi rice production locales, considerations unique to the characteristics of each locale are necessary. Preliminary efforts to apply variography analyses to the RED spectral band of LANDSAT 8 imagery from December 2016 are presented as first step toward learning a suite of methods which describe useful local characteristics affecting rice pest predators.
ARTICLE | doi:10.20944/preprints202306.0119.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Submerged aquatic vegetation; Carbon balance model; Landsat 8/9-OLI; Deep learning
Online: 2 June 2023 (03:42:30 CEST)
: Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learn-ing-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) tech-niques. A U-Net based model, Wetland-SAV Network, was trained to identify SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha) with a significant loss of SAV habitats observed post-Hurricane Barry (~2,300 ha) in 2019. The habitat areas and habitat-specific carbon fluxes were then used to quantify net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a Carbon Balance Model. The total net GHG flux was in the range of -0.13 ± 0.06 to -0.86 ± 0.37 ×105 tonne CO2e yr-1 and increased up to 40% (-0.23 ± 0.10 to -0.90 ± 0.39 ×105 tonne CO2e yr-1 ) when SAV was accounted for within the calculation. At the hectare scale, inclusion of SAV resulted in an increase of ~60% for net GHG sink in shallow areas adjacent to emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring of SAV in turbid sub-tropical coastal deltas as a component of increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.
ARTICLE | doi:10.20944/preprints202307.1043.v2
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Image classification; Land use/land cover mapping; Accuracy assessment; Landsat-8; Snetinel-2
Online: 14 August 2023 (09:01:24 CEST)
Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combi-nations of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Copernicus Land Cover (CLC2018) nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2a MSI for an acceptable overall accuracy over 85%. A comparison of the overall classification accuracies shows that Sentinel-2a overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards inte-grating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.
ARTICLE | doi:10.20944/preprints201810.0695.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Urban Remote Sensing; Sentinel-1; Landsat 8; Built-Up; Data Fusion; Texture; Africa
Online: 29 October 2018 (16:02:53 CET)
The rapid urbanization that takes place in developing regions such as Sub-Saharan Africa is associated with a large range of environmental and social issues. In this context, remote sensing is essential to provide accurate and up-to-date spatial information to support risk assessment and decision making. However, mapping urban areas remains a challenge because of their heterogeneity, especially in developing regions where the highest rates of misclassification are observed. Nevertheless, urban areas located in arid climates --- which are among the most vulnerables to anthropogenic impacts, suffer from the spectral confusion occurring between built-up and bare soil areas when using optical imagery. Today, the increasing availability of satellite imagery from multiple sensors allow to tackle the aforementioned issues by combining optical data with Synthetic Aperture Radar (SAR). In this paper, we assess the complementarity of the Landsat 8 and Sentinel-1 sensors to map built-up areas in twelve Sub-Saharan African urban areas, using a pixel-level supervised classification based on the Random Forest classifier. We make use of textural information extracted from SAR backscattering data in order to reduce the speckle noise and to introduce contextual information at the pixel level. Results suggest that combining both optical and SAR features consistently improves classification performances, mainly by enhancing the differentiation between built-up and bare lands. However, the fusion was less beneficial in mountainous case studies, suggesting that including features derived from a Digital Elevation Model (DEM) could improve the reliability of the proposed approach. As suggested by previous studies, combining features computed from both VV and VH polarizations consistently led to better classification performances. On the contrary, introducing textures computed from different spatial scales did not improve the classification performances.
ARTICLE | doi:10.20944/preprints202209.0416.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GEE; Landsat 8 OLI; Multi-linear regression; Remote Sensing; Vegetation indices; Wheat and barley
Online: 27 September 2022 (09:35:20 CEST)
Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing technology, the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R2 of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R2 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R2 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R2 0.1) and SAVI for Landsat image (R2 0.47).
ARTICLE | doi:10.20944/preprints201812.0067.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: built-up area; classification; Landsat 8- OLI; feature engineering; feature learning; CNN; accuracy evaluation
Online: 5 December 2018 (12:06:34 CET)
Detailed built-up area information is valuable for mapping complex urban environments. Although a large number of classification algorithms about built-up areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore we launched a unique investigation to provide a full test of the OLI imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we propose a method based on the ESA's 38-meter global built-up area data of 2014, Open Street Map and MOD13Q1-NDVI to achieve rapid and automatic generation of a large number of sample points. Our aim is to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape and texture as the input features, and SVM, random forest (RF) and AdaBoost as the classification algorithms. In feature learning, the convolution neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. Experimental results show that: (1) the approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers e.g., RF, is comparable to that of CNN. Two dimensional CNN and the 7 neighborhood RF have the highest classification accuracy of nearly 91%. (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (for example, PanTex and EMBI) can help improve classification accuracy.
ARTICLE | doi:10.20944/preprints201711.0075.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: water quality; eutrophication; tropic state index; Landsat-8, RapidEye, tropical inland water bodies, Brazil
Online: 13 November 2017 (03:33:35 CET)
We aimed at analyzing Chlorophyll-a and CDOM dynamics from field measurements and at assessing the potential of multispectral satellite data for retrieving water-quality parameters in three small surface reservoirs in the Brazilian semiarid region. More specifically, this work comprises i) analysis of Chl-a and trophic dynamics; ii) characterization of CDOM; iii) estimation of Chl-a and CDOM from OLI/Landsat-8 and RapidEye imagery. The monitoring lasted 20 months within a multi-year drought, which contributed to water-quality deterioration. Chl-a and trophic state analysis showed a highly eutrophic status for the perennial reservoir during the entire study period, while the non-perennial reservoirs ranged from oligotrophic to eutrophic, with changes associated with the first events of the rainy season. CDOM characterization suggests that the perennial reservoir is mostly influenced by autochthonous sources, while allochthonous sources dominate the non-perennial ones. Spectral-group classification assigned the perennial as CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially successful: the Chl-a was best modelled using RapidEye for the perennial; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed high potential for retrieving water quality parameters in dry areas with small reservoirs.
ARTICLE | doi:10.20944/preprints202301.0477.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Water quality; remote sensing; Sentinel-2; Landsat 8; TSM; CDOM; Secchi depth; Turbidity; Chlorophyll-a
Online: 26 January 2023 (09:12:01 CET)
Water quality is the measure of chemical, physical and biological suitability of water in relation to natural effects and intended purpose which may affect human health and aquatic life. Assessment of water quality is very essential for the management of water resources and human health. Traditionally, in-situ measurements have been used to obtain the water quality parameters of the water bodies. However, with the availability of satellite images, researchers have shown that satellite images are a reliable tool that can be used to estimate water quality. Satellite image-derived water quality parameters provide extensive spatial extent and large temporal variations when compared to traditional in situ sample collection and laboratory measurements. The present work estimated several parameters for quality of water in the Kamuzu reservoir of Lilongwe River for the 2013-2020 period using Sentinel-2 and Landsat-8 satellite images. The band ratio algorithms were used to retrieve Chlorophyll a (Chl-a), Turbidity, Total Suspended Matter (TSM), Secchi depth, Coloured Dissolved Organic Matter (CDOM), and Cyanobacteria from the reservoir. Turbidity and TSM were compared with the in-situ data collected over the same period. The comparison indicated R2 of 0.9 and 0.69 for TSM and Turbidity respectively from Sentinel-2 images whereas R2 of 0.56 and 0.61 was obtained using Landsat 8 images which are quite encouraging. The other set of results included the spatial distribution maps of water quality parameters using Landsat-8 and Sentinel-2 satellite data. It was observed that the spatial distribution of water quality parameters, except for CDOM and Cyanobacteria, showed very good distribution and matches with the theoretical results. However, for CDOM and Cyanobacteria, the distribution was almost similar for the entire study area and the band ratio algorithm may not be able to estimate them quite reasonably. This research reiterates the need for the use of remote sensing in estimating the water quality parameters and may be a substitute to the in-situ data, in terms of spread and frequency, which is very common to most of the water bodies, across the globe.
ARTICLE | doi:10.20944/preprints201609.0081.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: spectral reflectance; vegetation indices; vegetation; remote sensing; oil spill; mangrove forest; oil pollution; Landsat 8
Online: 23 September 2016 (06:19:49 CEST)
This study is aimed at demonstrating application of vegetation spectral techniques for detection and monitoring of impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G/NIR) and green-shortwave infrared (G/SWIR) from the spill sites (SS) and non-spill (NSS) sites in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G/NIR and G/SWIR indicated certain level difference between vegetation condition at the SS and the NSS were significant with p-value <0.5 in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G/NIR - p-value 0.01 and GSWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G/NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post—spill analysis show that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique is essential for real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in the mangrove forest.
ARTICLE | doi:10.20944/preprints201608.0149.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: landsat 8 OLI; Nalban Lake; East Kolkata Wetland; chlorophyll-a prediction; study points; validation points
Online: 15 August 2016 (13:51:19 CEST)
1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R2 value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.
ARTICLE | doi:10.20944/preprints201811.0515.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: crashed aircraft; NDVI; albedo; MH370; remote sensing; Landsat 8; disaster; Boeing 777; panchromatic band; thermal band
Online: 21 November 2018 (05:09:14 CET)
Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.
ARTICLE | doi:10.20944/preprints201612.0141.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: automated water extraction; landsat 8 Operational Land Imager (OLI); modified histogram bimodal method (MHBM); remote sensing
Online: 29 December 2016 (10:49:38 CET)
Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.
ARTICLE | doi:10.20944/preprints202102.0338.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Forests; biomass; ALOS-2 PALSAR-2; Sentinel-1 CSAR; Sentinel-2 MSI; Landsat 8 OLI; ensemble learning.
Online: 16 February 2021 (14:15:01 CET)
This paper presents ensemble learning of multi-source satellite sensors dataset to obtain better predictive performance of the forest biomass. Spectral, spectral-indices, and spectral-textural features were generated from two optical satellite sensors, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). In addition, two radar satellite sensors, Sentinel-1 C-band Synthetic Aperture Radar (CSAR), and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) were utilized to generate backscattering and backscattering-textural features. The plot-wise above ground biomass data available from five forests in New England region were utilized. Ensemble learning of multi-source satellite sensors dataset was carried out by employing four machine learning regressors namely, Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and Multilayer Perceptron (MLP). A five-fold cross-validation method was used to evaluate predictive performance of the multi-source satellite sensors. The integration of multi-source satellite features, comprising of spectral, spectral-indices, backscattering, spectral-textural, and backscattering-textural information, through ensemble learning and cross-validation approach implemented in the research showed promising results (R2 = 0.81, RMSE = 46.2 Mg/ha) for the estimation of plots-level forest biomass in New England region.
ARTICLE | doi:10.20944/preprints202009.0664.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: radiative transfer equation; improved mono-window; generalized single-channel; split-window; LANDSAT-8; urban land surface temperature
Online: 27 September 2020 (04:59:36 CEST)
Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.
ARTICLE | doi:10.20944/preprints201808.0301.v1
Subject: Environmental And Earth Sciences, Geography Keywords: Salinity intrusion; climate change; rising sea level; electrical conductivity; Landsat 8 OLI; Tra Vinh Province; Mekong Delta
Online: 17 August 2018 (11:41:14 CEST)
Salinity intrusion is one of the most serious consequences of climate change coupled with rising sea level that significantly affects agricultural activities in many parts of the world. This phenomenon has increasingly become more serious and frequently occurred in the Mekong Delta of Vietnam. As a result, Vietnam has been ranked among top five countries where have been devastatingly impacted by climate change, in particular, its Tra Vinh Province characterized by coastal plain and alluvial deposit. In addition, this area is of the tropical monsoon zone of long rainy season with source of salt brought from the sea by the tides and sea level rise. Regions that are contaminated by salt are located in lowland and often suffer from floods linking to tidal effects with salty water from river systems and channels. Soil salinity evaluation is critical for coastal protection, restoration, and agricultural planning since it can be considered as an agricultural indicator to evaluate quality of soil. Here, we attempt to estimate the soil salinity in Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI images are utilized to derive indices for soil salinity evaluation including single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Subsequently, satistical analysis between soil salinity, electrical conductivity (EC, dS/m), and environmental indices derived from Landsat 8 OLI image is performed. Results indicate that spectral value of Near Infrared (NIR) band and VSSI are highly correlated with EC (R2 = 0.7779 and R2 = 0.6957, respectively) in comparison with the other indices. Comparative results show that soil salinity derived from Landsat 8 is consistent with in situ data. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by providing the base map of soil salinity contamination for better selection of accomodating crop types to reduce economical lost in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be applied in the other regions.
ARTICLE | doi:10.20944/preprints202307.0594.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: High and medium multi-spectral imaging (Landsat 8, Sentinel-2); Airborne imagery; Normalized Difference Water Index; Geographical measurements
Online: 10 July 2023 (11:07:57 CEST)
We performed the accuracy assessment of three different Normalized Difference Water Indices (NDWIs) in water bodies during April 2019, a period in which floods occurred in a large proportion of the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging, and the reliability of three NDWIs to highlight shallow water features in satellite images, was evaluated using a high resolution airbone imagery as ground-truth. It is shown that these indices computed using Landsat 8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.
ARTICLE | doi:10.20944/preprints201812.0090.v3
Subject: Engineering, Control And Systems Engineering Keywords: deep convolutional neural networks; multi-class segmentation; global convolutional network; channel attention; transfer learning; ISPRS Vaihingen; Landsat-8
Online: 4 January 2019 (11:47:42 CET)
In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc, on raster images. A deep convolutional encoder--decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, "channel attention'' is presented in our network in order to select the most discriminative filters (features). Third, "domain-specific transfer learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.
ARTICLE | doi:10.20944/preprints201812.0328.v1
Subject: Chemistry And Materials Science, Nanotechnology Keywords: ZIF-8; hollow carbon; antibiotics; adsorbent
Online: 28 December 2018 (04:20:40 CET)
The harmful nature of high concentrations of antibiotics to humans and animals requires urgent development of novel materials and techniques for their absorption. In this work, CTAB (Cetyltrimethyl Ammonium Bromide)-assisted synthesis of ZIF-8 (zeolitic imidazolate framework) derived hollow carbon (ZHC) was designed, prepared and used as a high-performance adsorbent, further evaluated by Langmuir and Freundlich isothermal adsorption experiments, dynamic analysis as well as theoretical calculation. The maximum capacities of ZHC on adsorbing tetracycline (TC), norfloxacin (NFO) and levofloxacin (OFO) are 267.3, 125.6 and 227.8 mg g-1, respectively, which delivers superior adsorptive performance when compared to widely studied inorganic adsorbates. The design concept of ZIFs-derived hollow carbon material provides guidance and insights for the efficient adsorbent of environmental antibiotics.
ARTICLE | doi:10.20944/preprints202103.0494.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Landmass expansion; India Coast; Landsat Images
Online: 19 March 2021 (08:56:01 CET)
This study explores the changes in the landmass bounded by the coast of India during 1975-2005 by using on-screen visual interpretation technique (with 100m resolution and 1:50,000 scale) from NASA Landsat Imagery in three different time periods viz. 1975, 1990, and 2005. The result indicated an overall expansion of 130 sq. km area of the landmass that surrounded by the Indian coast during 1975-2005 (74 sq. km during 1975-1990 and 56 sq. km during 1991-2005). These estimations are based on the preliminary analysis and may be estimated more accurately by reducing the scale and using further higher resolution images.
ARTICLE | doi:10.20944/preprints202009.0749.v1
Subject: Environmental And Earth Sciences, Paleontology Keywords: Cave, hydrothermal, Landsat, Pawon, remote sensing
Online: 30 September 2020 (14:19:27 CEST)
Relationship between caveman prehistoric life in terms of heat induced food processing and its geological ecosystems have received many attentions. Previous studies have investigated the sources of heat included using Fourier transform infrared spectroscopy and biomarker approaches. Here this study proposes the use of remote sensing to identify the relationship of 9500 year old (9.5 ka) prehistoric mongoloid occupancy with hydrothermal manifestations at Pawon cave of West Java. The hydrothermal manifestations around Pawon cave were identified using Landsat 8 band combinations, land surface temperature, and sedimentary lithology. The results showed the hydrothermal manifestations surrounding Pawon cave were within a distance of 0.5-2 km. The results also showed bones representing 12 animal taxon groups with high abundance of rodents. To conclude this study sheds the light of proximity and preferences of mongoloid prehistoric occupancy towards hydrothermal landscape due to its advantage as heat sources for food processing purposes.
ARTICLE | doi:10.20944/preprints201808.0029.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat, analysis ready data, collection 1
Online: 1 August 2018 (20:03:52 CEST)
Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and
ARTICLE | doi:10.20944/preprints201703.0236.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: fractional ground cover; non-photosynthetic vegetation; landsat; standardised precipitation index; episodic rainfall; landsat; time series; growth-cycles
Online: 31 March 2017 (12:14:25 CEST)
Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (> 3 years) and short term (< 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.
ARTICLE | doi:10.20944/preprints202308.0624.v1
Subject: Medicine And Pharmacology, Gastroenterology And Hepatology Keywords: Crohn’s disease; metalloproteinases; MMP-3; MMP-8
Online: 8 August 2023 (07:12:46 CEST)
Crohn's disease is a non-specific inflammatory bowel disease, which is a chronic condition that affects the ileum and/or large intestine. At the same time, it can also involve any other part of the human body, that is, from the mouth to the anus. The symptoms are very bothersome and cause a significant reduction in quality of life and sometimes even crippling permanent damage to the gastrointestinal tract, which requires enteral or parenteral nutrition for life. The purpose of this study was to investigate tissue metalloproteinases as markers of recurrent Crohn's disease. The experimental groups included 31 patients and 10 patients with normal Crohn's disease, ranging in age from 23-70 years, with a mean age of 40.4. Collected tissues were frozen and then fragmented tissues were homogenized with Ripa Lysis buffer on ice. The supernatant was collared and four metalloproteinases - MMP 3, MMP 7, MMP 8 and MMP 9 - were an-alized by Enzyme-linked Immunosorbent Assay using SEA101HU, SEA102Hu, SEA103Hu and SEA553Hu kits (Cloud-Clone Corp., Kata, TX, USA). All chemical analyses were performed in triplicate. Metalloproteinase content was expressed as mean ± standard deviation. Metalloproteinase 3 and metalloproteinase 8) significantly influenced the possibility of Crohn recurrence
ARTICLE | doi:10.20944/preprints202111.0007.v1
Subject: Environmental And Earth Sciences, Soil Science Keywords: African agriculture; Irrigation; Landsat; Remote Sensing; Reservoir.
Online: 1 November 2021 (11:26:45 CET)
Agriculture in Morocco has been extensive until the middle of the 20th century due to the distribution of rainfall and the availability of water. In the middle of the last century hydraulic works were built that allowed the transition to intensive agriculture by the increase of irrigated areas, allowing that in the territories where there is water for irrigation and the climate allows it, the crops adapt to the demands of the market. The objective of the study is to assess by satellite images the land cover between 1985 and 2020, analyzing the changes in cultivation areas, as well as the changes in desert, sub-desert and forest areas of the Oum Er Rbia hydrological basin in Morocco. Landsat satellite images have been used since 1984 by the US government (Aerospace and Geological Agencies). A series of vegetation indices (NDVI, RVI, TNDVI and EVI) have been used; among which TNDVI (Transformed Normalized Vegetation Index) stands out for its better accuracy, which has allowed us to distinguish vegetation in cultivated and forest areas, as well as arid zones. In addition, the study has compared the use of two methodologies to calculate changes in the coverage of the Earth’s surface, has used local image processing from the Sentinel Application Platform tool and has also used the Google Earth Engine tool. The latter being the most optimal, although at the moment it has great limitations. In both methodologies and in the different indices it has been possible to observe during these 35 years as the cultivated area has increased (related to the availability of water by the construction of reservoirs and canals), how plant cover has improved in forest areas, and a range of variations in arid areas.
ARTICLE | doi:10.20944/preprints202011.0287.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Urban growth; cellular automata; Benslimane; GIS; Landsat
Online: 9 November 2020 (22:56:32 CET)
In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.
ARTICLE | doi:10.20944/preprints201801.0233.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: vegetation indices; LANDSAT; WorldView-2; RapidEye; AVIRIS
Online: 25 January 2018 (04:37:02 CET)
Oil spills from offshore drilling and coastal refineries often cause degradation of coastal wetlands that can take a long time to recover. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and die-off due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and die-off after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5m to 30m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.
ARTICLE | doi:10.20944/preprints201709.0038.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: windthrow; Xynthia storm; Landsat imagery; limited data
Online: 11 September 2017 (07:41:40 CEST)
Unlike the contiguous windthrows, the diffuse windthrows occurred as a result of wind gusts of lower speed (100-140 km/h) than in the first case (>140 km/h) are much more difficult to detect due to their much lower areas and due to their very large number, of several hundreds in the wooded mountain massifs. The objective of this research is to present a rapid procedure for the detection of the diffuse windthrows based on low cost, Landsat type images, knowing that certain sensors cannot be accessed without significant investments. Our application is based on the study of effects caused by the Xynthia storm in the Vosges Mountains in the North-East of France, on 28 February 2010. Thus, based on two sets of Landsat satellite images, we used the “dark object” approach and the Disturbance Index, as well as a classification of the images before and after the storm, resulting in a change map. Following the detection process, 257 scattered polygons were detected, totalling 229 ha. For validation purposes, high-resolution images and orthophotoplans taken before and after storm were used. The error matrix was calculated, achieving an overall accuracy of 86%, which confirms the quality of our analysis and supports this procedure for detecting diffuse windthrow based on low cost resources.
COMMUNICATION | doi:10.20944/preprints202305.0909.v1
Subject: Engineering, Bioengineering Keywords: 8-hydroxydeoxyguanosine (8-OHdG); Oxidative stress; Indium tin oxide (ITO) electrode; Integrated circuit system; Point-of-care testing (POCT)
Online: 12 May 2023 (08:59:39 CEST)
8-Hydroxydeoxyguanosine (8-OHdG) was the most widely used oxidative stress biomarker of the free radical-induced oxidative damage product of DNA, which may allow a premature assessment of various diseases. This paper designed a label-free, portable biosensor device to direct detect 8-OHdG by plasma coupled electrochemistry on transparent and conductive indium tin oxide (ITO) electrode. We reported a flexible printed ITO electrode made from particle-free silver and carbon inks. After inkjet printing, the working electrode was sequentially assembled by gold nanotriangles (AuNTAs) and platinum nanoparticles (PtNPs). This nanomaterial-modified portable biosensor showed excellent electrochemical performance for 8-OHdG detection from 10 μg/mL to 100 μg/mL by our self-developed constant voltage source integrated circuit system. This work demonstrated a portable biosensor for simultaneously integrating nanostructure, electroconductivity, and biocompatibility to construct advanced biosensors for oxidative damage biomarkers. The proposed nanomaterial modified ITO-based electrochemical portable device was a potential biosensor to approach 8-OHdG point-of-care testing (POCT) in various biological fluid samples, such as saliva and urine samples.
COMMUNICATION | doi:10.20944/preprints202305.0275.v1
Subject: Biology And Life Sciences, Biophysics Keywords: 8-hydroxyquinoline; PBT2; amyloid; copper; terdentate; ternary; antimicrobial
Online: 5 May 2023 (02:36:19 CEST)
The metal chelator PBT2 (5,7-dichloro-2-[(dimethylamino)methyl]-8-hydroxyquinoline) acts as a terdentate ligand capable of forming binary and ternary Cu2+ complexes. It was clinically trialed as an Alzheimer’s disease (AD) therapeutic but failed to progress beyond phase II. The β-amyloid (Aβ) peptide associated with AD was recently concluded to form a unique Cu(Aβ) complex that is inaccessible to PBT2. Herein, it is shown that the species ascribed to this binary Cu(Aβ) complex in fact corresponds to ternary Cu(PBT2)NIm[Aβ] complexes formed by anchoring of Cu(PBT2) on imine nitrogen (NIm) donors of His side chains. The primary site of ternary complex formation is His6, having a conditional stepwise formation constant at pH 7.4 (K [M−1] ) of log K = 6.4 ± 0.1, and a second site is supplied by His13 or His14 (log K = 4.4 ± 0.1). The stability of Cu(PBT2)NIm[H13/14] is comparable with that of the simplest ternary complexes involving free imidazole (log K = 4.22 ± 0.09) and histamine (log K = 4.00 ± 0.05). The 100-fold larger formation constant for Cu(PBT2)NIm[H6] indicates that outer-sphere ligand–peptide interactions strongly stabilize its structure. Despite the relatively high stability of Cu(PBT2)NImH6, PBT2 is a promiscuous Cu2+-binding ligand capable of forming a ternary Cu(PBT2)NIm complex with any ligand containing NIm donor. These ligands include histamine, L-His, and ubiquitous His side chains of peptides and proteins in the extracellular milieu, whose combined effect should outweigh that of a single Cu(PBT2)NIm[H6] complex regardless of its stability. We therefore conclude that PBT2 is capable of accessing Cu(Aβ) complexes with high stability but not specificity. The results have implications for future AD therapeutic strategies and understanding the role of PBT2 in the bulk transport of transition metal ions. Given the repurposing of PBT2 as a drug for breaking antibiotic resistance, ternary Cu(PBT2)NIm and analogous Zn(PBT2)NIm complexes may be relevant to its antimicrobial properties.
ARTICLE | doi:10.20944/preprints202211.0195.v1
Subject: Medicine And Pharmacology, Veterinary Medicine Keywords: EHDV; Tunisia; virus characterization; EHDV serotype 8; circulation
Online: 10 November 2022 (09:59:16 CET)
Epizootic haemorrhagic disease (EHD) is a Culicoides-borne viral disease caused by epizootic haemorrhagic disease virus (EHDV) and associated with clinical manifestations in cervids and bovids. In late September 2021, EHDV was reported in cattle farms in central/western Tunisia. It rapidly spread throughout the country with more than 200 confirmed outbreaks. A combination of classical and molecular techniques was applied to characterize the causative virus as a member of EHDV-8 serotype. This is the first evidence of EHDV- 8 circulation since 1982 when the prototype EHDV-8 strain was isolated in Australia. This work highlights the urgent need for vaccines for a range of EHDV serotypes.
ARTICLE | doi:10.20944/preprints201709.0095.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: thermal barrier coatings; 8 %YSZ; thermal conductivity; microstructure
Online: 20 September 2017 (08:28:21 CEST)
In this paper, the effect of microstructure on the thermal conductivity of plasma-sprayed Y2O3 stabilized ZrO2 (YSZ) thermal barrier coatings (TBCs) is investigated. Nine freestanding samples deposited on aluminum-base superalloy are studied. Cross-section morphology such as pores, cracks, m-phase content, grain boundary density of the coated samples are examined by scanning electron microscopy (SEM) and electron back-scattered diffraction (EBSD). Multiple linear regressions are used to develop quantitative models which describe the relationship between the particle parameters, m-phase content and the microstructure such as porosity, crack-porosity, the length density of small-angle-crack and the length density of big-angle-crack. Moreover, the relationship between microstructure and thermal conductivity is investigated. Results reveal that the thermal conductivity of the coating is mainly determined by the microstructure and grain boundary density at room temperature (25 ℃) and by the length density of big-angle-crack, monoclinic phase content and grain boundary density at high temperature (1200 ℃).
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: DNA lesions; 7,8-dihydro-8-oxoguanine (8-oxoG); mutagenic activity; method of detection; cell nuclear extracts; mice organs and embryos
Online: 14 November 2019 (09:00:19 CET)
We propose an improved earlier described “mirror” method  for detecting in cell nuclear extracts mutations that arise in DNA during its replication due to misincorporation of deoxyadenosine-5’-monophosphate (dAMP) opposite 7,8-dihydro-8-oxoguanine (8-oxoG). The method is based on the synthesis of a complementary chain (“mirror”) by nuclear extracts of different mice organs on a template containing 8-oxoG inside and dideoxycytidine residue (ddC) at the 3’-end. The “mirror”was amplified by PCR using primers part of which was non-complementary to the template. It allowed obtaining the “framed mirror” products. The misincorporation of dAMP in “framed mirror” products forms an EcoRI restriction site. The restriction analysis of double-stranded “framed mirror” products allows a quantification of the mutation frequency in nuclear extracts. The data obtained showed that the mutagenic potential of 8-oxoG markedly varied in different organs of adult mice and embryos.
ARTICLE | doi:10.20944/preprints201608.0114.v1
Subject: Biology And Life Sciences, Biophysics Keywords: 8-OHdG (8-hydroxy- 2-deoxyguanosine), vitamin A, vitamin C, vitamin E, ROS (reactive oxygen species), TAS (total antioxidant status)
Online: 10 August 2016 (16:49:20 CEST)
The present study was aimed to evaluate the levels of oxidative stress markers in breast diseases by measuring the 8-hydoxy-2-deoxyguanosine (8-OHdG), vitamin A, vitamin C, vitamin E and total antioxidant status (TAS) alterations in relation to cell proliferation activity and disease progression. Significant increases in the level of oxidative damage marker 8-OHdG and cell proliferation activity were observed in breast carcinoma patients in comparison to benign and normal controls, which were accompanied by significant decrease in non enzymatic antioxidants and TAS concentrations. 8-OHdG and cell proliferation level were negatively correlated with non enzymatic antioxidants viz., Vitamin A, Vitamin C, vitamin E level and total antioxidant activity. Altered levels of biomarkers of oxidative stress and cell proliferation activity amongst the malignant, benign and controls suggest a correlation of increased oxidative stress and cell proliferation activity in the progression of disease in breast carcinoma patients. Among the oxidative stress markers and cell proliferation index, decreased level of vitamin A, vitamin C, vitamin E, TAS and increased level of 8-OHdG, cell proliferation index emerged as best predicted biomarkers for subjects with malignancy and benign breast disease.
ARTICLE | doi:10.20944/preprints202211.0373.v1
Subject: Physical Sciences, Applied Physics Keywords: Indus; Gilgit Watershed; Hydrological characteristics; glacier changes; Landsat
Online: 21 November 2022 (04:54:14 CET)
Glaciers in northern Pakistan are a prime source of freshwater, providing headwater in the Indus river system and serving as a lifeline to millions of people in the region. These glaciers undergo continuous changes by melting due to global warming or accumulation due to snowfall/precipitation at higher altitudes. In this study, we used remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an extent of less than 5 square kilometers were selected, namely Phakor glacier, Karamber glacier, East Gammu glacier, Bhort glacier, and Bad-e-Swat glacier. The fluctuations in these glaciers were monitored using a digital elevation model (DEM) and a cloud-free continuous series of Landsat satellite pictures from the minimal snow cover season. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970 – 2000. We used it to study the variations of minimum and maximum temperature, solar radiation, and precipitation through the preparation of sub-sets from the original global grids. Because of its exact delineation in the Gilgit sub-basin, the characterized watersheds were visually compared to optical Landsat 8 OLI data for mountainous ridge matching, revealing that SRTM 30m (radar-based) demonstrated greater accuracy than other DEMs. The temporal assessment of Bhort, Bad-e-Sawat, East Gammu, Karamber, and other rivers was also carried out. It is observed that the glaciers in the Gilgit watershed are rather stable. The little variability of glaciers is due to their geographic condition, altitude, topography, and orientation. Validation of the mapped glacier classes has been performed to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated to be 84 %. The study makes a critical input to a greater understanding of watershed controlling and hydrological processes in the upper Indus catchment's Gilgit watershed.
ARTICLE | doi:10.20944/preprints201911.0173.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: coral reef; Landsat; population; remote sensing; small islands
Online: 15 November 2019 (04:14:59 CET)
In general, remote sensing has proven to be a powerful tool in the overall understanding of natural and anthropogenic phenomena. Satellites have become useful tools for tasks such as characterization, monitoring, and the continuous prospecting of natural resources. This research aims to analyze spatial dynamic and destructive on coral reefs area and correlation between live coral reduction and population on small islands. Landsat MSS, TM, ETM, and OLI-TIRS are used to spatial analyze of coral reef dynamics from 1972 to 2016. The image processing includes gap-filling, atmospheric correction, geometric correction, image composite (true color), water column correction, unsupervised classification, reclassification, accuracy assessment. The statistical analysis identifies the relationship between dynamic population data with a reduction of live coral, namely Principal Component Analysis (PCA) and Multiple Regression Analysis. The effect of the population shows a positive correlation with the reduction in the area of live coral, although it is significant. The fact is the practice of coral destruction on an island; it is usually not only caused or carried out by residents who live on the island but also carried out by other residents of different islands.
ARTICLE | doi:10.20944/preprints201810.0187.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: remote sensing; multi-temporal; Landsat; age; canopy; FCD
Online: 9 October 2018 (11:33:18 CEST)
In the oil palm industry, stands age is an important parameter to monitor the sustainability of cultivation, to develop the growth yield model, to identify the disease or stressed area, and to estimate the carbon storage capacity. This research is focused to estimate and distinguish oil palm stands age based on crown/ canopy density obtained using Forest Canopy Density (FCD) model derived from four indices as follows; Advanced Vegetation Index, Bare Soil Index, Shadow Index, and Thermal Index. FCD model employs multi temporal image analysis resulting four classes of oil palm stands age categorized as seed with FCD value of 29–56% (0 years), young with FCD value of 56–63% (1–9 years), teen with FCD value of 63–80% (10–15 years), and mature with FCD value of >80% (>15 years). Minimum canopy density value is 29% even in the zero years old indicates incomplete land clearance or the type of seed planted in the land.
ARTICLE | doi:10.20944/preprints201805.0470.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: remote sensing; python; data management; landsat; open-source
Online: 31 May 2018 (11:12:27 CEST)
Many remote sensing analytical data products are most useful when they are in an appropriate regional or national projection, rather than globally based projections like Universal Transverse Mercator (UTM) or geographic coordinates, i.e., latitude and longitude. Furthermore, leaving data in the global systems can create problems, either due to misprojection of imagery because of UTM zone boundaries, or because said projections are not optimised for local use. We developed the open-source Irish Earth Observation (IEO) Python module to maintain a local remote sensing data library for Ireland. This pure Python module, in conjunction with the IEOtools Python scripts, utilises the Geospatial Data Abstraction Library (GDAL) for its geoprocessing functionality. At present, the module supports only Landsat TM/ETM+/OLI/TIRS data that have been corrected to surface reflectance using the USGS/ESPA LEDAPS/ LaSRC Collection 1 architecture. This module and the IEOtools catalogue available Landsat data from the USGS/EROS archive, and includes functions for the importation of imagery into a defined local projection and calculation of cloud-free vegetation indices. While this module is distributed with default values and data for Ireland, it can be adapted for other regions with simple modifications to the configuration files and geospatial data sets.
ARTICLE | doi:10.20944/preprints201805.0360.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat; MODIS; change detection; forest disturbance; forest health
Online: 25 May 2018 (10:48:32 CEST)
The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 77.64% to 93.51% (kappa 0.09–0.59) in the Southern New England study area and 59.57% to 79.57% (kappa 0.09–0.45) in the Rio Grande National Forest study area. In general, ORS products met or exceeded the overall accuracy and kappa of IDS and RTFD products. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.
REVIEW | doi:10.20944/preprints202010.0134.v1
Subject: Chemistry And Materials Science, Analytical Chemistry Keywords: zinc sensor; 8-amidoquinoline; fluorescent probe; chemosensor; systematic review
Online: 6 October 2020 (14:55:13 CEST)
Abundant of preparatory works have recognized that fluorescent sensors based on 8-aminoquinoline are popular tools to detect Zn2+ ions in environmental and biological applications. Along with these studies, researchers started to introduce a variety of carboxamido group into an 8-aminoquinoline molecule in forming 8-amidoquinoline derivatives. Therefore, this systematic review aims to introduce a general overview of the fluorophore 8-aminoquinoline as Zn2+ receptors and to provide comparisons of collected studies that related to 8-amidoquinoline derivatives as fluorophore probe of the sensor. According to PRISMA systematic searches strategy, 13 articles were analyzed for trends, research designs, results and discussion, subject samples, and remarks or conclusions. We found cross-sectional studies with four aspects in zinc sensing that have been targeted; binding studies via titration, detection's limit, interferences studies, and validation of the study. Hence, this paper also included assessments of those criteria and the trends of development of 8-amidoquinoline derivatives based-zinc fluorescent chemosensor. It also showed that most of the researches conducted in China. In conclusion, this study identified various research designs of fluorescent chemosensors based on 8-amidoquinoline prolong with the effectiveness and potential as a recognition probe to assist the detection of zinc. Hence, elucidation of those derivatives essential to be explored because more studies are needed to improve the sensing criteria of the zinc sensor
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: h. pylori; restriction modification system; virulence; il-8; apoptosis
Online: 1 November 2019 (12:52:39 CET)
Helicobacter pylori is a common human pathogen that causes gastroduodenal diseases. H. pylori genome consists of numerous restriction-modification (R-M) genes. It is established that N6-adenine methylation plays a crucial role in bacterial gene regulation and virulence, but not much is known about the role of C5-cytosine methylation. In this study, we examined the influence of an orphan cytosine methyltransferase, hpyAVIBM on gastric infection in mice and cultured cells. Histopathological staining showed that the deletion of hpyAVIBM in H. pylori strain SS1 had increased damaging hemorrhagic effects on the mice stomach. The gelatin-zymography result demonstrated that the mice infected with mutant SS1ΔhpyAVIBM had significantly up-regulated pro-MMP-9 than those infected with SS1. Additionally, ELISA results of pro-inflammatory cytokines proved that mutant strain caused significantly more inflammatory effect on mice stomach than its wild-type counterpart. The immunohistochemistry data showed that mutant strain caused attenuated epithelial cell damage. Co-culture studies of H. pylori with AGS (Human Gastric Adenocarcinoma cell line) cells revealed that SS1ΔhpyAVIBM instigated significantly more apoptotic death in the AGS cells compared to the wild-type strain. Our results indicated that DNA methylation by hpyAVIBM plays a crucial role in modulating virulence factors in bacterial cells and their interaction with the host cells.
ARTICLE | doi:10.20944/preprints201910.0359.v1
Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: adiposity; dysbiosis; hops; menopause; microbiota; 8-prenylnaringenin; obesity; ovariectomy
Online: 31 October 2019 (02:22:34 CET)
Estrogen decline during menopause is associated with altered metabolism, weight gain and increased risk for cardiometabolic diseases. The gut microbiota also plays a role in the development of cardiometabolic dysfunction and is also subject to changes associated with age-related hormone changes. Phytoestrogens are plant-based estrogen mimics that have gained popularity as dietary supplements for treatment or prevention of menopause-related symptoms. These compounds have the potential to both modulate and to be metabolized by the gut microbiota. Hops (Humulus lupulus L.) contain potent phytoestrogen precursors, which rely on microbial biotransformation in the gut to estrogenic forms. We supplemented ovariectomized (OVX) or sham-operated (SHAM) C57BL/6 mice, with oral estradiol (E2), a flavonoid-rich extract from hops, or a placebo carrier oil to observe effects on adiposity, inflammation, and gut bacteria composition. Hops extract and E2 protected against increased visceral adiposity and liver triglyceride accumulation in OVX animals. Surprisingly, we found no evidence of OVX having a significant impact on the overall gut bacterial community structure. We did find differences in abundance of Akkermansia muciniphila, which was lower with HE treatment relative to the OVX E2 treatment and to placebo in the SHAM group.
ARTICLE | doi:10.20944/preprints201811.0406.v1
Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: SU-8, microchannel, prototyping, microfluidic gradient generator, axon elongation
Online: 16 November 2018 (11:19:18 CET)
We have developed a cast microfluidic chip for concentration gradient generation that contains a thin (~5 μm^2 crosssectional area) microchannel. Durable 2 μm-high microchannel mold features with a smooth bell-shaped sidewall were fabricated by exposing SU-8 photoresist to diffused 185 nm UV light emitted by a low-cost ozone lamp from the backside of the substrate to ensure sufficient crosslinking of small regions of the SU-8 photoresist. An H-shaped microfluidic configuration was used, in which the thin channel was able to maintain constant diffusion fronts beyond purely static diffusion confirmed with experiment. We also demonstrated the long-term effects of a gradient of nerve growth factor on axon elongation by primary neuronal cells cultured in the microfluidic channel.
ARTICLE | doi:10.20944/preprints201706.0071.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: 8-prenylnaringenin; naringenin; cellular accumulation; glioblastoma; cytotoxicity; confocal microscopy
Online: 16 June 2017 (03:25:34 CEST)
Gliomas are one of the most aggressive and treatment-resistant types of human cancer. One of the most promising field in gliomas cancer therapy is identification and evaluation of anticancer properties of compounds found in plants i.a. naringenin (N) and 8-prenylnaringenin (8PN). The prenyl group seem to be crucial to the anticancer activity of flavones, which may lead to enhanced cell membrane targeting and thus increased intracellular activity. Unfortunately, 8PN content in hop cones is from 10 to 100 times lower compared to other flavonoids i.e. xanthohumol. In this study we used a simple method for the synthesis of 8PN from isoxanthohumol, via O-demethylation with high, 97% of the isolated yield. Cellular accumulation and cytotoxicity of naringenin and 8-prenylnaringenin in normal (BJ) and cancer cells (U-118 MG) were also examined. Obtained data indicated that 8-prenylnaringenin exhibited higher toxicity against used cell lines than naringenin and both flavones inhibited stronger glioblastoma U-118 MG cells than normal fibroblasts. The anticancer properties of 8PN correlated with its significantly greater (37%), accumulation in glioblastoma cells than in normal fibroblasts. Additionally, naringenin indicated higher selectivity for glioblastoma as it was over 6 times more toxic for cancer than normal cells. Our results provide evidence that examined prenylated and non-prenylated flavanones have different biological activity against normal and cancer cell lines and this phenomenon may be useful in clinical practice to construct new, anticancer drugs for glioblastoma.
ARTICLE | doi:10.20944/preprints202205.0291.v2
Subject: Medicine And Pharmacology, Psychiatry And Mental Health Keywords: Yoga of Immortals (YOI); depression; healthcare workers; insomnia; anxiety; PHQ-8 (Pa-tient Health Questionnaire-8); ISI (Insomnia Severity Index); digital health
Online: 24 May 2022 (04:49:45 CEST)
The COVID-19 pandemic has caused significant medical and psychological challenges worldwide, and not only exceeded the capacity of hospitals and intensive care units but also an individuals’ ability to cope with life. Health-care workers have continued to provide care for patients despite exhaustion, fear of transmission to themselves and their family, illness or death of friends and colleagues, and losing many patients. They have also faced additional stress and anxiety due to long shifts combined with unprecedented population restrictions, including personal isolation. In this study, we study the effect of an app-based Yoga of Immortals (YOI) intervention on mental health of healthcare workers. In this study, the health care workers were digitally recruited, and their psychological parameters were measured using validated questionaries. The participants were randomly grouped into control and test groups. The validated psychological measures were the Patient Health Questionnaire-8 (PHQ-8), Insomnia Severity Index (ISI) and generalized anxiety disorder (GAD-7) scales. The digital YOI intervention significantly reduced the anxiety, depression symptoms, and insomnia in healthcare workers of all age groups. In contrast, there was no improvement in the control group. This study details the effectiveness of an app-based YOI intervention in healthcare workers.
ARTICLE | doi:10.20944/preprints202311.1077.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: eutrophication; landsat; Chl-a; turbidity; spectral signatures; OLI; Chile
Online: 16 November 2023 (15:19:03 CET)
This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, in south-central Chile by analyzing time series of Landsat-8 satellite images, specifically using the multispectral OLI sensor. The bio-optical parameters, i.e., chlorophyll-a (mg·m-3) and turbidity (NTU) were also measured in-situ synchronized with the satellite passes to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in-situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process. Subsequently, we harnessed the reflectance data derived from the ACOLITE model to establish correlations between various spectral indices and the in-situ data. The empirical retrieval models (based on band combinations) that showed superior performance, as indicated by higher R2 values, were subjected to rigorous statistical validation and optimization by applying a bootstrapping approach. Our analysis covered a spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014-2022) characterized by a decline in chlorophyll-a concentration in the lake possibly attributable to governmental measures in the region for the protection and conservation of the lake. The results of this initial study serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.
ARTICLE | doi:10.20944/preprints202112.0467.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: landsat; pasture degradation; brazilian pasturelands dynamics; low carbon agriculture
Online: 29 December 2021 (12:54:56 CET)
The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.
ARTICLE | doi:10.20944/preprints201910.0275.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Landsat; Sentinel 2; harmonization; crop monitoring; Google Earth Engine
Online: 24 October 2019 (06:02:04 CEST)
Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The synthesizing of ongoing satellite missions by ESA (Sentinel 2) and NASA (Landsat7/8) provides this unprecedented opportunity at a global scale; nonetheless, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a complete stream processing in the Google Earth Engine cloud platform to generate harmonized surface reflectance images of Landsat7,8 and Sentinel 2 missions. The harmonized images were generated for two agriculture schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during the period 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including image co-registration, brdf correction, topographic correction, and band adjustment. This study found that the miss-registration between Landsat 8 and Sentinel 2 images, varied from 10 meters in Ninh Thuan, Vietnam to 32 meters in Bekaa, Lebanon, and if not treated, posed a great impact on the quality of the harmonized dataset. Analysis of a pair overlapped L8-S2 images over the Bekaa region showed that after the harmonization, all band-to-band spatial correlations were greatly improved from (0.57, 0.64, 0.67, 0.75, 0.76, 0.75, 0.79) to (0.87, 0.91, 0.92, 0.94, 0.97, 0.97, 0.96) in bands (blue, green, red, nir,swir1,swir2, ndvi) respectively. We demonstrated that dense observation of the harmonized dataset can be very helpful for characterizing cropland in highly dynamic areas. We detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns (likely cycles of paddy rice) in Ninh Thuan province only during the year 2018. We fitted the temporal signatures of the NDVI time series using harmonic (Fourier) analysis. Derived phase (angle from the starting point to the cycle's peak) and amplitude (the cycle's height) were combined with max-NDVI to generate an R-G-B image. This image highlighted croplands as colored pixels (high phase and amplitude) and other types of land as grey/dark pixels (low phase/amplitude). Generated harmonized datasets that contain surface reflectance images (bands blue, green, red, nir, swir1, swir2, and ndvi at 30 meters) over the two studied sites are provided for public usage and testing.
ARTICLE | doi:10.20944/preprints201811.0113.v2
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat; artisanal-scale gold mining; infrastructure; protected areas; commodity
Online: 30 November 2018 (10:02:42 CET)
While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying ASGM is a broad network of factors that influence its growth, distribution, and practices such as poverty, flows of legal and illegal capital, conflicting governance, and global economic trends. Despite its central role in land use and land cover change in the Western Amazon and the severity of its social and environmental impacts, it is relatively poorly studied. While ASGM in Southeastern Peru has been quantified previously, doing so is difficult due to the heterogeneous nature of the resulting landscape. Using a novel approach to classify mining that relies on a fusion of CLASlite and the Global Forest Change dataset, two Landsat-based deforestation detection tools, we sought to quantify ASGM-caused deforestation in the period 1984–2017 in the southern Peruvian Amazon and examine trends in the geography, methods, and impacts of ASGM across that time. We identify nearly 100,000 ha of deforestation due to ASGM in the 34-year study period, an increase of 21% compared to previous estimates. Further, we find that 10% of that deforestation occurred in 2017, the highest annual amount of deforestation in the study period, with 53% occurring since 2011. Finally, we demonstrate that not all mining is created equal by examining key patterns and changes in ASGM activity and techniques through time and space. We discuss their connections with, and impacts on, socio-economic factors, such as land tenure, infrastructure, international markets, governance efforts, and social and environmental impacts.
ARTICLE | doi:10.20944/preprints201608.0098.v2
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ET; CWR; Landsat ETM+; Remote Sensing; SEBAL; SSEB; SSEBop
Online: 16 March 2017 (09:21:29 CET)
Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.
ARTICLE | doi:10.20944/preprints202311.0452.v1
Subject: Medicine And Pharmacology, Psychiatry And Mental Health Keywords: sensation seeking; Brief Sensation Seeking Scale; BSSS-8; psychometric properties
Online: 8 November 2023 (01:28:38 CET)
Sensation seeking (SS) is a psychobiological personality trait characterized by an individual’s propensity to engage in various forms of risk-taking behavior. The Brief Sensation Seeking Scale (BSSS-8) is a widely used instrument for assessing SS that has been translated into several languages. However, only outdated and non-validated questionnaires have been used to measure SS in the Slovenian population. The aim of this study was to translate and psychometrically validate the Slovenian version of the BSSS-8. A total of 363 participants aged between 14 and 65 years completed the translated BSSS-8 and the questionnaire on drug abuse. The scale demonstrated good reliability (Cronbach’s α=0.81) and a unidimensional factorial structure, as revealed by confirmatory factor analysis (CFA). The multigroup CFA showed gender-specific measurement invariance. In the nomological network, SS was positively associated with drug-related variables. The Slovenian version of the BSSS-8 scale is a short and simple instrument to assess SS for research and epidemiological purposes.
ARTICLE | doi:10.20944/preprints202308.0042.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: cancer vaccine; adjuvant; nanovaccines; TLR 7/8 agonist; melanoma; immunomodulator
Online: 1 August 2023 (10:13:40 CEST)
Despite numerous studies on cancer treatment, cancer remains a challenging disease to cure, even after decades of research. In recent years, cancer vaccine has emerged as a promising approach for cancer treatment, offering few unexpected side effects compared to existing therapies. How-ever, cancer vaccine faces obstacles to commercialization due to its low efficacy. Particularly, the Toll-like receptor (TLR) adjuvant system, specifically the TLR 7/8 agonist, has shown potential for activating Th1 immunity, which stimulates both innate and adaptive immune responses through T cells. In this study, we developed ProLNG-S, a cholesterol-conjugated form of resiquimod (R848), to enhance immune efficacy by stimulating the immune system and reducing toxicity. ProLNG-S was formulated as ProLNG-001, a positively charged liposome, and co-administered with oval-bumin (OVA) protein in the B16-OVA model. ProLNG-001 effectively targeted secondary lym-phoid organs, resulting in a robust systemic anti-tumor immune response and tumor-specific T cell activation. Consequently, ProLNG-001 demonstrated potential for preventing tumor progression and improving survival compared to AS01 by enhancing anti-tumor immunity.
ARTICLE | doi:10.20944/preprints202305.0995.v1
Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: CD44 variant 8; monoclonal antibody; gastric cancer; flow cytometry; immunohistochemistry
Online: 15 May 2023 (07:41:36 CEST)
Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. GC with peritoneal metastasis exhibits a poor prognosis due to the lack of diagnostic biomarkers and effective therapy. A comprehensive analysis of malignant ascites identified the genomic alterations and significant amplifications of cancer driver genes, including CD44. CD44 and its splicing variants are overexpressed in tumors, and play crucial roles in the acquisition of invasiveness, stemness, and resistance to treatments. Therefore, the development of CD44-targeting monoclonal antibodies (mAbs) is important for GC diagnosis and therapy. In this study, we immunized mice with CD44v3–10-overexpressed PANC-1 cells and established several dozens of clones that produce anti-CD44v3–10 mAbs. One of the clones (C44Mab-94; IgG1, kappa) recognized the variant-8-encoded region and peptide, indicating that C44Mab-94 is a specific mAb for CD44v8. Furthermore, C44Mab-94 could recognize CHO/CD44v3–10 cells, oral squamous cell carcinoma cell line (HSC-3), or GC cell lines (MKN45 and NUGC-4) in flow cytometric analyses. C44Mab-94 could detect the exogenous CD44v3–10 and endogenous CD44v8 in western blotting and stained the formalin-fixed paraffin-embedded gastric cancer cells in immunohistochemistry. These results indicate that C44Mab-94 is useful for detecting CD44v8 in various applications and is expected to be useful for the application of GC diagnosis and therapy.
ARTICLE | doi:10.20944/preprints202302.0437.v1
Subject: Medicine And Pharmacology, Pathology And Pathobiology Keywords: CD44; CD44 variant 7/8; monoclonal antibody; flow cytometry; immunohistochemistry
Online: 27 February 2023 (04:13:02 CET)
Cluster of differentiation 44 (CD44) has been investigated as a cancer stem cell (CSC) marker, and plays critical roles in tumor malignant progression. The splicing variants are overexpressed in many carcinomas, especially squamous cell carcinomas, and play critical roles in the promotion of tumor metastasis, the acquisition of CSC properties, and resistance to treatments. Therefore, each CD44 variant (CD44v) function and distribution in carcinomas should be clarified for the establishment of novel tumor diagnosis and therapy. In this study, we immunized mice with a CD44 variant (CD44v3−10) ectodomain and established various anti-CD44 monoclonal antibodies (mAbs). One of the established clones (C44Mab-34; IgG1, kappa) recognized a peptide which covers both variant 7 and 8-encoded region, indicating that C44Mab-34 is a specific mAb for CD44v7/8. Moreover, C44Mab-34 reacted with CD44v3–10-overexpressed Chinese hamster ovary-K1 (CHO) cells or oral squamous cell carcinoma (OSCC) cell line (HSC-3) by flow cytometry. The apparent KD of C44Mab-34 for CHO/CD44v3–10 and HSC-3 was 1.4 × 10−9 M and 3.2 × 10−9 M, respectively. C44Mab-34 could detect CD44v3–10 in western blotting, and stained the formalin-fixed paraffin-embedded OSCC in immunohistochemistry. These results indicate that C44Mab-34 is useful for detecting CD44v7/8 in various applications, and expected for the application of OSCC diagnosis and therapy.
ARTICLE | doi:10.20944/preprints202009.0571.v1
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: medical students; travellers; MDR bacteria; CPE; mcr-1; mcr-8
Online: 24 September 2020 (08:05:32 CEST)
Background: In France, no previous studies had addressed the acquisition of multidrug resistant (MDR) bacteria and colistin resistance genes by medical students when undertaking internships abroad. Methods: Nasopharyngeal, rectal, and vaginal swabs samples were collected from 382 French medical students before and after travel to investigate the acquisition of MDR bacteria. The bacterial diversity in the samples was assessed by culture on selective media. We also genetically characterised the isolates of MDR bacteria including Extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E), methicillin-resistant Staphylococcus aureus (MRSA), and Carbapenemase-producing Enterobacteriacae (CPE) using the real-time polymerase chain reaction method. The samples were collected from 293 students and were investigated for mcr colistin-resistance genes using RT-PCR directly on the samples, followed by conventional PCR and sequencing. Results: A proportion of 29.3% of the participants had acquired ESBL-E and 2.6% had acquired CPE. The most common species and ESBL-E encoding gene were Escherichia coli (98.4%) and blaCTX-M-A (95.3%), respectively. A proportion of 6.8% of the participants had acquired mcr-1 genes, followed by mcr-3 (0.3%) and mcr-8 (0.3%). We found that taking part in humanitarian missions to orphanages, being in contact with children during travel, the primary destination of travel being Vietnam and north India, using antibiotics during travel, and studying in 2017 were associated with the acquisition of ESBL-E. When the primary destination of travel was Vietnam and the year of study was 2018, this was associated with acquisition of colistin resistance genes. Conclusion: Medical students are at a potential risk of acquiring ESBL-E, CPE and colistin resistance genes. A number of risk factors have been identified, which may be used to develop targeted preventive measures.
ARTICLE | doi:10.20944/preprints201803.0209.v2
Subject: Chemistry And Materials Science, Analytical Chemistry Keywords: Mitracarpus scaber Zucc.; pentalogin; anti-inﬂammatory; MS/MS; Il-8
Online: 10 May 2018 (11:51:09 CEST)
Re-investigation of the chemical composition of the annual plant Mitracarpus scaber Zucc. led to the identification of clarinoside, a new pentalogin derivative containing a rare quinovose moiety, and the known compound harounoside. While the planar structure was fully determined using tandem MS and quantum mechanics calculations (QM), the tridimensional structure was unravelled after isolation and NMR analysis. The absolute configuration was assigned by comparison of experimental and theoretical SRCD spectra. Both compounds were tested for anti-inflammatory activity and compound 1 showed the ability to inhibit the production of interleukin-8 (Il-8) with an IC50 of 9.17 µM.
ARTICLE | doi:10.20944/preprints202310.2060.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Aboveground carbon stock; tropical forest; Landsat; Malaysia ecosystem; spectral indicators
Online: 31 October 2023 (09:56:37 CET)
The accurate estimation of biomass carbon in forests is of paramount importance for effective forest management and mitigating climate change. This study presents a novel approach to produce a high-resolution map of biomass carbon over forests in Malaysia using the Aboveground Carbon Density Indicator (ACDI) and a comprehensive collection of 12 years of inventory data, i.e., from 2012 to 2023. The ACDI was derived based on several vegetation indices (VIs) that were produced from the original Landsat images to indicate the level of aboveground biomass carbon (AGC) stock in the forested areas. The VIs includes Normalised Difference Vegetation Index (NDVI), Normalised Burn Ratio (NBR), Shadow Index (SI), Soil-Adjusted Vegetation Index (SAVI), Iron Oxide Index (IO), Modified Normalised Difference Water Index (NDWI), and Enhanced Vegetation Index (EVI). The ACDI was then integrated with ground-based measurements, and serves as a robust indicator for estimating AGC. This calculation was conducted on Google Earth Engine (GEE) platform to match the date of field observation with the satellite imagery datasets. The production of seamless mosaic of the latest date of Landsat imagery and the forest type classification were also performed on GEE. The forested areas were classified into three major types, which are dry inland forest, mangrove forest, and peat swamp forest. Results indicated significant spatial variations in AGC across Malaysia's forests. The derived AGC prediction models based on the ACDI varied among the forest types. Based on the estimates, a 30-metre resolution, wall-to-wall map of AGC across the entire forested region of Malaysia has been created. The ACDI was calibrated and validated using a separate validation plots dataset to ensure the accuracy of the AGC estimates. The total AGC in all types of forests in Malaysia was estimated at 3.0 billion Mg C with an attainable accuracy of about 80%. These estimates were also divided into categories and reported to the AGC at the state level. This high-resolution map provides essential information for various stakeholders, with critical implications for carbon sequestration efforts, conservation priorities, and sustainable forest management. The presented methodology not only showcases the value of combining advanced remote sensing techniques with long-term inventory data but also underscores the potential for similar approaches in other tropical forest regions globally. Ultimately, this study contributes to the understanding of carbon dynamics in Malaysian forests and promotes effective strategies for mitigating climate change through better-informed forest conservation and management practices.
ARTICLE | doi:10.20944/preprints201809.0501.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: evapotranspiration; remote sensing; TSEB; METRIC; Landsat; Arizona; wheat; cotton; alfalfa
Online: 26 September 2018 (07:37:15 CEST)
A remote sensing-based evapotranspiration (ET) study was conducted over the Central Arizona Irrigation and Drainage District (CAIDD), an Arizona agricultural region. ET was assessed means for 137 wheat plots, 183 cotton plots, and 225 alfalfa plots. The remote sensing ET models were the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), the Two Source Energy Balance (TSEB), and Vegetation Index ET for the US Southwest (VISW). Remote sensing data were principally Landsat 5, supplemented by Landsat 7, MODIS Terra, MODIS Aqua, and ASTER. The models produced similar daily ET for wheat, with 6–8 mm/d mid-season. For cotton and alfalfa daily ET showed greater differences, where TSEB produced largest daily ET, METRIC the least, and VISW in the midrange. Modeled cotton ET at mid-season ranged from 9.5 mm/d (TSEB), to 8 mm/d (VISW), and 6 mm/d (METRIC). For alfalfa ET, values at peak cover ranged from 8 mm/d (TSEB), 6 mm/d (VISW), and 5 mm/d (METRIC). Model bias ranged −10% to +18%. Relative to potential ET, FAO-56 ET, and USDA-SW gravimetric-ET, model variability ranged from negligible to 35% of annual crop water use. Model averaging was found a useful way to consider and reconcile all ET estimates.
ARTICLE | doi:10.20944/preprints201807.0040.v1
Subject: Engineering, Civil Engineering Keywords: Google Earth Engine; EEFlux; METRIC; evapotranspiration; Landsat; water resources management
Online: 3 July 2018 (11:51:31 CEST)
Reliable evapotranspiration (ET) estimation is a key factor for water resources planning, attaining sustainable water resources use, irrigation water management, and water regulation. During the past few decades, researchers have developed a variety of remote sensing techniques to estimate ET. The Earth Engine Evapotranspiration Flux (EEFlux) application uses Landsat imagery archives on the Google Earth Engine platform to calculate the daily evapotranspiration at the local field scale (30 m). Automatically calibrated for each Landsat image, the EEFlux application design is based on the widely vetted Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model and produces ET estimation maps for any Landsat 5, 7 or 8 scene in a matter of seconds. In this research we evaluate the consistency and accuracy of EEFlux products that are produced when standard US and global assets are used. Processed METRIC products for 58 scenes distributed around the western and central United States were used as the baseline for comparison. The goal of this paper is to compare the results from EEFlux with the standard METRIC applications to illustrate the utility of the EEFlux products as they currently stand. Given that EEFlux is derived from METRIC, differences are expected to occur due to differing calibration methods (automatic versus manual) and differing input datasets. The products compared include the fraction of reference ET (ETrF), actual ET (ETa), and surface energy balance components net radiation (Rn), ground heat flux (G), and sensible heat flux (H), as well as Ts, albedo and NDVI. The product comparisons show that the intermediate products of Ts, Albedo, and NDVI, and also Rn have similar values and behavior for both EEFlux and METRIC. Larger differences were found for H and G. Despite the more significant differences in H and G, results show that EEFlux is able to calculate ETrF and ETa values comparable to the values from trained expert METRIC users for agricultural areas. For non-agricultural areas such as semi-arid rangeland and forests, the automated EEFlux calibration algorithm needs to be improved in order to be able to reproduce ETrF and ETa that is similar to the manually calibrated METRIC products.
ARTICLE | doi:10.20944/preprints202105.0393.v1
Subject: Chemistry And Materials Science, Food Chemistry Keywords: cannabidiol (CBD); ∆9-tetrahydrocannabinol (∆9-THC); cannabinol (CBN); ∆8-tetrahydrocannabinol (∆8-THC); cannabinoids; CBD oil; nuclear magnetic resonance spectroscopy (NMR); PULCON methodology; 1H NMR; qNMR
Online: 17 May 2021 (16:56:15 CEST)
Toxicologically relevant levels of the psychoactive ∆9-tetrahydocannabinol (∆9-THC) as well as high levels of non-psychoactive cannabinoids potentially occur in CBD (cannabidiol) oils. For consumer protection in the fast-growing CBD oil market, facile and rapid quantitative methods to determine the cannabinoid content are crucial. However, the current standard method, i.e., liquid chromatography combined with tandem mass spectrometry (HPLC-MS/MS), requires a time-consuming multistep sample preparation. In this study, a quantitative nuclear magnetic resonance spectroscopy (qNMR) method for screening cannabinoids in CBD oils was developed. Contrary to the HPLC-MS/MS method, this qNMR features a facile sample preparation, i.e., only diluting the CBD oil in deuterochloroform. Pulse length-based concentration determination (PULCON) enables a direct quantification using an external standard. The signal intensities of the cannabinoids were enhanced during the NMR spectra acquisition by means of multiple suppression of the triglycerides which are a major component of the CBD oil matrix. The validation confirmed linearity for CBD, cannabinol (CBN), ∆9-THC and ∆8-THC in hemp seed oil with sufficient recoveries and precision for screening. Comparing the qNMR results to HPLC-MS/MS data for 46 commercial CBD oils verified the qNMR accuracy for ∆9-THC and CBD but with higher limits of detection. The developed qNMR method paves the way for increasing the sample throughput as a complementary screening before HPLC-MS/MS.
ARTICLE | doi:10.20944/preprints202212.0391.v1
Subject: Biology And Life Sciences, Cell And Developmental Biology Keywords: PUF-8; MPK-1; sperm fate; dedifferentiation; resveratrol; C. elegans germline
Online: 21 December 2022 (07:08:56 CET)
Using the nematode C. elegans germline as a model system, we previously reported that PUF-8 (a PUF RNA-binding protein) and LIP-1 (a dual-specificity phosphatase) repress sperm fate at 20°C and the dedifferentiation of spermatocytes into mitotic cells (termed "spermatocyte dedifferentiation") at 25°C. Thus, double mutants lacking both PUF-8 and LIP-1 produce excess sperm at 20°C, and their spermatocytes return to mitotically dividing cells via dedifferentiation at 25°C, resulting in germline tumors. To gain insight into the molecular competence for spermatocyte dedifferentiation, we compared the germline phenotypes of three mutant strains – fem-3(q20gf), puf-8(q725; fem-3(q20gf), and puf-8(q725); lip-1(zh15). Both fem-3(q20gf) and puf-8(q725); fem-3(q20gf) mutants produced excess sperm like puf-8(q725); lip-1(zh15) double mutants. Our results show that spermatocyte dedifferentiation was not observed in fem-3(q20gf) mutants, but it was more aggressive in puf-8(q725); lip-1(zh15) than in puf-8(q725); fem-3(q20gf) mutants. These results suggest that MPK-1 (the C. elegans ERK1/2 MAPK ortholog) activation by removing the function of LIP-1 in the absence of PUF-8 promotes spermatocyte dedifferentiation. This idea was confirmed using Resveratrol (RSV), a potential activator of MPK-1 and ERK1/2 in C. elegans and human cells. Notably, spermatocyte dedifferentiation was significantly enhanced by RSV treatment, and its effect was blocked by mpk-1 RNAi. We, therefore, conclude that PUF-8 and MPK-1 are normally required to inhibit spermatocyte dedifferentiation and tumorigenesis. Since these regulators are broadly conserved, we suggest that similar regulatory circuitry may control cellular dedifferentiation and tumorigenesis in other organisms, including humans.
ARTICLE | doi:10.20944/preprints202111.0470.v1
Subject: Chemistry And Materials Science, Inorganic And Nuclear Chemistry Keywords: ionic liquids; liquid-liquid extraction; iron extraction; 8-hydroxyquinoline; acetylacetone; thenoyltrifluoroacetone
Online: 25 November 2021 (11:58:50 CET)
(200 words) Imidazolium ionic liquids containing acetylacetone, thenoyltrifluoroacetone, or 8-hydroxyquinoline, respectively, were used as the extracting agents for the separation of traces of iron (III) from its aqueous solutions with or without citric and oxalic acids. The results show that 8-hydroxyquinoline in imidazolium ionic liquids extract iron quantitatively from all the tested solutions including complexing ones, regardless indications of unexpected iron behavior/speciation.
ARTICLE | doi:10.20944/preprints201907.0046.v1
Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: secretome; computed tomography; interleukin-8; tumor-derived factor; C2C12 cells; Cachexia
Online: 3 July 2019 (06:41:42 CEST)
Cachexia is a syndrome characterized by an ongoing loss of skeletal muscle mass associated with poor patient prognosis in non-small cell lung cancer (NSCLC). However, prognostic cachexia biomarkers in NSCLC are unknown. Here, we analyzed computed tomography (CT) images and tumor transcriptome data to identify potentially secreted cachexia biomarkers (PSCB) in NSCLC patients with low-muscularity. We integrated radiomics features (pectoralis muscle, sternum, and T10 vertebra) from CT of 89 NSCLC patients, which allowed us to identify an index for screening muscularity. Next, a tumor transcriptomic-based secretome analysis from these patients (discovery set) was evaluated to identify potential cachexia biomarkers in patients with low-muscularity. The prognostic value of these biomarkers for predicting recurrence and survival outcome was confirmed using expression data from eight lung cancer datasets (validation set). Finally, C2C12 myoblasts differentiated into myotubes were used to evaluate the ability of the selected biomarker, IL-8, in inducing muscle cell atrophy. We identified 75 over-expressed transcripts in patients with low-muscularity, which included IL6, CSF3, and IL8. Also, we identified NCAM1, CNTN1, SCG2, CADM1, IL8, NPTX1, and APOD as PSCB in the tumor secretome. These PSCB were capable of distinguishing worse and better prognosis (recurrence and survival) in NSCLC patients. IL8 was confirmed as a predictor of worse prognosis in all validation sets. In vitro assays revealed that IL-8 promoted C2C12 myotube atrophy. Tumors from low-muscularity patients presented a set of upregulated genes encoding for secreted proteins, including pro-inflammatory cytokines that predict worse overall survival in NSCLC. Among these up-regulated genes, IL8 expression in NSCLC tissues was associated with worse prognosis and the recombinant IL-8 was capable of triggering atrophy in C2C12 myotubes.
TECHNICAL NOTE | doi:10.20944/preprints202209.0011.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: fire management; fire regime; pyrodiversity; pyrogeography; remote sensing; wildlife; wildfire, Landsat
Online: 1 September 2022 (07:41:44 CEST)
A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.
ARTICLE | doi:10.20944/preprints202206.0020.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat-9 data; Qinghai-Tibet Plateau; Lake Waterbody; GEE; Algorithms comparison
Online: 1 June 2022 (13:14:56 CEST)
The monitoring of lake waterbody area in the Qinghai Tibet Plateau (QTP) is of great significance to deal with global climate change. As the latest generation of Landsat series satellites, Landsat-9 data not only have higher radiometric resolution, but also cooperate with other Landsat satellites to greatly improve the temporal resolution. It has great application potential in lake waterbody area monitoring. In order to explore the performance of different algorithms for extracting waterbody and lake waterbody area in Landsat-9 data under large-scale QTP regions, this study relies on Google Earth Engine (GEE) platform and selects 10 waterbody extraction algorithms as the basis to realize the quantitative evaluation of QTP lake waterbody area extraction results. The results show that the Random Forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average error of lake waterbody area extraction is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithms. This study proves that Landsat-9 data can effectively classify QTP waterbodies. With the development of cloud computing technologies such as Gee, more complex models such as RF can be selected to improve the extraction accuracy of water body and Lake area in large-scale research.
ARTICLE | doi:10.20944/preprints202012.0150.v1
Subject: Social Sciences, Geography, Planning And Development Keywords: Change detection; NDVI; Landsat; Land cover land use change; Urban environment
Online: 7 December 2020 (12:44:21 CET)
Urban cities are the major drivers of economic growth and development. Economic growth and development however results in considerable land cover land use dynamics. This study assessed the dynamics in land cover land use that have occurred in New Braunfels, Texas in the last 7 years (2013 - 2020) to observe areas in the city that had experienced considerable shifts in land cover and land use. A 30-meter resolution Landsat images were used to examine possible changes in land cover land use. New Braunfels was observed to have experienced significant changes in land use especially in developed areas. This change can be attributed to the influx of people into the city, contributing to the need for increased urban development. Analysis of this study shows that about 16% (about 553 hectares) of forest land cover class and 28% (about 1,139 hectares) of grassland class in time 1 (August 31, 2013) changed to built-up land use class in time 2 (November 5, 2020). A limitation to this study was that of the spatial resolution of images used. Higher spatial resolution images could impact the producers, users, and overall accuracy assessment. Results from this study can aid in supporting better decision-making for sustainable urban development and climate change mitigation.
ARTICLE | doi:10.20944/preprints202007.0273.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: UAV; forest; ecology; remote sensing; phenology; modis; rgb imagery; phenocam; landsat
Online: 12 July 2020 (18:56:00 CEST)
Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).
ARTICLE | doi:10.20944/preprints201812.0320.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Central Rift Valley, Ethiopia, Landsat images, Lake, land use/land cover
Online: 27 December 2018 (10:49:16 CET)
LULC changes are major environmental challenges in many parts of the world which are adversely affecting ecosystem services. This study was aimed to analyze LULC changes in the ecological landscape of Ethiopia CRV areas from 1985 to 2015. Satellite images were accessed and pre-processing and classification is done. Major LULC types were detected and change analysis was executed. Nine LULC changes were successfully evaluated. The classification result revealed that in 1985, 44.34% of the land was covered with small scale farming followed by mixed cultivated/acacia (21.89%), open woodland (11.96%), and water bodies (9.77%). Whereas for the same study year open grazing land, forest, degraded savannah and settlements accounted the smallest proportion. Though the area varied among land use classes, the trend of share occupied by the LULC types in the study area remained the same in 1995 and 2015. Increase in small and large scale farming, settlements and mixed cultivation/acacia while a decrease in water bodies, forest, and open woodlands is noted. About 86.11% of the land showed major changes in land use/cover. Lastly, DPSIR framework analysis was done and integrated land use and development planning and policy reform are suggested for sustainable land use planning and management.
ARTICLE | doi:10.20944/preprints202309.1040.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: double cropping; multi cropping; cropping intensity; Landsat; NDVI; remote sensing; machine learning
Online: 15 September 2023 (05:40:19 CEST)
The extent of single and multi-cropping systems in any region, and potential changes to it, have consequences on food and resource use raising important policy questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we describe a relatively low-cost and scalable method to identify double cropping at the field-scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. We demonstrate the process by measuring double cropping extent in a portion of Washington State in the Pacific Northwest United States--- a region with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity---that apply rule-based thresholds on vegetation indices---do not work well in regions with high-crop-diversity. Our deep learning model was able to capture the diverse nuances and achieve a high accuracy (99\% overall accuracy and 0.92 Kappa coefficient). Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable to inform several policy questions related to food production and resource use.
ARTICLE | doi:10.20944/preprints202207.0248.v2
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat; urban growth; Land Use Land Cover (LULC); remote sensing; urbanisation; NDVI
Online: 7 September 2023 (04:05:46 CEST)
The rapid growth of urban areas is a major challenge facing cities around the world. This growth can have a significant impact on the local climate, leading to higher temperatures and other changes. In desert climates, the effects of urban expansion can be particularly pronounced. This study investigated the impact of urban expansion on land surface temperature (LST) in Baghdad, Iraq. Notably, this study employs a sophisticated artificial intelligence method known as Random Forest for Land Use Land Cover (LULC) classification, utilizing three Landsat images spanning the temporal spectrum from 1985 to 2021 to meticulously monitor land use transformations and associated LST variations. The results showed that vegetated areas declined by 46.8% during the study period, while built-up areas increased by 124.7%. This decline in vegetation was accompanied by an increase in LST, with bare soil recording the highest temperatures. The study also found that LST has a strong inverse relationship with vegetation and moisture. This means that areas with more vegetation and moisture tend to have lower LSTs. These findings suggest that urban expansion can lead to higher LSTs in desert climates, which can have implications for the health and wellbeing of residents. The study has important implications for urban planners and policymakers in Baghdad and other cities in desert climates. By identifying the main factors that control LST, the study provides insights into strategies for mitigating the effects of urban expansion on temperature.
ARTICLE | doi:10.20944/preprints202306.1621.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: geological exploration; UAV; LiDAR; radiometry; geophysics; remote sensing; Landsat 9; GIS; lithium
Online: 22 June 2023 (12:22:05 CEST)
Due to the energetic transition at course, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. Literature review indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated namely remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provided valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets, compared to Landsat 8. The user-friendly webGIS facilitated data integration, visualization, and sharing, supporting potential users in similar exploration approaches.
ARTICLE | doi:10.20944/preprints202305.1345.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Support Vector Machine; Maximum Likelihood; Minimum Distance; machine learning; classification algorithm; Landsat
Online: 18 May 2023 (12:39:51 CEST)
In order to conduct an accurate classification of the heterogeneous landscape in Jiului Valley, Romania mining basing, four machine learning algorithms (SVMs) and two common algorithms (MLC and MD) have been compared, using a temporal series of Landsat satellite images from the period 1988-2017. By using independent validation, an accuracy assessment was established together with the analysis of the differences between the classification algorithms used. Although all six algorithms used have shown a high overall accuracy (ranging from 80.29% to 93.14%) and Kappa values (from 0.77 to 0.92), SVM-RBF appears to have a higher overall applicability in describing the spatial distribution and the cover density of each land cover category. Results have indicated a large difference in classification accuracy between the SVM-RBF algorithm and commonly used algorithms, the SVM-RBF algorithms have slightly outperformed the MLC with an overall accuracy of 7.14–8.86% and by 0.0833–0.1033 kappa coefficient. On the other hand, the same algorithm have outperformed the MD by and overall accuracy of 9.71–10.86% and by 0.1133–0.1267 kappa coefficient. By using SVM-RBF, certain classified maps have been developed and used for assessing changes by post classification comparison. The results have shown an average growth of 6.5% in mined areas over the studied period.
ARTICLE | doi:10.20944/preprints202207.0071.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Urban Mapping; Impervious Surface Area; Google Earth Engine; GISAI; Spectral Index; Landsat
Online: 5 July 2022 (10:07:01 CEST)
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.
ARTICLE | doi:10.20944/preprints202107.0630.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Africa; Ethiopia; Landsat; Land Use Land Cover Change; Remote Sensing; SWAT model
Online: 28 July 2021 (12:20:13 CEST)
Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover change (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC happened during the last 30 years was investigated comparing the situation in three reference years: 1994, 2004 and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 19.99% between the years 1994-2004, and it decreased by 11.85% in the following period 2004-2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 Hydrologic Response Units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.
ARTICLE | doi:10.20944/preprints201905.0161.v1
Subject: Environmental And Earth Sciences, Ecology Keywords: Landuse and landcover; LULC change; remote sensing; LandSat image; Bahir Dar city
Online: 13 May 2019 (13:33:35 CEST)
Spatio-temporal Land-Use and Land-Cover (LULC) changes have been affecting geo-environmental and climate change globally. This study aims to analyze LULC changes in Bahir Dar city and its surrounds. Landsat 5 TM (1987), Landsat 7 ETM+ (2002) and Landsat 8 OLI (2017) and SPOT images, and aerial photographs, master plan map and Google Earth Landsat images were used to analyze changes. In Bahir Dar city and its surrounds, LULC has been changing in space and time. During 1987-2017, more than 50% of the study area was covered with cropland. Settlement areas have increased from 3.3% in 1987 to 9.13% in 2017. However, wetland vegetation, shrubland, grassland, forest, and waterbodies have degraded. These changes are mainly attributed to population growth and its effect on the environment. Land-use and land-cover is a serious problem and it causes land and environmental degradation, climate change and loss of the biological environment.
ARTICLE | doi:10.20944/preprints201902.0046.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Soil Moisture; Remote Sensing; Landsat; SMAP; Random Forest; Machine Learning; Downscaling; Microwave
Online: 5 February 2019 (08:01:58 CET)
If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most learning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign. This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region.
ARTICLE | doi:10.20944/preprints201808.0172.v1
Subject: Biology And Life Sciences, Plant Sciences Keywords: carotenoid cleavage dioxygenase 8; Orobanche minor; Phelipanche aegyptiaca; shoot branching; Solanum lycopersicum; strigolactones
Online: 8 August 2018 (15:24:04 CEST)
Strigolactones (SLs), a group of plant hormones, induce germination of root-parasitic plants and inhibit shoot branching in many plants. Shoot branching is an important trait that affects the number and quality of flowers and fruits. Root-parasitic plants such as Phelipanche spp. infect tomato roots and cause economic damage in Europe and North Africa. Thus, resistant tomato cultivars are needed. In this study, we found carotenoid cleavage dioxygenase 8-defective mutants of Micro-Tom tomato (slccd8) by the “targeting induced local lesions in genomes” (TILLING) method. The mutants showed excess branching, which was suppressed by exogenously applied SL. Grafting shoot scions of the slccd8 mutants onto wild-type (WT) rootstocks restored normal branching in the scions. The levels of endogenous orobanchol and solanacol in WT were enough detectable, whereas that in the slccd8 mutants were below the detection limit of quantification analysis. Accordingly, root exudates of the slccd8 mutants hardly stimulated seed germination of root parasitic plants. In addition, SL deficiency did not critically affect the fruit traits of Micro-Tom. Using a rhizotron system, we also found that Phelipanche aegyptiaca infection was lower in the slccd8 mutants than in wild-type Micro-Tom because of the low germination. We propose that the slccd8 mutants might be useful as new tomato lines resistant to P. aegyptiaca.
ARTICLE | doi:10.20944/preprints202308.0211.v1
Subject: Engineering, Civil Engineering Keywords: Landsat; random forest classification; performance assessment; irrigation; cropland map; remote sensing; satellite image
Online: 3 August 2023 (02:50:08 CEST)
With growing global concern of food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world, where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications on national policy, project’s annual budgets, and donor fund-ing. Here we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006-2011) and Landsat 8 OLI (2013-2022) images were used to develop a machine-learning model which classifies irrigated and non-irrigated areas in study areas. The random forest classification achieved overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and the consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine-learning model outperforms the existing global cropland map, highlighting the need of such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated area with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge.
ARTICLE | doi:10.20944/preprints202305.0613.v2
Subject: Environmental And Earth Sciences, Ecology Keywords: Yucatan; Puuc; Landsat; Carbon; Species Diversity; ALOS-2 PALSAR-2; tropical dry forests
Online: 21 June 2023 (12:56:03 CEST)
The Puuc Biocultural State Reserve (PBSR is an unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on the mainte-nance of low impact productive activities coordinated by multiple communal and private land-owners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and its relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000-2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (+19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000-2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing has changed over the years after the creation of the PBSR in 2011. An emergent hotspot analysis shows, however, that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012-2021 pe-riod was less common than prior to 2011 and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, results show that land owners outside the PBSR often clear forests with lower carbon density and species diversity than land owners inside the PBSR. This suggests that, compared to land owners outside the PBSR, land owners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012-2021 period minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend the continuation of efforts for providing alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. The combination of these models with contin-uous change detection datasets will allow to reveal underlying ecological processes and generate information better adapted to forest governance scales.
ARTICLE | doi:10.20944/preprints202306.1161.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Mine site; Revegetation; Reclamation; Rehabilitation; Land cover; Sustainable mining; Remote sensing; Landsat image
Online: 16 June 2023 (03:26:15 CEST)
The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to examine the long-term land cover changes at post-closure mine sites. Six representative post-closure mine sites were selected for the evaluation and comparison. The Normalized Difference Vegetation Index (NDVI) analysis, Landsat image classification, post-classification change detection, and Regrowth Index (RI) analysis were conducted to assess the speed and extent of landscape and vegetation recovery at the target mine sites. A significant vegetation recovery was quantified for the mine sites that have experienced active reclamation activities. In contrast, the post-closure mine area undergoing only the passive revegetation typically demonstrated a slow and minor increase in vegetation over time. The actively revegetated mine sites can typically be restored to a level that equals or better than the pre-mining situation. This work confirms that active reclamation and revegetation at post-closure mine sites is critically important in sustainable mining. The quantified mine site reclamation behavior and the relevant sustainable practices would be useful for evidence-based sustainable resource management in Canada.
ARTICLE | doi:10.20944/preprints202303.0526.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Social survey; Mago National Park; Landsat; GIS; Remote sensing; land use land cover
Online: 30 March 2023 (10:38:58 CEST)
Land use land cover change analysis is one of the most particular techniques to understand how land was used in the past, what types of changes are to be expected in the future, as well as the forces and processes behind the changes. Thus, the objective of this study was to investigate the land use land cover changes and its driving forces in Mago National Park, southern Ethiopia. Satellite image of Landsat5 TM (1988, 1998 and 2008) and Landsat8 OLI/TIRS (2018) with a time span of 30 years were employed. In addition, field observation, and social survey were conducted to study the drivers of land use land cover changes. QGIS 3.2 and SPSS (for social data analysis) software’s’ were used for satellite image processing, accuracy assessment, map preparation and descriptively analyze the driving forces of LULCC respectively. Supervised classification with maximum likelihood algorithm was conducted for satellite image analysis and generation of information using Quantum GIS 3.2 Post classification change detection method was applied to quantify the land use/land cover change. The result of the study indicated riverine forest, woodland, grassland, water body, degraded land and bare land as a major land use land cover class in the park. The result of land use land cover classification showed that in 1988 most of the study area was covered by woodland and grass land. In the first period (1988-1998), woodland, riverine forest, water body and bare land decreased by 6.76%, 37.98%, 22.37% and 70.14% respectively, while grass land, and degraded land increased by 16.11% and 85.67% respectively. In the second period, (1998 -2008), woodland, riverine forest and degraded land were decreased by 5.44%, 4.61%, and 80.74% respectively, while grass land, water body and bare land is increased by 14.74%, 3.76% and 52.58% respectively. From 2008-2018 riverine forest, grassland, water body and bare land decreased by 1.33%, 15.16% and 4.82% and 25.02% respectively, while woodland increased by 11.84%, and degraded land increased by 85.49% respectively. Riverine forest, water body, grass land and bare land showed decrement and that of woodland, degraded land indicated increment during study period. From 1988-2018, woodland, riverine forest, water body and bare land indicated decrement and the remaining grass land and bare land cover types indicated increment during study period. The result of social survey indicated that expansion of agriculture, human induced fire, overgrazing and hunting are proximate driving forces of the change in Mago National Park. Population pressure from a different area, poverty, decreased farmlands productivity; education, weak law enforcement and cultural factors are the major underlying causes of the observed changes. Therefore, proper land use planning, legal support, and strong law enforcement are the key recommendations to sustain natural resources of the study area.
ARTICLE | doi:10.20944/preprints202203.0253.v1
Subject: Environmental And Earth Sciences, Soil Science Keywords: soil reflectance composites; digital soil modeling; soil organic carbon; GEOBIA, Landsat; terrain analysis
Online: 17 March 2022 (11:42:28 CET)
There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at field scale for food security, monitoring long-term changes related to soil health and climate change. In Germany, large-scale SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and Landsat-based multi-temporal soil reflectance composites (SRC) as explanatory variables. On the example of an Bavarian test of about 8000 km2, the scale-specific dependencies between the representativeness of 220 soil samples and different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters lead to a significant model improvement at large field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2=0.84 and RMSE=1.99.
TECHNICAL NOTE | doi:10.20944/preprints202003.0038.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Okavango Delta; inundation maps; inundation extent; Landsat; Google Earth Engine; automated time series
Online: 3 March 2020 (11:25:49 CET)
Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.
ARTICLE | doi:10.20944/preprints201911.0218.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Landsat; Google Earth; water index; unsupervised image classification; supervised image classification; Kappa coefficient
Online: 19 November 2019 (03:10:17 CET)
To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.
ARTICLE | doi:10.20944/preprints201811.0347.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Jason-2; Jason-3; glacier; Landsat; Mt. Tanggula; satellite altimeter; Tibet; TOPEX/Poseidon
Online: 15 November 2018 (06:03:39 CET)
An oceanic radar altimeter such as TOPEX/Poseidon (T/P) is typically for observing elevation changes over the open oceans or large inland lakes/rivers, with limited applications over solid earth due to its large footprint and susceptibility to waveform contamination and slope effect. Here we demonstrate that it is possible to construct a long-term time series of glacier elevation change from T/P-series radar altimeters over two flat surfaces near a glacier terminus and an icefield (Sites A and B, with slopes of 2° and 0.8°) in Mt. Tanggula, Tibet, at elevations over 5400 m. We retracked radar waveforms using the subwaveform threshold algorithm, selected quality altimeter data (1/4 of the original) with nearly the same slope and adjusted the original elevations by fitting with a time-varying, 2nd order surface. The glacier elevation changes at the two sites from T/P (1993–2002) show seasonal elevation oscillations with linear rates at about −3 m/year and abnormal seasonal changes around the 1997–98 El Niño. Site A is over a deep valley in southern Tanggula. Its elevation dropped about 30 m over 1993–2002 (from T/P) and the glacier almost disappeared by 2016 (from altimeters and satellite images). Despite the sporadic Jason-2 and Jason-3 altimeter data, we also derived long-term rates of glacier elevation change over 1993–2017. Landsat-derived glacier area and elevation changes near the two sites confirm the rapid glacier thinning from the altimeters. The glacier meltwater near Site A supplied increasing source water to Chibuzhang Co west of Mt. Tanggula, contributing partially to its accelerated rising lake level. The glacier thinning at Site B (icefield) was correlated with the increased discharge of the Tuotuo River in eastern Mt. Tanggula, a source region of the Yangtze River. The successful detection of glacier thinning at the two sites shows that T/P-series altimeters can serve as a virtual station at a flat glacier spot to monitor long-term glacier elevation changes in connection to climate change. This virtual station concept is particularly useful for inaccessible glaciers, but its implementation faces two challenging issues: increasing the volume of quality altimeter data and improving the ranging accuracy over a targeted mountain glacier spot.
ARTICLE | doi:10.20944/preprints201810.0085.v1
Subject: Social Sciences, Geography, Planning And Development Keywords: SDG11; Land Use Efficiency; Open Data, GHSL; Landsat; Urbanization; Urban expansion; Population mapping
Online: 4 October 2018 (15:35:06 CEST)
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet based mainly on two quantitative factors: i) the spatial distribution (density) of built-up structures and ii) the spatial distribution (density) of resident people. Both factors are observed in the long-term temporal domain and per uniform surface units in order to support trends and indicators for monitoring the implementation of international framework agreements. The GHSL uses various input data including global, multi-temporal archives of fine-scale satellite imagery, census data, and volunteered geographic information. In this paper, we present the characteristics of GHSL information to demonstrate how original frameworks of data and tools rooted on Earth Observation could support Sustainable Development Goals monitoring. In particular, we demonstrate the reach of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for the Sustainable Development Goal 11. Our experiments produce a global estimate for the Land Use Efficiency indicator (SDG 11.3.1) for 10,000 urban centers, calculating the ratio of land consumption to population growth rate that took place between 1990 and 2015. The results of our research demonstrate that there is a potential to lift SDG 11.3.1 from a tier 2 as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
REVIEW | doi:10.20944/preprints201809.0059.v1
Subject: Physical Sciences, Biophysics Keywords: landuse change; climate change; garden city model; green vegetation; Landsat; urban heat island
Online: 4 September 2018 (06:28:33 CEST)
The key anthropogenic effects on climate include the changes in land use and emission of greenhouse gases into the atmosphere. Depletion of vegetation poses serious threat that speeds the process of climate change and reduces carbon sequestration by the environment. Thus, the preservation of natural environment in urban areas is an essential component of the garden city model, proposed by Sir Ebenezer Howard in 1898, to ensure ecological balance. Recent Landsat images showed that Kumasi does not have the required percentage of green vegetation as was stipulated in the garden city model on which the city was built. It was observed that most parts of Kumasi's green vegetation have been lost to built environments. This study was conducted to assess the impact of urbanization on the garden city status and its effect on the micro-climate of the city. Significant changes in the vegetation cover of the city was evaluated from Landsat-TM imagery and analysis of a long term climatic data of Kumasi carried out over a 55-year period (1960 to 2015). It was observed that, climatic conditions have slightly changed, as mean surface temperature of has increased by 1.2 °C/ 55 years, due to the significant landuse changes from development of non-transpiring, reduced evaporative urban surfaces. However, the impact is not greatly felt due to the geographical location of the city on the globe despite the evidence of a considerable temperature change. Green vegetation conservation for the city is recommended as a top priority in future for city authorities and planners.
ARTICLE | doi:10.20944/preprints201608.0069.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Rubber (Hevea brasiliensis) plantation; phenology; Xishuangbanna; Landsat; object-based approach; pixel-based approach
Online: 6 August 2016 (11:54:28 CEST)
Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.
ARTICLE | doi:10.20944/preprints202305.0408.v1
Subject: Medicine And Pharmacology, Clinical Medicine Keywords: acute lung injury; endothelial-to-mesenchymal transition; milk fat globule factor 8; Smad1/5/Smad4
Online: 6 May 2023 (09:54:26 CEST)
Background and Objectives: Acute lung injury (ALI) is an inflammatory response in the lung caused by bacteria, viruses, trauma and other factors that results in lack of alveolar surfactant and subsequently respiratory distress. Endothelial-to-mesenchymal transition (EndoMT) is closely related to ALI-induced pulmonary fibrosis development and progression. This study examined the effect of milk fat globulin factor 8 (MFGE8) knockdown and overexpression on lipopolysaccharide (LPS)-induced EndoMT. Methods: Total MFGE8 activity in patients with acute respiratory distress syndrome (ARDS) and healthy volunteers was assessed using a colorimetric kit. In vitro, cell morphology was observed by microscopy, and invasion and migration were tested by Transwell and scratch assays. Specific siRNAs were transfected into HLMECs to downregulate MFGE8, and CD31 and α-SMA protein expression was detected by fluorescence microscopy and FCM. MFGE8, BMP, Smad 1, Smad4, Smad5, Snail, CD31 and α-SMA protein and gene expression was assessed by western blotting (WB) and qRT‒PCR. In vivo, the changes in cell morphology and alveolar septum in the lung of mice (C57BL/6, aged 7-8 weeks, male) were observed by H&E staining. In addition, ECM deposition in lung tissue was detected by Masson staining. CD31 and α-SMA protein expression in lung tissue was measured by WB. Results: The serum level of MFGE8 was lower in the ARDS group than in the control group. MFGE8 was identified as a protective factor associated with hospital survival. In vitro, the treatment of human lung microvascular endothelial cells (HLMECs) with LPS for 96 h clearly changed the cells from the typical cobblestone shape of ECs to spindle-like fibroblasts. The scratch and Transwell assay results indicated that endothelial cell migration and invasion were enhanced in the LPS group compared with the control group. Fluorescence microscopy, FCM, WB and PCR showed that LPS greatly inhibited CD31 (endothelial marker) expression and increased α-SMA (mesenchymal marker) expression. These data showed that LPS could induce EndoMT in HLMECs. Notably, siRNA-treated HLMECs yielded the same results. The administration of rhMFGE8 to HLMECs in the LPS or LPS+siRNA group ameliorated the changes in cell morphology and decreased cell migration and invasion. rhMFGE8 attenuated the effect of LPS or LPS+siRNA on EndoMT induction by increasing CD31 and decreasing α-SMA protein and gene expression. Moreover, activation of BMP/Smad1/5-Smad4 signalling in response to LPS and Snail (related to EndoMT transcription factors) expression were increased by MFGE8 knockdown but inhibited by rhMFGE8. In vivo, H&E staining revealed a thickened alveolar septum in the LPS group, and the thickness increased over time; in contrast, rhMFGE8 reversed this effect. ECM deposition occurred early in ALI induced by LPS and increased over time, and the administration of rhMFGE8 reversed this effect. WB showed that LPS inhibited CD31 protein expression and increased α-SMA protein expression in lung tissue. Conclusions: rhMFGE8 exerts a protective effect early in LPS-induced EndoMT through BMP/Smad1/5/Smad4 signalling and could be a therapeutic target in ALI.
REVIEW | doi:10.20944/preprints202310.0892.v1
Subject: Environmental And Earth Sciences, Geography Keywords: earth observation; rice mapping; SCOPUS; Vietnamese Mekong delta; bibliometric analysis; google earth engine; MODIS; Landsat
Online: 13 October 2023 (17:46:12 CEST)
The present article summarises Earth Observation (EO)-based rice mapping strategies since 1979, with a focus on data, methodologies, and methods based on 3,700 research publications across global literature and its comparison with the Vietnamese Mekong Delta (VMD). Various quan-titative analyses were conducted through bibliometric analysis using the VOS viewer and Scopus database. Optical images, particularly MODIS and Landsat time series datasets, were found to be the most commonly utilized. Landsat data had the highest share in the global context, while MODIS data research dominated in the VMD, while Sentinel series data and the Google Earth Engine (GEE) platform became more popular in recent years. The research on rice mapping using UAVs has been gradually creeping into global rice mapping research but is a loophole yet to be implemented in the VMD. The most widely used approaches for rice mapping globally were Random Forest, Support Vector Machine, and Principal Component Analysis. Indices like EVI, NDVI, and RVI were commonly used for rice mapping and monitoring. The findings underscore the critical role of EO-based rice mapping studies in the VMD in addressing sustainability and food security chal-lenges.
ARTICLE | doi:10.20944/preprints201809.0192.v1
Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land surface temperature; the Flexible Spatiotemporal Data Fusion method; Landsat-like; Building density; urban expansion
Online: 11 September 2018 (11:17:43 CEST)
Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environments under rapid urban expansion. Current MODIS data is, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data can’t explore temporally the refined analysis due to the low temporal resolution. In order to resolve this situation, we used MODIS and Landsat data to generate “Landsat-like” data by using the flexible spatiotemporal data fusion method (FSDAF), and then studied spatiotemporal variation of land surface temperature (LST) and its driving factors. The results showed that 1) The estimated “Landsat-like” data have high precision; 2) By comparing 2013 and 2016 datasets, LST increases ranging from 1.8°C to 4°C were measurable in areas where the impervious surface area (ISA) increased, while LST decreases ranging from -3.52°C to -0.70°C were detected in areas where ISA decreased; 3) LST has a strongly negative relationship with the Normalized Difference Vegetation Index (NDVI), and a strongly positive relationship with Normalized Difference Built Index (NDBI) in summer; and 4) LST is well correlated with Building density (BD), in a complex conic mode, and LST may increase by 0.460°C to 0.786°C when BD increases by 0.1. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.
ARTICLE | doi:10.20944/preprints201803.0233.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: wetland vegetation; normalized difference vegetation index (NDVI); Landsat; precipitation; air temperature; snowmelt; extremely arid regions
Online: 28 March 2018 (06:13:23 CEST)
Based on 541 Landsat images between 1988 and 2016, the normalized difference vegetation indices (NDVIs) of the wetland vegetation at Xitugou (XTG) and Wowachi (WWC) inside the Dunhuang Yangguan National Nature Reserve (YNNR) in northwest China were calculated for assessing impacts of climate change on wetland vegetation in the YNNR. It was found that the wetland vegetation at the XTG and WWC both had shown a significant increasing trend in the past 30 years, and the increase in both annual mean temperature and peak snow depth over the Altun Mountains led to the increase of wetland vegetation. The influence of local precipitation on the XTG wetland vegetation was greater than on the WWC wetland vegetation, which demonstrates that in extremely arid regions, the major constrain to the wetland vegetation is water availability in soils which is greatly related to the surface water detention and discharge of groundwater. At both XTG and WWC, snowmelt from the Altun Mountains is the main contributor to the groundwater discharge, while local precipitation plays a less role in influencing the wetland vegetation at the WWC than at the XTG, because the wetland vegetation grows on a relatively flat terrain at the WWC, while in a stream channel at the XTG.
ARTICLE | doi:10.20944/preprints201712.0045.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: endorheic; lake; Central Asia; evaporation; semi-arid; Kazakhstan; climate change; Landsat; regional climate model; Burabay
Online: 7 December 2017 (14:56:58 CET)
Both climate change and anthropogenic activities contribute to the deterioration of terrestrial water resources and ecosystems worldwide. Central Asian endorheic basins are among the most affected regions through both climate and human impacts. Here, we used a digital elevation model, digitized bathymetry maps and Landsat images to estimate the areal water cover extent and volumetric storage changes in small terminal lakes in Burabay National Nature Park (BNNP), located in Northern Central Asia (CA), for the period of 1986 to 2016. Based on the analysis of long-term climatic data from meteorological stations, short-term hydrometeorological network observations, gridded climate datasets (CRU) and global atmospheric reanalysis (ERA Interim), we have evaluated the impacts of historical climatic conditions on the water balance of BNNP lake catchments. We also discuss the future based on regional climate model projections. We attribute the overall decline of BNNP lakes to long-term deficit of water balance with lake evaporation loss exceeding precipitation inputs. Direct anthropogenic water abstraction has a minor importance in water balance. However, the changes in watersheds caused by the expansion of human settlements and roads disrupting water drainage may play a more significant role in lake water storage decline. More precise water resources assessment at the local scale will be facilitated by further development of freely available higher spatial resolution remote sensing products. In addition, the results of this work can be used for the development of lake/reservoir evaporation models driven by remote sensing and atmospheric reanalysis data without the direct use of ground observations.
ARTICLE | doi:10.20944/preprints201708.0065.v1
Subject: Biology And Life Sciences, Biophysics Keywords: 4SM; satellite derived bathymetry; water depth; water column correction; remote sensing; Landsat; San Lorenzo Channel
Online: 18 August 2017 (12:14:16 CEST)
Satellite derived bathymetry methods over coastal areas were born to deliver basic and useful information like bathymetry. However, the process is not straightforward, the main limitation being the need of field data. The Self-calibrated Spectral Supervised Shallow-water Modeler (4SM) method was tested to obtain coastal bathymetry without the use of any field data. Using LANDSAT-8 multispectral images from 2013 to 2016, a bathymetric time series was produced. Groundtruthed depths and an alternative method, Stumpf’s Band Ratio Algorithm, were used to verify the results. Retrieved (4SM) vs groundtruthed depths scored an average r2 (0.90), and a low error (RMSE = 1.47 m). Also 4SM showed, over the whole time series, the same average accuracy of the control method (40%). Advantages, limitations and operability under complex atmosphere and water column conditions, and high and low-albedo bottom processing capabilities of 4SM are discussed. In conclusion, the findings suggest that 4SM is equally accurate as the commonly used Stumpf’s method, the only difference being the independence of 4SM to previous field data, and the potential to deliver bottom spectral characteristics for further modelling. 4SM represents a significative advance in coastal remote sensing potential to obtain bathymetry and optical properties of the marine bottom.