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Preprint ARTICLE | doi:10.20944/preprints202306.0390.v1

Regularization Algorithm for Right-Angled Polygon Building Outlines with Jagged Edges

Linghui Kong, Haizhong Qian, Yuqing Wu, Xinyu Niu, Di Wang, Zhekun Huang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: map generalization; jagged edge; building outline; regularization

Online: 6 June 2023 (07:50:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0021.v1

Quantitative Characterization of Coastal Cliff Retreat and Landslide Processes at Portonovo-Trave Cliffs (Conero, Ancona, Italy) Using Multi-Source Remote Sensing Data

Nicola Fullin, Enrico Duo, Stefano Fabbri, Mirko Francioni, Monica Ghirotti, Paolo Ciavola

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Rocky coast; Cliff erosion; UAV; Conero

Online: 1 June 2023 (04:00:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.2126.v1

Vector Angular Continuity in the Fusion of Coseismic Deformations at Multiple Optical Correlation Scales

Rui Guo, Qiming Zeng, Shangzong Lu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: deformation angle continuity; coseismic deformation; optical remote sensing; Maduo earthquake

Online: 30 May 2023 (11:26:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.2125.v1

Performance of Water Indices for Water Resources Monitoring Using Large-Scale Sentinel-2 Data

Mathias Tesfaye, Lutz Breuer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-2; remote sensing; Google Earth Engine; large-scale; water resource

Online: 30 May 2023 (11:24:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0191.v2

A Triangular Grid Filter Method Based on the Slope Filter

Chuanli Kang, Zitao Lin, Siyi Wu, Yiling Lan, Chongming Geng, Sai Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Slope Filter; point cloud; triangular grid; KD-Tree-Based Euclidean Clustering

Online: 30 May 2023 (05:44:25 CEST)

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High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

Preprint ARTICLE | doi:10.20944/preprints202305.2055.v1

Superconducting Gravimeters: A Novel Tool for Validating Remote Sensing Evapotranspiration Products

Jonatan Pendiuk, María Florencia Degano, Luis Guarracino, Raúl Eduardo Rivas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; ground-truth data; validation; superconducting gravimeters; evapotranspiration

Online: 30 May 2023 (05:31:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1982.v1

Evaluating the Impact of Engineering Works in Tidal Flats Using Time-Series Satellite Images – Case of the Mont-Saint-Michel Bay, France

Jean-Paul Deroin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: sedimentation; erosion; Sentinel 2; EOS-Aster

Online: 29 May 2023 (05:44:18 CEST)

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The Mont-Saint-Michel is known worldwide for its unique combination of the natural site and the Medieval abbey at the top of the rocky islet. But the Mont is also located within an estuarine complex, which is considerably silting up. For two decades, large-scale works were planned to prevent the Mont from being surrounded by the expanding salt meadows. The construction of a new dam over the Couesnon River, the digging of two new channels, and the destruction of the causeway were the main operations carried out between 2007 and 2015. The remote sensing approach is fully suitable for evaluating the real impact of the engineering project in both time and space, particularly the expected large-scale hydrosedimentary effects, for reestablishing the maritime landscape around the Mont. Sentinel-2 satellite data have been used for the period from 2015 to 2023. Aster data were used for the previous period covering 2000 to 2017. Aerial photographs and an ALOS scene have been also used. The remote sensing approach is based on time-series images. It allows identifying local or regional consequences and temporary or permanent effects. The migration of the different channels (especially for the new west and east Couesnon river courses) and the erosion-progradation balance of the vegetation through space and time are the main features to study. Between 2007 and 2023, the erosion of the salt meadows is significant to the south-west of the Mont (− 150 ha) but more limited to the south-east (− 65 ha). The erosion effect is limited to the immediate environment because the vegetation fringe of the uppermost tidal flat along the main dike is slightly increasing (+ 35 ha) to the west and to the east (+ 40 ha). During the same period, the sedimentation considerably increased to the north-east of the Bay, between the Bec d’Andaine, the Grouin du Sud and Tombelaine islet, which seems now facing the same silting-up problem. At this stage, the remote-sensing survey indicates mixed results for the engineering project.

Preprint TECHNICAL NOTE | doi:10.20944/preprints202305.1918.v1

Characteristics and Evolution of Structural and Functional Connectivity in a Large Catchment (Poyang Lake) during the Past 30 Years

Bingru Zeng, Yunliang Li, Jing Yao, Zhiqiang Tan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Structural connectivity; Functional connectivity; Poyang Lake catchment; Runoff and sediment; Remote sensing and hydrology

Online: 26 May 2023 (10:34:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1683.v1

Thaw-Season InSAR Surface Displacements and Frost Susceptibility Mapping to Support Community-Scale Planning in Ilulissat, West-Greenland

Johanna Scheer, Rafael Caduff, Penelope How, Marco Marcer, Tazio Strozzi, Annett Bartsch, Thomas Ingeman-Nielsen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: InSAR; permafrost; active layer; Arctic infrastructure; ice content

Online: 24 May 2023 (04:15:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1631.v1

Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest

Luciano Emmert, Robinson Isaac Negrón-Juárez, Jeffrey Quintin Chambers, Joaquim dos Santos, Adriano José Nogueira Lima, Susan Trumbore, Daniel Magnabosco Marra

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: blowdowns; crown damage; forest inventory; extreme wind gusts; natural disturbances; spatial resolution; Spectral Mixture Analysis

Online: 23 May 2023 (08:53:39 CEST)

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Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p<0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

Preprint ARTICLE | doi:10.20944/preprints202305.1586.v1

Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan

Mohammed B Altoom, Elhadi Adam, Khalid Adem Ali

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Rain gauge; observations; satellite-based precipitation; spatio-temporal; TMPA; CHIRPS; ARC2; and North Darfur State; Sudan

Online: 23 May 2023 (05:36:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1345.v1

Comparing the Performance of Different Classification Algorithms for Mapping and Assessing Land Cover Changes in Areas with Surface Mining and Complex Landscape Using Landsat Imagery

Iosif Vorovencii

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)

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Preprint ARTICLE | doi:10.20944/preprints202305.1054.v1

Characterization of antenna radiation pattern and penetration depth in GPR field tests

Pavel Morozov, Fedor Morozov, Maxim Lazarev, Leonid Bogolyubov, Alexei Popov

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: impulse subsurface radar, radiation pattern, archaeological and natural objects

Online: 15 May 2023 (14:33:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0782.v1

Spatiotemporal Variability of Global Atmospheric Methane Observed from Two Decades of Satellite Hyperspectral Infrared Sounders

Lihang Zhou, Juying Warner, Nicholas R.Nalli, Zigang Wei, Youmi Oh, Lori Bruhwiler, Xingpin Liu, Murty Divakarla, Ken Pryor, Satya Kalluri, Mitch D. Goldberg

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyperspectral IR Sounding, Methane, CH4 Concentration, AIRS, CrIS, CH4 Trends

Online: 11 May 2023 (04:26:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0750.v1

A New Approach Combining a Multilayer Radiative Transfer Model with an Individual Based Forest Model: Application to Boreal Forests in Finland

Hans Henniger, Friedrich J Bohn, Andreas Huth

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest model; radiative transfer; vegetation indices; individual based; forest reflectance

Online: 10 May 2023 (11:31:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0604.v1

Atmospheric Ducts and Their Electromagnetic Propagation Characteristics in the Northwestern South China Sea

Ning Yang, Debin Su, Tao Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: atmospheric ducts; northwestern SCS; parabolic equation model; propagation loss

Online: 9 May 2023 (08:33:51 CEST)

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The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are a significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ERA5 in studying atmospheric ducts using measured data, we analyzed the space-time distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. Using the parabolic equation model, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in both uniform and non-uniform duct environments within a typical atmospheric duct structure. The study found that the NCEP CFSv2 data accurately captures the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height obtained from the inversion of ERA5 and the measured data has a good consistency. The occurrence rate and height of evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns. Conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layer, mainly concentrated from June to August. The monsoon plays a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. Electromagnetic propagation simulations demonstrate that higher frequency and lower elevation angles of radiation sources in the trapping layer of the evaporation duct make it easier to be trapped. As the evaporation duct height decreases, the amplitude of the "sinusoidal fluctuation" of the propagation loss also decreases. Frequency changes of the radiation source in the surface duct environment have minimal impact on electromagnetic propagation loss, but the elevation angle of the radiation source is a critical factor. The frequency of the "sinusoidal fluctuation" in propagation loss is higher in a hybrid duct environment compared to a uniform surface duct. Additionally, the propagation loss increases faster with distance at the height of the evaporation duct, resulting in greater electromagnetic propagation loss.

Preprint ARTICLE | doi:10.20944/preprints202305.0381.v1

Coupling Normalized Abundance with an Improved Continuum Removal Algorithm for Quantitative Inversion of Carbonate Minerals Using Hyperspectral Data

Lei Chen, Linfeng Wang, Ying Ma, Yanzhen Lin, Shengbo Chen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: improved continuum removal; abundance normalization; continuum removal band depth (CRBD); linear correlation; carbonate mineral inversion

Online: 6 May 2023 (05:22:50 CEST)

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Preprint ESSAY | doi:10.20944/preprints202305.0371.v1

Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data

Runxiang LI, Xiaohong Gao, Feifei Shi, Hao Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land cover; Scale effect; Uncertainty; Spatial Heterogeneity

Online: 6 May 2023 (03:04:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0351.v1

Greening and Browning Trends on the Pacific Slope of Peru and Northern Chile

Hugo Vincent Lepage, Eustace Barnes, Eleanor Kor, Morag Hunter, Crispin H. W. Barnes

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: MODIS; EVI; time series; greening; browning; Andes; Peru; climate zones; life zones; trend

Online: 5 May 2023 (10:34:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0191.v1

Triangular Grid Filter Method Based on Slope Filter

Chuanli Kang, Zitao Lin, Siyi Wu, Yiling Lan, Chongming Geng, Sai Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Slope Filter; point cloud; triangular grid; KD-Tree-Based Euclidean Clustering

Online: 4 May 2023 (04:36:07 CEST)

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High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

Preprint ARTICLE | doi:10.20944/preprints202305.0134.v1

HALF: A High-performance Automated Large-scale Land Cover Mapping Framework

Jiarui Zhang, Zhiyi Fu, Yilin Zhu, Bin Wang, Keran Sun, Feng Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land Cover; High-performance computing; Remote sensing; Workflow; Automation

Online: 3 May 2023 (10:50:07 CEST)

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Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.

Preprint ARTICLE | doi:10.20944/preprints202305.0042.v1

Determination of Multi-Spectral Band Utility for Mapping Wildland Fire Burn Extent and Severity

Cole McCall, Dale Hamilton

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Support Vector Machine (SVM); Worldview2; Satellite Imagery; Iterative Dichotomiser 3 (ID3); Burn Extent; Burn Severity; Biomass Consumption

Online: 2 May 2023 (02:23:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0029.v1

High Resolution Profiling the Turbulence in an Atmosphere Using UAVs Flight Logs

Alexander Shelekhov, Aleksey Afanasiev, Evgenia Shelekhova, Alexey Kobzev, Alexey Tel’minov, Alexander Molchunov, Olga Poplevina

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: profile; turbulence; wind velocity; UAV; anemometer; spectra; correlation; scales of turbulence

Online: 1 May 2023 (11:31:32 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202304.1156.v1

Analysis and Correction of the Seafloor Topography Distortion Caused by the Sound Velocity Error for MBS Soundings

Wenzhou Sun, Anmin Zeng, Xiang Zhao

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: multi-beam bathymetric system (MBS); seafloor topography distortion; SVP area; acoustic ray tracing; beam footprint

Online: 28 April 2023 (09:37:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.1131.v1

Quantifying Grassland Biomass and Regenerative Grazing Using Satellite Remote Sensing and Machine Learning

Michael Gbenga Ogungbuyi, Juan Pablo Guerschman, Andrew Martin Fischer, Richard Azu Crabbe, Caroline Mohammed, Peter Scarth, Phil Tickle, Jason Whitehead, Matthew Tom Harrison

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; machine learning; regenerative grazing; grassland biomass; total standing dry matter; digital agriculture; grazing management; climate change; Cibo Labs; Sentinel-2

Online: 28 April 2023 (07:15:03 CEST)

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The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.

Preprint ARTICLE | doi:10.20944/preprints202304.1062.v1

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

Jie Wang, Chunjing Yao, Hongchao Ma, Junhao Xu, Chen Qian

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LiDAR; Tree Segmentation; Tree Species Identification; Tree Species Identification; DBN; Forest Parameter

Online: 27 April 2023 (09:35:20 CEST)

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The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide precise tree height and detailed vertical structure of the tree stands. Coordinating some representative ground sample plots, LiDAR can be used to estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. Firstly, the tree crowns are segmented using the profile-rotating algorithm, and dominant trees height are used to check and rectify the tree locations. Secondly, considering the correlation between forestry parameters and tree species, we establish models to distinguish between species using geometric characteristics of tree crowns. Thirdly, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass and stock volume can be extracted from trees. The prediction models of forestry parameters can also be verified, which can be extended to accurate large-scale forestry surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east-west canopy width, north-south canopy width, diameter at breast height, above-ground biomass and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based estimation of forestry parameters is practical and that these forestry parameter prediction models can be widely applied in forest resource monitoring.

Preprint ARTICLE | doi:10.20944/preprints202304.1021.v1

Automatic Point Cloud Colorization of Ground-based LiDAR Data Using Video Imagery Without POS

Junhao Xu, Chunjing Yao, Hongchao Ma, Chen Qian, Jie Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: camera calibration; registration; normalized Zernike moments; corresponding point matching; essential matrix; relative orientation; absolute orientation; point cloud coloring

Online: 27 April 2023 (03:54:51 CEST)

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With the continuous development of 3D city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of LiDAR and camera in mobile measurement systems provides a new and highly effective solution. The LiDAR can quickly and accurately acquire the 3D spatial coordinates of target objects, while optical imagery contains abundant color information. If the two can be integrated, they can play an important role in multiple fields such as streetscape modeling, archaeology and digital city, etc. Currently, integrated mobile measurement systems commonly require cameras, lasers, POS and IMU, thus the hardware cost is relatively expensive and the system integration is complex. Therefore, in this paper we propose a simple ground mobile measurement system composed of a LiDAR and a GoPro camera without a POS system, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. Normalized Zernike moments are region-based shape descriptors that can reflect the features of images in multiple dimensions even for low-quality video images. The results show that registration based on normalized Zernike moments provides a good result, with an error accuracy of 0.5-1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing adjacent video image relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the SURF algorithm with distance restriction and RANSAC to select corresponding points, which can improve the reliability of the corresponding points. The results indicate that the accuracy of the relative orientation method is high. Moreover, this method can converge to good results for stereo image pairs with large rotation angles and displacement amounts. Therefore, relative orientation based on essential matrix decomposition and nonlinear optimization has good applicability. (4) The video imagery suffers from significant motion blur and boundary distortion. Therefore, a point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. Experimental results show that the coloring accuracy between the video imagery and point cloud data is high, meeting the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

Preprint ARTICLE | doi:10.20944/preprints202304.0960.v1

First Retrievals of Surface and Atmospheric Properties Using EnMAP Measurements over Antarctica

Alexander Kokhanovsky, Maximillian Brell, Karl Segl, Giovanni Bianchini, Christian Lanconelli, Angelo Lupi, Boyan Petkov, Ghislain Picard, Laurent Arnaud, Robert Stone, Sabine Chabrillat

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: snow remote sensing; radiative transfer; light scattering; ice grain size; snow albedo

Online: 26 April 2023 (05:01:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0926.v1

The Potential of Visual ChatGPT For Remote Sensing

Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves, Ana Paula Marques Ramos, José Marcato Junior

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: artificial intelligence; image analysis; visual language model

Online: 25 April 2023 (10:47:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0831.v1

A Survey on Digital Agriculture in Five West African Countries

Jules DEGILA, Ida TOGNISSE, Anne-Carole HONFOGA, Ariane HOUETOHOSSOU, Fréjus SODEDJI, Gracias AVAKOUDJO, Achille ASSOGBADJO, Souand TAHI

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Digital agriculture; Innovations, Artificial Intelligence; Internet of Things; Machine Learning; West Africa.

Online: 24 April 2023 (06:18:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0653.v1

Remote Sensing Based Land Cover Map of Watersheds in the Swedish Arctics: Study of Spatial and Temporal Variabilities of Land Cover

Yves AUDA Auda, Erik J. Lundin, David Gustafsson, Oleg S. Pokrovsky, Simon Cazaurang, Laurent Orgogozo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land cover; sentinel-2 images; random forest; boreal forest; alpine tundra

Online: 20 April 2023 (10:51:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0496.v1

Aerial Identification of Fruit Maturity in Amazonian Palms via Plant-Canopy Modeling

Willintong Marin, Ivan F. Mondragon, Julian D. Colorado

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV; Deep Learning; Vegetation indices; graphs models; Mauritia Flexuosa Palm; dense forests

Online: 18 April 2023 (05:50:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0479.v1

Analysis of Relationship Between Grain Yield and NDVI From MODIS in the Fez-Meknes Region, Morocco

Belmahi Mohamed, Hanchane Mohamed, Nir Y. Krakauer, Kessabi Ridouane, Bouayad Hind, Mahjoub Aziz, Zouhri Driss

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Normalized difference vegetation index; MODIS; Cereal grains; Yield prediction; Fez-Meknes region

Online: 18 April 2023 (04:13:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0464.v1

Real-World Urban Light Emission Functions and Quantitative Comparison With Spacecraft Measurements

Brian Russell Espey, Xinhang Yan, Kevin Patrascu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: light pollution; public lighting; photometry; LiDAR; Digital Elevation Models; VIIRS DNB

Online: 18 April 2023 (03:13:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0424.v1

Spatiotemporal Heterogeneity of Green Land and Its Effect on the Thermal Comfort of Urban Human Settlements in the Central Coast of China, a Case in Rizhao Region

Tao Pan, Shanfeng He, Zhaoyu Liu, Liming Jiang, Qinglei Zhao, Rafiq Hamdi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land use; greening; land-surface thermal properties; residential environments; central coast region of China

Online: 17 April 2023 (07:47:16 CEST)

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The central coast is one of the importantly livable regions in China, and green land plays a meaningful role in improving human settlements through regulating ecosystem services. However, the research on spatiotemporal heterogeneity of green land in different urbanized areas are still lacking in this region, which limits the timeliness of reporting on the land-surface thermal properties of residential environment from green land perspective. To address this issue, the synergistic methodology of “artificial numerical technology -- urban interior mixed pixel classification -- surface radiation energy balance model” was applied to investigate the green land change and its impact on human settlements in Rizhao region, which is known for its coastal ecology and greenery in China and obtained the United Nations habitat award. Main conclusions of this study are below: (1) Although total green land decreased by 474.05 km2, resulting in a decrease of 9.17% in its share of the entire study area from 2000-2022 during the urbanization process, but the greening levels improved within the built-up area, with the greening proportion of 25.34% in 2000 and 42.98 km2 in 2022. (2) Differences in green land at different urbanized regions were first observed, namely, urban greening rate was 37.78% in the old urban areas of 2022, while it was up to 46.43% in new urban areas. More attention was paid to the construction of urban green space during urban expansion, which will inevitably bring better visual and comfort experience to residents. (3) Thermal comfort indicators for urban residents were evaluated in latent heat flux (0-457.83 W/m2), sensible heat flux (0-645.09 W/m2), total available energy (254.07-659.42 W/m2) and others. (4) The lowered cooling temperature effect in the middle, high green land density region was 1.05 ℃ and 2.12 ℃ compared to the low-density region, and the average air temperature was 25.86 ℃. Also, spatial patterns of hot and cold uncomfortable areas were displayed in this study. Overall, this study provides the meaningfully practical reference for exploring the spatiotemporal heterogeneity of green land and its impact on residents' environments from the perspective of land-surface thermal properties in coastal areas of China and around the world.

Preprint ARTICLE | doi:10.20944/preprints202304.0305.v1

Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine Resolution Data in West Africa

Beatrice Asenso Barnieh, Li Jia, Massimo Menenti, Le Yu, Emmanuel Kwesi Nyantakyi, Amos Tiereyangn Kabo-Bah, Min Jiang, Jie Zhou, Yunzhe Lv, Yelong Zeng, Ali Bennour

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Natural Vegetation; Intensity Analysis; Spatial Patterns; Systematic Transitions; Random Transitions; West Africa

Online: 13 April 2023 (07:44:04 CEST)

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Land Use /Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key among the past changes is the loss of natural vegetation related to changes in different Land Use/Land Cover (LULC) categories, e.g., water bodies, wetland and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in the Africa continent. Here we applied an open and synergistic framework to update and improve the existing LULC maps at every five years intervals from 1990 to 2020 in West Africa at a finer spatial resolution of 30m. Next, we detected spatial temporal pattern in past and present LULC changes with the intensity analysis framework in the periods of 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 (first period) and 2010–2020 (recent period) than in 2000–2010 (middle). We observed consistent gains in rangeland (shrubland and grassland) in all the periods, which confirms the observed re-greening of rangeland in the West Africa continent. By contrast, forestland areas experienced consistent losses for the entire periods, which indicate deforestation and degradation. As regards cropland, a net loss was observed in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all the periods. We observed net gains in water bodies in 1990–2000 and net losses in the last period. We highlighted the active forestland losses as systematic and hence a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concern and must send an early warning signal to policy makers that the food security of marginal geographic locations is under threat despite the massive expansion of cropland observed in this study area. The intensity analysis framework was vital to identify the settlement category as relevant for LULC change management intervention across time and space in West Africa instead of focusing on dynamics of all the LULC categories that may be irrelevant and hence a cost effective LULC change management approach. Policies to control the expansion of settlements will indirectly regulate the expansion of cropland field because we observed that as settlement encroached on cropland, simultaneously cropland encroached on natural vegetation to meet the food security requirements of the inhabitants.

Preprint ARTICLE | doi:10.20944/preprints202304.0264.v1

Early Detection of Crop Types by Integrating Sentinel-1A and Sentinel-2 Imagery based on the CNN Model

Hongwei Zhao, Jia Liu, Liang Sun, Jianqiang Ren

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: early-season detection; crop types; convolutional neural network; Dual-1DCNN; SAR; optical image; Sentinel-1; Sentinel-2

Online: 12 April 2023 (08:49:04 CEST)

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Early-season crop-type data are required for a variety of agricultural monitoring and decision-making applications. The early season in this study referred to the prophase and middle of a growth season. Early-season detection of crop types remains challenging because of limited discriminative features, especially for landscapes that are characterized by complex cropping patterns. In fact, different remote-sensing satellites can increase the frequency of data acquisition, which can provide more information in the early season. Moreover, optical and radar sensors have different degrees of sensitivity to crop parameters. Therefore, the integration and application of two types of multitemporal data are of great significance to improve the accuracy and timeliness of crop type detection.In deep learning (DL), convolutional neural network (CNN) and recurrent neural network (RNN) models have great potential for temporal feature extraction. Compared with RNNs, CNNs usually have fewer parameters and are more conducive to determining early-season detection dates of different crop types, which requires a lot of training because of the need to model on different dates. Nevertheless, revisit dates and temporal intervals of different satellites are usually different, resulting in different data acquisition time series; these data cannot be directly used as input for the same convolutional layer. To address this challenge, a Dual-1DCNN was bulit based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtained the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2). To verify this method, classical methods support vector machine (SVM) and random forest (RF) were implemented. The results demonstrated the following: (1) the Dual-1DCNN extracted discriminative features from S1A and S2 time series at the early season by producing the highest overall accuracy (OA: 87.23%); (2) for summer maize, cotton, and common yam rhizome, the Dual-1DCNN achieved F1 values of 92.39%, 87.71%, and 84.38%, respectively, at the early season (moreover, the early seasons were almost 40, 70, and 80 days before the end of the growth seasons, respectively). These findings suggested that the Dual-1DCNN is promising for the accurate and timely detection of crop types.

Preprint ARTICLE | doi:10.20944/preprints202304.0197.v1

Quantitative analysis of desertification driving mechanisms in the Shiyang River Basin: Examining interactive effects of key factors through the Geographic Detector Model

Maurice Ngabire, Tao Wang, Jie Liao, Ghada Sahbeni

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Desertification; Geographical Detector Model; Google Earth Engine; Driving factors; The Shiyang River Basin

Online: 11 April 2023 (05:15:47 CEST)

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Desertification is a global environmental and socio-economical issue threatening humanity's survival and development. The Shiyang River Basin ecosystem is vulnerable and prone to desertification. In addition, establishing the quantitative analysis of desertification driving factors and understanding their relative contribution, separately or combined, is still an unresolved problem. The present study applied geographic information system (GIS) techniques and a geographic detector model to quantify desertification spatial extent and driving mechanisms. This research utilized Fractional Vegetation Cover (FVC) to elucidate desertification spatial heterogeneity. The 30 years Coefficient of Variation (CV) of the Normalized Difference Vegetation Index (NDVI) was a dependent variable and indicator of ecosystem terrestrial conditions; Elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables. The results showed that 89.41% of the total area is under desertification risk, where 20.99% is extremely desertified, 34.45% is severely desertified, 12.05% is moderately, and 21.92% is slightly desertified. The results from the Geodetector model showed that Power Determinant (PD) values ranged between 0.004 and 0.270. Elevation and soil types had the highest contributing factors with PD values of 0.270 and 0.227, whereas precipitation, soil salinity, the buffer of the waterway, and wind velocity played a moderate role with PD values of 0.146, 0.117, 0.107, and 0.071. Near-surface air temperature, road buffer, and land cover dynamics exhibited lower impact with PD values of 0.028, 0.013, and 0.004. In most cases, investigating the interaction between driving factors resulted in a mutual or non-linear enhancement. There was an apparent linear and mutual enhancement between elevation and soil salinity, precipitation, and soil types with values of 0.3513, 0.3232, and 0.3204, respectively. In addition, there was a mutual enhancement between soil salinity and soil types with a value of 0.2962. On the other hand, a non-linear enhancement was observed between Elevation and near-surface air temperature (0.3116), Elevation and Land cover dynamics (0.2759), soil types and near-surface air temperature (0.2687), land cover dynamics and soil types (0.234), precipitation and near-surface air temperature (0.2248), precipitation and wind velocity (0.2248), and between land cover dynamics and precipitation (0.223). This research revealed irrefutable evidence that environmental factors might be the primary drivers of ecosystem disturbance, provided the basis for the environmental footprint of desertification mechanism, and might be a cornerstone for future policy on ecological restoration sustainability in the Shiyang River Basin.

Preprint ARTICLE | doi:10.20944/preprints202304.0159.v1

Application of GIS and Remote Sensing to Identify Changes in Surface Water Bodies and Wetland Depletion in the Kumasi Metropolis, Ghana

Benjamin Amoako-Attah, Isaac Kwasi Henyo, Thomas Amoako-Mensah

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: NDWI 1; MNDWI 2; AWEI 3; GIS 4; RS 5; QGIS 5; GPS 6; USGS 7; LULC 8; UTM 9; KAMA.

Online: 10 April 2023 (09:38:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0150.v1

Analysis of the Coupled Assessment of Ecosystem Health and Its Spatial and Temporal Evolution in Fujian China

Delong Cao, Tingyuan Tang, Shuang Wang, Shixian Hu, Sa Gen, Siping Wu, Zhen Lin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Fujian; coupling and coordination mechanism; intensity of land use; extent of ecosystem health; quality of the ecosystem

Online: 10 April 2023 (05:05:20 CEST)

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As the first pilot ecological civilization zone in China, Fujian Province has an excellent ecological environment quality, and its ecological environment-related indices continue to be among the highest in the country. In order to investigate the causes of the excellent ecological quality in Fujian Province in the past 15 years, this paper selects MODIS images, land use data and provincial and municipal statistical yearbook data of Fujian province from 2005 to 2020 as data sources, evaluates the ecological system health of Fujian Province by remote-sensing ecological index (i.e. RSEI), constructs the land use intensity system of Fujian Province at the same time, and uses a coupled coordination model to analyze the coupling between RSEI and The coupled coordination model was used to analyze the coupling relationship between RSEI and land use intensity, and finally the spatial and temporal evolution of ecological health in Fujian Province from 2005 to 2020 was analyzed. The results show that the coupled coordination between land use intensity and ecosystem health in Fujian province showed a continuous increase between 2005 and 2020, especially in Ningde, where the increase was most significant, from 0.0993 to 0.963, this pattern of spatial distribution is lower in the south-eastern coastal region than in the north-western interior, higher in the south-western region than in the north-western region, and higher in the north-eastern region than in the south-eastern region. In terms of the type of role, the land use intensity of most cities in Fujian Province in 2005 played an obvious role in blocking the degree of ecosystem health, but as time changed, urban land use continued to be optimized and reached moderate or even high quality coordination with the quality of the ecological environment, but Xiamen City developed relatively slowly and its 2020 coupling degree value was 0.315, which was still in mild disorder. This study fills a gap in the research mechanism of the interaction between the degree of ecosystem health and land use intensity, and also provides a new perspective for the construction of ecological civilization and the assessment of the degree of ecosystem health in Fujian Province and the whole country.

Preprint ARTICLE | doi:10.20944/preprints202304.0141.v1

Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments

Mohammad Koushafar, Gunho Sohn, Mark Gordon

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: plume rise; deep learning; plume cloud recognition

Online: 10 April 2023 (04:11:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0105.v1

Machine-Learning Based Modelling of Air Temperature in the Complex Environment of Yerevan City, Armenia.

Garegin Tepanosyan, Shushanik Asmaryan, Vahagn Muradyan, Rima Avetisyan, Azatuhi Hovsepyan, Anahit Khlghatyan, Grigor Ayvazyan, Fabio Dell’Acqua

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: urban air temperature; land surface temperature; multiple independent variables; urban heat; remote sensing data; machine learning (ML); ML-driven partial least squares regression (PLSR)

Online: 7 April 2023 (03:34:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0056.v1

Remote Sensing for Water Quality Monitoring – A Study Case for the Marateca Reservoir, Portugal

Cristina Alegria, Teresa Albuquerque

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Trophic level index; spectral indices change; spectral signatures; Random Forest algorithm.

Online: 5 April 2023 (11:02:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0045.v1

Assessment of Urban Heat Island in the Northeastern State Capitals in Brazil Using Sentinel Satellite Data – 3 SLSTR

Rodrigo Fernandes, Antonio Ferreira, Victor Nascimento, Marcos Freitas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Surface Urban heat island; Northeastern region; Sentinel 3; Eco-environmental spaces; Thermal comfort

Online: 4 April 2023 (12:37:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202302.0417.v1

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

Ning You, Libo Han, Yuming Liu, Daming Zhu, Xiaoqing Zuo, Weiwei Song

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: edge detection; wavelet transform modulus maxima; OTSU; complex background; threshold

Online: 24 February 2023 (04:35:48 CET)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202302.0233.v1

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

Piyush S Agram, Michael S Warren, Scott A Arko, Matthew T Calef

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: SAR; RTC; Sentinel-1; GTC; ARD

Online: 14 February 2023 (04:21:05 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0550.v1

Assessing Completeness of OpenStreetMap Building Footprints Using a Gamification Approach in MapSwipe

Tahira Ullah, Sven Lautenbach, Benjamin Herfort, Danijel Schorlemmer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: OpenStreetMap; MapSwipe; data completeness; disaster management; exposure, Volunteered Geographic Information; data quality

Online: 30 January 2023 (09:29:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0381.v1

Simulation of ICESat-2 DEM using Machine Learning Algorithms

Shruti Pancholi, A Abhinav, Ashutosh Bhardwaj

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Cartosat-1; CartoDEM; Digital Elevation Model; DEM interpolation; DEM simulation; Machine Learning; ICESat-2

Online: 23 January 2023 (01:26:40 CET)

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Digital Elevation Model (DEM) is a representation of elevation data that represent terrain with or without overlaying objects of the Earth. It is the ideal and most widely used method for determining topography. DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. To estimate a DEM from ICESat-2 LiDAR point data, machine learning regression algorithms are implemented in this study. The present study has been performed for a plain region of Ghaziabad, Uttar Pradesh, India. Studies have shown that Cartosat-1 DEM V3 R1 product provides an accuracy of the order of 2m in predominantly plain regions, hence taken for this region. Current work focuses on comparing various regression-based machine-learning techniques to interpolate DEM from ICESat-2 data The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested.

Preprint ARTICLE | doi:10.20944/preprints202212.0570.v1

Adaptive Multi-Scale Fusion of Image Deblurring Networks for Drone and Aerial Remote Sensing Object Detection

Baoyu Zhu, Qunbo Lv, Zheng Tan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Drone and Aerial Remote Sensing; Image Deblurring; Generative Adversarial Networks; Multi-Scale; Image blur level; Object Detection; Deep Learning

Online: 30 December 2022 (04:45:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0535.v1

Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model

Utku Demirci, Mehmet Cüneyd Demirel

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: cropland, evapotranspiration; LAI; aspect; remote sensing; mHM

Online: 28 December 2022 (09:19:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0491.v1

Research on Image Denoising in edge detection Based on Wavelet Transform

You Ning, Han LiBo, Zhu Daming, Song Weiwei

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: edge detection; wavelet transform; wavelet basis function; canny; Pratt quality factor

Online: 26 December 2022 (13:29:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0156.v1

Open-source-based Approach to Delineate the Shoreline from Space: A Case Study in Failaka Island, the State of Kuwait

Jasem Albanai

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing (RS); Geographic Information system (GIS); geography; coastal geomorphology; Arabian Gulf

Online: 8 December 2022 (10:03:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0461.v1

Landslide Field Mapping: IFFI-Restart Forms for Data Acquisition and Management for the Classification of Landslides in the Municipalities Affected by the 2016 Central Italy Earthquake

Mauro De Donatis, Giulio Fabrizio Pappafico, Roberta Bonì, Mirko Francioni, Lucia Marino, Stefano Morelli, Vitalii Ivanovych Zatserkovny

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Digital Geological Survey; Mobile GIS; Relational database; Geopackage; Landslide Inventory; Post-disaster management

Online: 25 November 2022 (02:25:21 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0190.v1

Estimation of Above Ground Volume of Mangrove Forest Trees from Terrestrial LiDAR Data using Supervised Machine Learning Algorithms

Yeshwanth Adimoolam, Nithin D. Pillai, Gnanappazham Lakshmanan, Deepak Mishra, Vinay Kumar Dadhwal

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Above-ground biomass; mangroves; pneumatophores; terrestrial LiDAR; machine learning; random forest

Online: 13 October 2022 (08:14:07 CEST)

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Accurately quantifying the above-ground volume (AGV) and thus above-ground biomass (AGB) of forest stands is an important aspect in the conservation of mangrove ecosystem owing to their ecological and economic benefits. However, the number of studies focusing on quantifying mangrove forests’ biomass has been relatively low due to their marshy terrain, making exploratory studies challenging. In recent times, the use of LiDAR technologies in forest inventory studies has become increasingly popular, due to the reliability of LiDAR as a highly accurate means of 3D spatial data acquisition. In this study, we propose an end-to-end methodology for estimating AGV of mangrove forest stands from terrestrial LiDAR data. Many of the recent studies on this topic effectively employ machine learning algorithms such as multi layer perceptron, random forests, etc. for filtering foliage in the point cloud data of single trees. This study further extends that approach by incorporating the impact of class imbalance of forest point cloud data in a weighted random forest classifier. For the task of segmentation of wood/foliage points in a single tree point cloud, this approach yielded an average increase of 2.737% in the balanced accuracy score, 0.007 in the Cohen’s kappa score, 2.745% in the ROC AUC score and 0.857% in the F1 score. For the task of AGV estimation of a single tree, this approach resulted in an average coefficient of determination of 0.93 with respect to the ground truth volumes. For the task of counting pneumatophores in a plot-level point cloud, the proposed breadth-first searching method yielded an average coefficient of determination of 0.9391. Also, the machine learning classifier and geometric features used in this study were invariant to tree species and hence could be generalised for the classification of point clouds of other tree species as well. Finally, a breadth-first graph-search segmentation based approach is also proposed as part of this pipeline to estimate the contribution of pneumatophores to the AGB of mangrove forest stands. Since pneumatophores are a special adaptation of mangrove forests for gaseous exchange in marshy environments, this study aims to incorporate the detection and AGB estimation of pneumatophores in the inventory of mangrove forest stands. Studying the contribution of pneumatophores to the AGB of mangrove forest plots could also aid future mangrove forest inventory studies in modeling the underlying root network and estimating the below-ground biomass of mangrove trees.

Preprint REVIEW | doi:10.20944/preprints202209.0465.v1

Spatial Decision Support Systems with Automated Machine Learning: A Review

Richard Wen, Songnian Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Spatial; Decision Support; Machine Learning; Automation; Framework; System; SDSS; AutoML; GIS

Online: 29 September 2022 (10:06:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0416.v1

Sentinel‐2 Accurately Estimated Wheat Yield in a Semi-arid Region Compared With Landsat‐8

Gaylan R. Faqe Ibrahim, Azad Rasul, Haidi Abdullah

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)

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Preprint ARTICLE | doi:10.20944/preprints202209.0368.v1

Water Stress Index Detection Using a Low-Cost Infrared Sensor and Excess Green Image Processing

Rodrigo Leme de Paulo, Angel Pontin Garcia, Claudio Kiyoshi Umezu, Antonio Pires de Camargo, Fabrício Theodoro Soares, Daniel Albiero

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Water Stress; Precision Irrigation; Non-Water-Stressed Baseline; Soil Moisture; Infra-Red Sensor.

Online: 23 September 2022 (09:29:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0169.v1

Integration of Sentinel 1 and Sentinel 2 Data for Crop Classification Improvement: Barley and Wheat as an Example

Gaylan R. Faqe Ibrahim, Azad Rasul, Haidi Abdullah

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Synthetic Aperture Rader (SAR); Optical image (Sentinel 2); Random Forest (RF); CART; GEE

Online: 13 September 2022 (10:06:14 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202208.0506.v1

A New Data Processing Pipeline for Generating Sea Ice Surface Roughness Products from the Multi-angle Imaging SpectroRadiometer (MISR) Imagery

Ehsan Mosadegh, Anne W. Nolin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: sea ice; surface roughness; remote sensing; MISR

Online: 30 August 2022 (04:44:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0426.v1

Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds

Martin Štroner, Rudolf Urban, Lenka Línková

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Point cloud; Ground filtering; Classification

Online: 25 August 2022 (07:22:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0216.v1

Modelling of Soil Spatial Variability in an Olive Grove by Fusing Remote Drone-based Multispectral Data, Ground-based Hyperspectral and Sample Data with Multivariate Geostatistics Taking into Account Change of Support

Antonella Belmonte, Carmela Riefolo, Lovergine Francesco, Annamaria Castrignanò

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: block cokriging; clay composition; granulometry; multi-collocated cokriging; multi-collocated fac-torial cokriging; regularization; SIDSAM; VIS-NIR-SWIR spectroscopy

Online: 11 August 2022 (11:30:23 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0438.v1

Monitoring Seasonal Morphobathymetric Change of Nearshore Seafloor Using Drone-Based Multispectral Imagery

Evangelos Alevizos, Dimitrios Alexakis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: drones; UAV; bathymetry; shallow water; multispectral; multi-temporal; geomorphology

Online: 28 July 2022 (09:28:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0265.v1

Early Detection of Wildfires with GOES-R Time Series and Deep GRU-Network

Yu Zhao, Yifang Ban

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GOES-R; GRU; Deep Learning; Wildfires; Active Fires; Early Detection; Monitoring

Online: 18 July 2022 (10:28:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0071.v1

Development of Global Impervious Surface Area Index for Automatic Spatiotemporal Urban Mapping

Akib Javed, Zhengfeng Shao, Iffat Ara, Md. Enamul Huq, Md. Yeamin Ali, Nayyer Saleem, Muhammad Nasar Ahmad

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)

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Preprint ARTICLE | doi:10.20944/preprints202206.0013.v1

A Neural Network based Fusion Approach for Improvement of SAR Interferometry based Digital Elevation Models in Plain and hilly terrain of India

Priti Girohi, Ashutosh Bhardwaj

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: SAR Interferometry (InSAR); Digital Elevation Models (DEM); Neural Networks; DEM Fusion; ICESat-2 spaceborne altimetry

Online: 1 June 2022 (10:11:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0054.v2

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Maciej Adamiak, Krzysztof Będkowski, Adam Bielecki

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: generative adversarial networks; NDVI; green areas; orthophoto; artificial datasets.

Online: 19 April 2022 (10:11:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0007.v2

An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing

Yongzhu Xiong, Xiaofeng Zeng, Yankui Chen, Jiawen Liao, Weiqian Lai, Mingyong Zhu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: individual fruit tree (IFT); individual pomelo tree (IPT) detection; deep learning; transfer learning; YOLOv5; remote sensing; unmanned aerial vehicle (UAV); spatial distribution

Online: 4 April 2022 (13:35:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202201.0123.v2

Genus-Physiognomy-Ecosystem Map with Solar Panels Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

Ram C. Sharma

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-2; Land cover; Vegetation; Mapping; Plant communities; Machine learning; Genus-Physiognomy-Ecosystem; Gradient Boosting Decision Trees; Solar panel; Vegetation disturbance

Online: 4 April 2022 (10:40:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202204.0007.v1

An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing

Yongzhu Xiong, Xiaofeng Zeng, Yankui Chen, Jiawen Liao, Weiqian Lai, Mingyong Zhu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: individual fruit tree (IFT); individual pomelo tree (IPT) detection; deep learning; transfer learning; YOLOv5; remote sensing; unmanned aerial vehicle (UAV); spatial distribution

Online: 1 April 2022 (12:34:02 CEST)

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The location and number data of individual fruit trees is critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for obtaining the data rapidly and reducing errors of human operations. This study aims to propose an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing was used to collect high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision (AP@0.5) of the four YOLOv5 models for validation reaches 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was predicted and counted using YOLOv5x and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study is desired to provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.

Preprint ARTICLE | doi:10.20944/preprints202203.0337.v1

Stacking Ensemble of Machine Learning Methods for Landslide Susceptibility Mapping in Zhangjiajie City, Hunan Province, China

Yuke Huan, Lei Song, Umair Khan, Baoyi Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: landslide susceptibility; stacking ensemble; machine learning; random forest; gradient boosting decision tree; extreme gradient boosting

Online: 25 March 2022 (03:43:32 CET)

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The current study aims to apply and compare the performance of six machine learning algorithms, including three basic classifiers: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGB), as well as their hybrid classifiers, using the logistic regression (LR) method (RF+LR, GBDT+LR, and XGB+LR), in order to map the landslide susceptibility of Zhangjiajie City, Hunan Province, China. First, a landslide inventory map was created with 206 historical landslide points and 412 non-landslide points, which was randomly divided into two datasets for model training (80%) and model testing (20%). Second, 15 landslide conditioning factors (i.e., altitude, slope, aspect, plane curvature, profile curvature, relief, roughness, rainfall, topographic wetness index (TWI), normalized difference vegetative index (NDVI), distance to roads, distance to rivers, land use/land cover (LULC), soil texture, and lithology) were initially selected to establish a landslide factor database. Thereafter, the multicollinearity test and information gain ratio (IGR) technique were applied to rank the importance of the factors. Subsequently, we used a series of metrics (e.g., accuracy, precision, recall, f-measure, area under the ROC (receiver operating characteristic) curve (AUC), kappa index, mean absolute error (MAE), and root mean square error (RMSE)) to evaluate the accuracy and performance of the six models. Based on the AUC values derived from the models, the GBDT+LR model with the highest AUC value (0.8168) was identified as the most efficient model for mapping landslide susceptibility, followed by the XGB+LR, XGB, RF+LR, GBDT, and RF models, which achieved AUC values of 0.8124, 0.8118, 0.8060, 0.7927, and 0.7883, respectively. The results from this study suggest that the stacking ensemble machine learning method is promising for use in landslide susceptibility mapping in the Zhangjiajie area and is capable of targeting the areas prone to landslides.

Preprint ARTICLE | doi:10.20944/preprints202202.0250.v1

Natural Disaster Identification and Mapping of Tsunami and Earthquake in Indonesia Using Satellite Imagery Analysis (Case Study: Aceh, Palu, and Yogyakarta)

M. Rifqi Jundullah, Arie Wahyu Wijayanto

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: satellite images; disaster mapping; GIS

Online: 21 February 2022 (10:09:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202202.0054.v1

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

Maciej Adamiak, Krzysztof Będkowski, Adam Bielecki

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: generative adversarial networks; remote sensing; NDVI; green areas; orthophoto; artificial datasets.

Online: 3 February 2022 (15:11:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0431.v1

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

Liu Jianhua, Feng Guoqiang, Luo Jingyan, Wen Danqi, Chen Zheng, Wang Nan, Zeng Baoshan, Wang Xiaoyi, Li Xinyue, Gu Botong

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: cell phone indoor positioning; scene recognition; building map; map location anchor; YOLOv5; geocoding matching

Online: 28 January 2022 (08:55:08 CET)

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At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

Preprint ARTICLE | doi:10.20944/preprints202201.0417.v1

Space-based Deformation Monitoring of Coastal Urban Areas: The Case of Limassol’s Coastal Front

Kyriaki Fotiou, Dimitris Kakoullis, Marina Pekri, George Melillos, Ramon Brcic, Michael Eineder, Diofantos G. Hadjimitsis, Chris Danezis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deformation Monitoring; Land Subsidence; Coastal Areas; PSI; SAR; Cyprus

Online: 27 January 2022 (11:30:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0352.v1

Pixel Level Classification Confidence for Remote Sensing Imagery: An Evaluation of Three Interpolation Based Methods

Shiguo Jiang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Per-pixel classification confidence; spatial pattern; image classification; accuracy assessment; interpolation method

Online: 24 January 2022 (11:53:46 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0202.v1

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Herman Snevajs, Karel Charvat, Vincent Onckelet, Jiri Kvapil, Frantisek Zadrazil, Hana Kubickova, Jana Seidlova, Iva Bartlova

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: crop detection; Sentinel 1; Sentinel 2; supervised classification; unsupervised classification; time series; agriculture; food security

Online: 14 January 2022 (11:18:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202201.0123.v1

Genus-Physiognomy-Ecosystem Map with 88 Legends Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

Ram C. Sharma, Keitarou Hara

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-2, Land cover, Vegetation, Mapping, Plant communities, Machine learning, Genus-Physiognomy-Ecosystem, Gradient Boosting Decision Trees

Online: 10 January 2022 (15:02:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0443.v1

Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region

Ram C. Sharma, Hidetake Hirayama, Masatsugu Yasuda, Miki Asai, Keitarou Hara

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-2; Land cover; Vegetation; Mapping; Plant communities; Machine learning; Genus-Physiognomy-Ecosystem; Gradient Boosting Decision Trees

Online: 28 December 2021 (10:49:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202112.0077.v1

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Grayson R. Morgan, Cuizhen Wang, Zhenlong Li, Steven R. Schill, Daniel R. Morgan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deep Learning; Machine Learning; Change Detection; Coastal; Marsh; Remote Sensing; Aerial Imagery

Online: 6 December 2021 (13:31:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202108.0181.v1

Remote Sensing of Aerosols at Night with the CoSQM Sky Brightness Data

Charles Marseille, Martin Aubé, Africa Barreto Velasco, Alexandre Simoneau

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Artificial Light at Night; Aerosol optical depth; Radiometry; Multispectral; Measurement

Online: 9 August 2021 (08:40:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202107.0257.v1

Remote Sensing by using Unsupervised Algorithm

Afshan Saleem

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyperspectral images, unsupervised Algorithm, clustering,K-means algorithm, spectral signature.

Online: 12 July 2021 (12:14:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202107.0100.v1

Investigation of June 2020 Giant Saharan Dust Storm Using Remote Sensing Observations and Model Reanalysis

A. Asutosh, V. Vinoj, M. Nuncio

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Dust storm; Aerosols; Satellite remote sensing; Radiative forcing; Thermodynamics

Online: 5 July 2021 (13:16:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202107.0046.v1

Combining Remote Sensing Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Johannes H. Uhl, Stefan Leyk, Zekun Li, Weiwei Duan, Basel Shbita, Yao-Yi Chiang, Craig A. Knoblock

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: urbanization; long-term settlement patterns; built-up land data; global human settlement layer; historical maps; topographic map processing; data integration.

Online: 2 July 2021 (10:03:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202106.0706.v1

Hyperspectral Image Classification Using Unsupervised Learning Algorithms

Wafa Fatima, Iqra Ejaz

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyperspectral Images, Classification, K means, Spectral Matching, Abundance Estimation

Online: 29 June 2021 (12:56:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202105.0199.v1

Temporal Change in Relationships Between Urban Structure and Surface Temperature

Justin Stewart, Peleg Kremer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: urban structure, remote sensing, temporal change, NYC

Online: 10 May 2021 (14:26:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202103.0407.v1

Combination of Geostatistical and Geovisualisation Techniques for Analysing 120 Year Earthquake Events in Indonesia Using Open-Source Software

Fatwa Ramdani, Vivid Chairunnisa

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: geostatistics; geovisualisation; rshiny; earthquake; Indonesia

Online: 16 March 2021 (09:30:11 CET)

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Working Paper ARTICLE

Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

Martin Štroner, Rudolf Urban, Jan Seidl, Tomáš Reindl, Josef Brouček

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Drone; GNSS RTK; UAV; photogrammetry; precision; accuracy; elevation

Online: 11 March 2021 (11:49:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202102.0516.v1

Detecting the Attack of the Fall Armyworm (Spodoptera Frugiperda) in Cotton Plants with Machine Learning and Spectral Measurements

Felipe David Georges Gomes, Mayara Maezano Faita Pinheiro, Danielle Ellis Garcia Furuya, Wesley Nunes Gonçalves, José Marcato Júnior, Mirian Fernandes Furtado Michereff, Maria Carolina Blassioli-Moraes, Miguel Borges, Raúl Alberto Alaumann, Veraldo Liesenberg, Lúcio André de Castro Jorge, Ana Paula Marques Ramos, Lucas Prado Osco

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: machine learning; insect-damage; spectral data; theoretical model

Online: 23 February 2021 (14:12:28 CET)

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In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

Preprint ARTICLE | doi:10.20944/preprints202102.0338.v1

Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region

Ram C. Sharma

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)

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Preprint ARTICLE | doi:10.20944/preprints202102.0251.v1

Evaluation Model of Remote Sensing Satellites Cooperative Observation Capability

Zhonggang Zheng, Kun Fu, Qingmei Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; collaborative application; observation capability; evaluation

Online: 10 February 2021 (10:27:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202012.0721.v1

A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

Yue Ma, Guoqing Li, Xiaochuang Yao, Jin Ben, Qianqian Cao, Long Zhao, Lianchong Zhang, Rui Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing; Global discrete grid; Accuracy evaluation; Hexagon grid

Online: 29 December 2020 (09:19:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202011.0287.v1

Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

BENCHELHA MOHAMED, Benzha Fatiha, Rhinane Hassan, BENCHELHA SAID, BENCHELHA TAOUFIK, BENCHELHA NAJAT, Bezza Mohamed

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Urban growth; cellular automata; Benslimane; GIS; Landsat

Online: 9 November 2020 (22:56:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202009.0574.v1

Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Juan Carlos Laso Bayas, Linda See, Hedwig Bartl, Tobias Sturn, Mathias Karner, Dilek Fraisl, Inian Moorthy, Michaela Busch, Marijn Van Der Velde, Steffen Fritz

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land cover; land use; citizen science; mobile apps; in-situ data collection; LUCAS

Online: 24 September 2020 (08:26:29 CEST)

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There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

Preprint ARTICLE | doi:10.20944/preprints202009.0512.v1

Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps

Satej Soman, Anni Beukes, Cooper Nederhood, Nicholas Marchio, Luís M.A. Bettencourt

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: OpenStreetMap; cities; slums; network analysis; remote sensing; human development; urban planning; GIS; cloud computing

Online: 22 September 2020 (08:58:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0034.v1

Validation of Airborne Sensor Photogrammetric Digital Terrain and Global Digital Elevation Models around Mekelle City, Ethiopia

Hareya Biryhane, Tulu B. Bedada, Berhan Gessesse, Martin Vermeer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: vertical accuracy; photogrammetric DTM; ASTER; SRTM; TanDEM-X; orthometric height; geoid height

Online: 2 September 2020 (08:30:48 CEST)

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The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

Working Paper ARTICLE

Potential and Limitations of Satellite Altimetry Constellations for Monitoring Surface Water Storage Changes – A Case Study in the Mississippi Basin

Denise Dettmering, Laura Ellenbeck, Daniel Scherer, Christian Schwatke, Christoph Niemann

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: satellite altimetry; terrestrial water storage; Mississippi basin; SWOT

Online: 27 August 2020 (11:54:43 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202008.0535.v1

ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data

Kathryn Elmer, Raymond Soffer, J. Pablo Arroyo-Mora, Margaret Kalacska

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: spectral processing; reflectance; spectrometer; spectroradiometer

Online: 25 August 2020 (03:37:57 CEST)

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Working Paper ARTICLE

Deformations Prior to the Brumadinho Dam Collapse Revealed by Sentinel-1 InSAR Data Using SBAS and PSI Techniques

Fábio Furlan Gama, José Claudio Mura, Waldir Renato Paradella, Cleber Gonzales de Oliveira

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Brumadinho; Tailings dam; SBAS; PSI; Sentinel-1B

Online: 18 August 2020 (16:21:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0293.v1

PhotonLabeler: An Inter-disciplinary Platform for Visual Interpretation and Labeling of ICESat-2 Geolocated Photon Data

Lonesome Malambo, Sorin C. Popescu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ICESat-2; photon-counting lidar; photon labeling; visualization; ATL03; ATL08; visual interpretation; solar-induced noise

Online: 13 August 2020 (08:08:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202008.0271.v1

Soil Loss and Sediment Export from Land Use of the George Town Conurbation Catchment

Zullyadini A. Rahaman, Mohamad Adam Omar, Narimah Samat, and Mohd Amirul Mahamud

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: geographic information system; land demand; land use; universal soil loss erosion

Online: 12 August 2020 (05:09:55 CEST)

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Environmental and Earth Sciences