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Preprint ARTICLE | doi:10.20944/preprints202410.0348.v1

Spatio-Temporal Analysis of Urban Heat Island (UHI) Intensity in Mbombela City, Mpumalanga Province from 2008 to 2023

Njabulo Quentin Magagula

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UHI; Land Surface Temperature (LST); Spatio-Temporal Analysis; Urbanization

Online: 4 October 2024 (14:53:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0344.v1

Comprehensive Representations of Subpixel Land Use and Cover Shares by Fusing Multiple Geospatial Datasets and Statistical Data with Machine-Learning Methods

Yuxuan Chen, Rongping Li, Yuwei Tu, Xiaochen Lu, Guangsheng Chen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Fractional land cover share; machine-learning method; the northeastern China; land use and cover change (LUCC); NDVI

Online: 4 October 2024 (14:03:04 CEST)

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Land use and cover change (LUCC) is a key factor influencing global environmental and socio-economic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products can not accurately reflect the spatiotemporal change patterns of LUCC at regional scale in China. Based on these geospatial LUCC products, Normalized Difference Vegetation Index (NDVI), socioeconomic data, and statistical data, we developed multiple procedures to represent both spatial and temporal changes of the major LUC types by applying machine-learning, regular decision tree and hierarchical assignment methods using the northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in the NEC, with an overall accuracy of 0.82, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated our approach and dataset can more accurately and comprehensively represent the spatiotemporal patterns of all LUC types at subpixel level. Our approach unfolded the mixed pixel issue and integrated the strengths of all LUCC products through the fusion process. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11×104 km2, 15.19×104 km2 and 2.85×104 km2, respectively during 1980-2020, while grassland, wetland, shrubland and bareland decreased by 26.06×104 km2, 4.24×104 km2, 3.97×104 km2, and 0.92×104 km2, respectively. The temporal change patterns of all these LUC types were consistent with the provincial inventory data. Our developed approach can be widely applied in the entire China and worldwide, and our data products can provide accurate data supports for studying the LUCC consequences and making effective land use policies.

Preprint ARTICLE | doi:10.20944/preprints202410.0261.v1

A Comprehensive Approach for Mapping Ecological System Types Using Sentinel‐2 Satellite Imagery

Michael Sunde, David Diamond, Lee Elliott

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ecosystems; mapping; land‐cover; remote sensing; sentinel‐2; machine learning; image classification; vegetation

Online: 3 October 2024 (12:12:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0230.v1

Saguaro Recognition from Drone Imagery Using Mask R-CNN in Detectron2

Wenting Luo, Breeze Scott, Jacky Cadogan, Truman P. Combs, Don E Swann, Kamel Didan, Haiquan Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Saguaro cactus; population monitoring; image recognition; drone imagery; Mask R-CNN; Detectron2

Online: 3 October 2024 (11:23:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0172.v1

Spatio-Temporal Dynamics of Urban Green Spaces and Climatic Vulnerability of Togolese Cities in the Context of Rapid Urbanisation: The Case of Lome and Kara

Yetondé Deton, Hodabalo Kamou, Abalo Atato, Bimare Kombate, Abalo Mabafei, Josef YOKA, Marra Dourma, Atsu Kudzo Guelly, Komlan Batawila, Koffi Akpagana

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Uban Green Spaces; dynamiques spatio-temporelles; vulné; rabilité; climatique; Lomé; ; Kara (Togo)

Online: 2 October 2024 (13:00:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0171.v1

Land Cover Change Detection with Optical and SAR Satellite Data after Tropical Cyclone Seroja - Case Study in Dili, Timor Leste

Pedro Junior Fernandes, Masahiko Nagai

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land cover change; change detection; disaster monitoring; Random Forest classifier; Otsu Thresholding; SAR data; optical imagery; Timor-Leste

Online: 2 October 2024 (12:59:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0134.v1

High-Resolution Monitoring of Deforestation and Carbon Sequestration Using Satellite Data in Costa Rica

Andrea Navarro

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deforestation; ; Carbon Sequestration; ; Remote Sensing; ; Satellite Monitoring; ; Tropical Forests

Online: 2 October 2024 (11:12:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0032.v1

Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

Damianos Mantsis, Anastasia Moumtzidou, Ioannis Lioumbas, Ilias Gialampoukidis, Aikaterini Christodoulou, Alexandros Mentes, Stefanos Vrochidis, Ioannis Kompatsiaris

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-1 satellite; atmospheric corrections; oil spill; pollution over an inland lake

Online: 2 October 2024 (10:25:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0071.v1

High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction

Facheng Li, Qiming Zeng

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: SAR geolocation; tropospheric correction; TerraSAR-X; Tianhui-2

Online: 1 October 2024 (14:21:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0009.v1

Forest Fire Discrimination Based on Angle Slope Index and Himawari-8

Pingbo Liu, Gui Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Forest fire discrimination; Angle slope index; Himawari-8; decomposed three-dimensional OTSU

Online: 1 October 2024 (11:55:15 CEST)

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In the context of high frequency and intensity forest fires driven by future warming and drying climate, early detection and effective control of fires are extremely important to reduce losses. Satellite remote sensing, characterized by its broad observational scope and strong repeat observation capability, has become an important method for rapid monitoring of forest fires, replacing traditional means such as manual measurement and aerial photography. This paper addresses the misjudgments caused by solely relying on changes in infrared band brightness values and single-band separation in forest fire discrimination. It constructs the angle slope index ANIR based on the red band, near-infrared band and short-wave infrared band, Angle slope index AMIR based on the short-wave infrared band, mid-infrared band and far-infrared band, and angle slope index AMNIR based on the difference between the AMIR and ANIR, integrating the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. To address the omissions caused by single-image fire discrimination algorithms, the paper constructs time-series Angle slope difference indices ∆ANIR and ∆AMIR. To address the different fire point discrimination thresholds caused by background difference in different time and space, the paper uses the decomposed three-dimensional OTSU(Maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data. The methods were validated using high temporal resolution meteorological satellite Himawari-8 and ground truth fire data from Hunan Province, China, from 2018-2019. The results show that the discrimination method based on the ASITR(Angle slope indices thresholds) method exhibits an accuracy of 0.88, a missed detection rate of 0.17, and an overall evaluation value of 0.85, which is 0.09 higher in accuracy and 0.03 higher in overall evaluation than the discrimination method based on infrared bands brightness values proposed by Feng et al. [1] (accuracy 0.79, missed detection rate 0.14, overall evaluation value 0.82); the method based on the FAMN_OTSU_ASITR (Fusion of Angle slope difference index (false) data, Decomposed three-dimensional OTSU adaptive threshold segmentation Algorithm and ASITR) exhibits an accuracy of 0.85, a missed detection rate of 0.08, and an overall evaluation value of 0.88, which is 0.09 lower in missed detection rate and 0.03 higher in overall evaluation as compared to the ASITR. The results demonstrate that the forest fire discrimination method based on Angle slope indices, Himawari-8 satellite data, and the decomposed OTSU adaptive threshold segmentation algorithm can improve the accuracy of forest fire discrimination and reduce errors in missed detections.

Preprint DATA DESCRIPTOR | doi:10.20944/preprints202409.2161.v1

Two datasets over South Tyrol and Tyrol areas to understand and characterize water resource dynamics in mountain regions

Ludovica De Gregorio, Giovanni Cuozzo, Riccardo Barella, Francisco Corvalán, Felix Greifeneder, Peter Grosse, Abraham Mejia-Aguilar, Georg Niedrist, Valentina Premier, Paul Schattan, Alessandro Zandonai, and Claudia Notarnicola

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; hydrology; snow cover fraction; soil moisture

Online: 27 September 2024 (11:39:26 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2048.v1

Two-Dimensional Directions Determination for GNSS Spoofing Source Based on MEMS-Based Dual-GNSS/INS Integration

Chengzhong Zhang, Dingjie Wang, Jie Wu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GNSS spoofing interference; MIMU; integrated navigation; spoofing signal source; integer ambiguity; common-clock receiver

Online: 26 September 2024 (09:53:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1994.v1

Quantification of Soil Water Erosion Using the RUSLE Method in the Mékrou Watershed (Middle Niger River)

Rachid Attoubounou Abdourahamane, Hamidou Diawara, Ralf Ludwig, Julien Adounkpe

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: water erosion; remote sensing; Mé; krou watershed; middle Niger river; West Africa

Online: 25 September 2024 (11:47:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1781.v1

Detecting trends in post-fire forest recovery in Middle Volga from 2000 to 2023

Eldar Kurbanov, Ludmila Tarasova, Aydin Yakhyayev, Oleg Vorobev, Siyavush Gozalov, Sergei Lezhnin, Jinliang Wang, Jinming Sha, Denis Dergunov, Anna Yastrebova

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest ecosystems; wildfire; burnt area; post-fire forest recovery; Landsat; time-series; GEE; LandTrendr; trend analyses; climatic factors

Online: 24 September 2024 (05:14:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1717.v1

Shifting from Trade-Offs to Synergies in Ecosystem Services Through Effective Ecosystem Management in Arid Areas

Yan Xu, Xiaoyun Song, Mingjiang Deng, Tao Bai, Wanghai Tao

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Ecosystem services; spatial-temporal pattern; arid area; trade-offs and synergies; Central Asia

Online: 24 September 2024 (04:06:03 CEST)

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Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation and the intensity of land development decreased, only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000-2010 and 2010-2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for Comprehensive ES （CES） functions. By considering the trade-offs/synergies relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia.

Preprint ARTICLE | doi:10.20944/preprints202409.1774.v1

Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application

Kenneth Okechukwu Ekpetere, Amita V. Mehta, James Matthew Coll, Chen Liang, Sandra Ogugua Onochie, Michael Chinedu Ekpetere

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: IPE; IMERG; Rainfall anomaly index; climate change; rainfall intensity; rainfall frequencies; rainfall storm; web application; NOAA; CONUS

Online: 23 September 2024 (14:07:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1741.v1

Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data

Temitope Omoniyi, Allan Sims

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: National Forest Inventory; Growing stock volume; Remote sensing; Sentinel-2; airborne laser scanning; Machine Learning; random forest; support vector regression; extreme gradient boosting

Online: 23 September 2024 (13:00:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1679.v1

Adsorption and Sensing Properties of Ni Modified InSe Monolayer Towards Toxic Gases: A DFT Study

Jianhong Dong, Xiaoqian Qiu, Shuying Huang, Shaomin Lin, Lisha Liu, Huihui Xiong

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: doped InSe monolayer; gas sensor; DFT method; adsorption mechanism

Online: 23 September 2024 (11:45:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1603.v1

Use of Vegetation Activity Index for Evaluation of L-Alpha Amino Acid Treatment in Sugarcane

Américo Ferraz Dias Neto, Ivan Bazo Bergamim, Flavio Roberto de Freitas Gonçalves, Raffaella Rossetto, Daniel Albiero

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sugarcane; Biostimulant; Vegetation activity index; Crop assessment; Sustainable agriculture; Agricultural management

Online: 22 September 2024 (06:33:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1766.v1

Recurrent Convolutional Neural Networks Applied to Short-Term Weather Forecasting by Radar Images

Felipe Copceski Rossatto, Fabrício Harter, Elcio Shiguemori, Leonardo Calvetti

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Neural Network; Nowcasting; Radar

Online: 19 September 2024 (03:53:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1417.v1

Rehabilitated Coal Mine Tailings as Green Cooling Islands in the Metropolitan Ruhr Area (Germany)

Britta Stumpe, Bernd Marschner

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: coal mine tailings; urban green spaces; urban heat island; mitigation; cooling islands; land surface temperature; vegetation indices; Landsat 8

Online: 19 September 2024 (03:41:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1405.v1

Estimation Network for Multiple Chemical Parameters of Astragalus Leaves Based on Attention Mechanism and Multivariate Hyperspectral Features

Yulong Zhou, Zhiyun Xiao, Tengfei Bao, Tao Fang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Astragalus membranaceus var. mongholicus; Chemical parameters; Hyperspectral estimation; Attention mechanism; Deep network

Online: 18 September 2024 (12:47:49 CEST)

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In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

Preprint ARTICLE | doi:10.20944/preprints202409.1355.v1

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Alessio Biondi, Luca Facheris, Fabrizio Argenti, Fabrizio Cuccoli

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Weather radar; rain gauge; quantitative precipitation estimation (QPE); rainfall; data fusion; merging methods

Online: 18 September 2024 (04:55:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1321.v1

Investigating the Performance of Open Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments

Kauê de Moraes Vestena, Silvana Phillipi Camboim, Maria Antonia Brovelli, Daniel Rodrigues dos Santos

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Open-Vocabulary Algorithms; Pavement Segmentation; Surface Material Detection; Street-View Imagery; Urban Mobility

Online: 17 September 2024 (12:34:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0882.v2

A Channel State Information and Geomagnetic Fused Fingerprint Localization Algorithm Based on Multi-Input CNN

Zhenhao Cheng, Dongqing Zhao, Wenzhuo Guo, Luguang Lai, Linyang Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: 5G channel state information; low-cost magnetometer; multi-input convolutional neural network; fingerprint localization; heterogeneous fusion

Online: 17 September 2024 (12:01:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1211.v1

Towards Sustainable Forest Management: Assessing and Predicting Landuse/cover Change on Forest Landscape and Estimating its Impact on Forest Health in Southeast Georgia, USA

Michael Batame, Kingsley Kanjin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sustainable Forest Management; LULC; NDVI; Landsat Satellite; Google Earth Engine

Online: 17 September 2024 (06:12:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1176.v1

Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data

Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Spatial LiDAR; GEDI; ICESat-2; canopy height; modelling; data combinations; forest-savannah mosaics

Online: 16 September 2024 (10:03:51 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1107.v1

Comparative Study of Random Forest and SVM for Land Cover Classification and Post-Wildfire Change Detection

Tan Yan Cheng, Lia Duarte, Ana Cláudia Teodoro

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Machine Learning; Land cover classification; Object-based image analysis; Pixel-based image analysis; Random Forest; Support Vector Machine

Online: 14 September 2024 (07:38:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1041.v1

Marine Oil Pollution Monitoring Based on Morphological Attention U-Net Using SAR Images

Lena Chang, Yi-Ting Chen, Ching-Min Cheng, Yang-Lang Chang, Shang-Chih Ma

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: marine oil pollution; Synthetic Aperture Radar (SAR); U-Net model; convolutional block attention module (CBAM); Automatic Identification System (AIS)

Online: 13 September 2024 (08:23:46 CEST)

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With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

Preprint ARTICLE | doi:10.20944/preprints202409.0970.v1

Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction

Akshay Dagadu Yewle, Oktay Karakus

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Crop Yield; Rice Crop; Multi-modal Remote Sensing; Machine Learning; Ensemble Learning; Deep Ensemble Model

Online: 12 September 2024 (15:50:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.1033.v1

Improved Urban Impervious Surface Mapping Using Multi-Sensor Feature Fusion: A Cross-City Analysis

Muhammad Nasar Ahmad, Fahad Almutlaq, Md. Enamul Huq, George D. Bathrellos, Akib Javed, Fakhrul Islam, Hariklia D. Skilodimou

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Data Fusion; Impervious surface; Landsat 8; LST; Sentinel-1

Online: 12 September 2024 (15:48:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0913.v1

Dust Monitoring and Three-Dimensional Transport Characteristics of Dust Aerosol in Beijing, Tianjin, and Hebei

Siqin Zhang, Jianjun Wu, Jiaqi Yao, Xuefeng Quan, Haoran Zhai, Qingkai Lu, Haobin Xia, Mengran Wang, Jinquan Guo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: dust aerosols; dust identification index; FY-4A; CALIPSO; HYSPLIT; Three-dimensional distribution

Online: 11 September 2024 (15:25:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0905.v1

Pan-Arctic Lead Maps based on Sentinel-1 SAR Images with a Convolutional Neural Network

Dmitrii Murashkin, Gunnar Spreen, Marcus Huntemann

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: sea ice; leads; SAR; Sentinel-1; U-Net; CNN

Online: 11 September 2024 (12:31:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0809.v1

A Procedure for Analyzing Vertical Ground Deformation Anomalies in Active Volcanic Caldera during Unrest Phases

Fabio Matano, Annarita Casaburi, Giuseppe De Natale

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: caldera unrest; ground deformation; volcanic hazard; MT-InSAR; Sentinel-1

Online: 11 September 2024 (02:51:33 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202409.0770.v1

Pre-Detection and Pre-Registration Averaging of Full Wave Signals in Airborne LiDAR Bathymetry

Roland Schwarz, Martin Pfennigbauer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LiDAR; Bathymetry; Waveform Stacking; Waveform Averaging

Online: 10 September 2024 (17:57:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0702.v1

Deep Learning Approaches for Wildfire Severity Prediction: A Comparative Study of Image Segmentation Networks and Visual Transformers on the EO4WildFires Dataset

Dimitris Sykas, Dimitrios Zografakis, Konstantinos Demestichas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest wildfires; earth observation; remote sensing; machine learning; deep learning; training dataset; multi-sensor data

Online: 9 September 2024 (16:11:36 CEST)

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This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1 and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. Resulting in a coverage of 45 countries (Figure \ref{Figure1-events}) with a total of 31730 wildfire events from 2018 to 2022. All these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test different deep learning architectures’ effectiveness in predicting the size and shape of wildfire burned areas. The study incorporates both Image Segmentation networks and Visual Transformers, employing a consistent experimental design across various models to ensure comparability of results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 Score, IoU Score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing with Image Segmentation networks and Visual Transformers, the research not only aims at enhancing the accuracy of estimating the burned area but also underscores the significance of quality training data and the comparative effectiveness of traditional segmentation methods over transformer-based models for this specific application. The dataset is published with an Open Access license and is hosted at: \url{10.5281/zenodo.7762564}.

Preprint ARTICLE | doi:10.20944/preprints202409.0674.v1

Exploring Multisource Remote Sensing for Assessing and Monitoring the Ecological State of the Mountainous Natural Grasslands in Armenia

Grigor Ayvazyan, Vahagn Muradyan, Andrey Medvedev, Anahit Khlghatyan, Shushanik Asmaryan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Multisource remote sensing; mountainous grasslands; UAV data; trend analysis; short-term data sets; vegetation indices; Armenia

Online: 9 September 2024 (13:48:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0526.v1

Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua

Victor Arcia Castro, Gerald Corzo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: data-driven modelling; machine learning; landslide; remote sensing; Logistic Regression; Random Forest; Support Vector Classifier; Ometepe; Nicaragua

Online: 6 September 2024 (13:04:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0487.v1

A Likelihood-based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Mohamed Ali GHANNAMI, Sylvie DANIEL, Guillaume SICOT, Isabelle QUIDU

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Bathymetric charting; Coastal areas; Through-water photogrammetry; Likelihood-based inference; Water Column Depth (WCD); Water Air Interface (WAI) height; Uncertainty modeling; Stereo-triangulation

Online: 6 September 2024 (12:04:07 CEST)

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Coastal environments, of major economic, social, and strategic importance, rely heavily on bathymetry for navigation safety, harbor development, and resource monitoring. Innovations in bathymetry include optical alternatives for shallower waters, as traditional acoustic solutions are less efficient. Recent studies demonstrated airborne imagery is an efficient option to frequently map shallow waters at synoptic scales and has the capacity to retrieve the Water Column Depth (WCD). Geometric approaches allow to obtain 3D reconstruction from images by triangulating a set of pixels that are located on more than one image (feature points). They demonstrate great potential for WCD assessment, achieving a penetration depth of over 5 meters with an accuracy in the decimeter range when creating high resolution maps of clear shallow water bodies as long as there is sufficient bottom texture with identifiable features. As the navigation safety of vessels relies on WCD accuracy, it is of utmost importance to quantify the uncertainty associated with the derived product. Studies on bathymetry estimation using geometric approaches tend to focus on the estimation accuracy, rather than the assessment of uncertainty, despite the fact that there are many sources of uncertainty. As a result, here is a lack of robust uncertainty modeling methods, crucial for assessing the quality of bathymetric charts, especially in shallow waters. In response, this research introduces a novel likelihood-based approach for through-water photogrammetry with a focus on the uncertainties associated with camera pose, a key factor affecting through-water depth mapping. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation. In addition to providing a realistic estimate of the uncertainty, this approach allows to handle complex cases involving noisy camera poses in addition to retrieving the water interface height. The results proposed in this paper reveal the significant influence of viewing geometry and camera pose quality on the resulting uncertainties, overshadowing the impact of depth. In addition, they demonstrate the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Also, we highlight the transformative potential of drone-based applications in underwater surveying, leveraging high-precision and efficient data collection to contribute to more accurate bathymetric charts.

Preprint ARTICLE | doi:10.20944/preprints202409.0423.v1

How Urban Is World Heritage? An Investigation of Built-up Area Increase in Cultural Heritage Properties

Moses Katontoka, Francesco Noardo, Daniela Palacios-Lopez, Thomas Esch, Nourian Pirouz, Fulong Chen, Ana Pereira Roders

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: World Heritage; urban built-up; World Settlement Footprint Ev; land-cover; UNESCO

Online: 5 September 2024 (10:40:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0333.v1

Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India

Anamika Anand, Ryoichi Imasu, Surendra Kumar Dhaka, Prabir Patra

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: burn areas; crop residue burning; deep learning; neural networks; transfer learning

Online: 4 September 2024 (16:08:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0129.v1

Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives

Carolien Toté, Else Swinnen, Claire Henocq

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sentinel-3; SPOT/VEGETATION; PROBA-V; surface reflectance; NDVI; long term data record; consistency

Online: 3 September 2024 (03:03:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0017.v1

Conservation, Evolution and Restauration Analysis of the Vegetation and the Beach Dune System of Enebrales Beach (Huelva, Spain)

Laura del Valle, Guillem X. Pons

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GIS; Enebrales beach; orthophotography; vegetation evolution; beach-dune system evolution; Huelva

Online: 2 September 2024 (13:24:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.0072.v1

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (DR Congo), 1990-2024: Integrating Remote Sensing and Landscape Ecology Techniques

Yannick Useni Sikuzani, Médard Mpanda Mukenza, John Kikuni Tchowa, Delphin Kabamb Kanyimb, François Malaisse, Jan Bogaert

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deforestation; Biodiversity conservation; Anthropogenic pressures; Remote sensing; Landscape analysis; Protected areas

Online: 2 September 2024 (10:58:51 CEST)

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Lualaba Province, located in the southeastern Democratic Republic of the Congo, consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The unique miombo woodlands of Lualaba are threatened by deforestation, posing risks to biodiversity and local livelihoods that depend on these woodlands for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba's landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. Our provincial-level analysis revealed a pronounced deforestation trend, with miombo woodland cover declining from 62,90 % to less than 25 % over the 34-year period. This trend was characterized by the fragmentation and dissection of woodland patches and a decline in remaining woodland fragments, due to their ongoing replacement by savannas, agriculture, and urbanization. The average distance between miombo woodland patches increased significantly, indicating heightened fragmentation and spatial isolation. Agricultural areas such as Sandoa and Kapanga were particularly vulnerable to deforestation. On the other hand, the miombo forest cover in the mining areas is still representative, with over 30% of the landscape covered. Notably, the reduction in woodland cover within protected areas was substantial, with significant losses observed across both agricultural and mining territories. The loss of miombo woodland cover in Lualaba and its territories was accompanied by an increase in landscape patch diversity, as indicated by the Shannon diversity index, suggesting a shift to more heterogeneous landscapes. These findings underscore a complex deforestation pattern, highlighting the intense local impact on miombo woodland cover loss. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen miombo woodland regulation enforcement, and actively support protected areas. Involvement from both local and international stakeholders is imperative to preserve the ecological richness and functionality of Lualaba Province's miombo woodland ecosystems.

Preprint ARTICLE | doi:10.20944/preprints202408.1956.v1

Predictive Modelling of Land Cover Changes in the Greater Amanzule Peatlands Using Multi-Source Remote Sensing and Machine Learning Techniques

Alex Owusu Amoakoh, Paul Aplin, Pedro Rodríguez-Veiga, Cherith Moses, Carolina Peña Alonso, Joaquín A. Cortés, Irene Delgado-Fernandez, Stephen Kankam, Justice Camillus Mensah, Daniel Doku Nii Nortey

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land cover change; Tropical peatlands; Machine learning classification; Environmental conservation; Predictive modelling

Online: 27 August 2024 (16:38:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1937.v1

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Haili Dong, Fei Tian

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: soil salinity; machine learning; stacking; remote sensing inversion

Online: 27 August 2024 (11:48:58 CEST)

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Soil salinization is an essential risk factor for agricultural development as well as for food security, and how to obtain regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of the inversions for soil salinity, a new inversion model for soil salinity based on stacking integrated algorithm for learning was submitted for this work which took the prediction results of several basic models as new features and then trained a secondary model to fuse the prediction results of basic models. We compared and analyzed it against four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network. Findings indicated the stacking integrated learning regression model fitted better and had good stability, on the test set, the stacking integrated learning regression model showed a relative increase of 8.16% in R2, a relative decrease of 13.95% in RMSE, and a relative increase of 6.47% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south in 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.8 g/kg in the study area had decreased by 19.64% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for a fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Preprint ARTICLE | doi:10.20944/preprints202408.1829.v1

A New NDSA (Normalized Differential Spectral Attenuation) Measurement Campaign for Estimating Water Vapor along a Radio Link

Luca Facheris, Fabrizio Cuccoli, Ugo Cortesi, Samuele del Bianco, Marco Gai, Giovanni Macelloni, Francesco Montomoli

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: microwaves; water vapor; troposphere; differential attenuation

Online: 27 August 2024 (02:50:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1571.v1

The Use of Remote Sensing for Maritime Surveillance for Security and Safety

Shivam Kumar Pandey, Bansi Kaneria

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; maritime surveillance; synthetic aperture radar (SAR); unmanned aerial vehicles (UAVs); maritime security; international maritime law; illegal fishing; environmental monitoring; data integration; satellite imagery

Online: 22 August 2024 (08:22:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1491.v1

Development and Evaluation of New Visible Index on Remote Sensing for Estimating House Damage and Sediment Runoff in Disasters

Haruhiro Shiraishi, Yuichiro Usuda

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Earthquake; Remote sensing; House damage

Online: 20 August 2024 (15:19:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1458.v1

Study on Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Pingbo Liu, Gui Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest fire spread; Wang Zhengfei model; remote sensing; prediction

Online: 20 August 2024 (14:24:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1292.v1

Quantitative Evaluations of Pumping-Induced Land Subsidence and Mitigation Strategies by Integrated Remote Sensing and Site-Specific Hydrogeological Observations

Thai-Vinh-Truong Nguyen, Chuen-Fa Ni, Ya-Ju Hsu, Pi-E Rubia Chen, Nguyen Hoang Hiep, I-Hsian Lee, Chi-Ping Lin, Gabriel Gosselin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land subsidence; InSAR; multilayer compaction; groundwater level drops; Taiwan High Speed Rail

Online: 20 August 2024 (12:46:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1404.v1

Fitness of Multi-Resolution Remotely Sensed Data for Cadastral Mapping in Ekiti State, Nigeria

Israel Oluwaseun Taiwo, Olomolatan Matthew Ibitoye, Sunday Olukayode Oladejo, Mila Koeva

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV Adoption; Multi-resolution aerial imageries; Cadastral mapping; Fit-for-Purpose Land Administration; Land governance; Spatial data collection/acquisition

Online: 20 August 2024 (12:04:04 CEST)

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In developing nations, such as Ekiti State, Nigeria, the use of remote sensing data, especially satellite and UAV imagery, remains significantly underutilized in land administration. This limits the potential of multi-resolution imagery in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping six distinct rural, peri-urban, and urban sites, and creating orthomosaics with resolutions ranging from 2.2cm to 3.39cm. Boundaries were manually delineated from multi-resolution orthomosaics to acquire coordinates, and parcel areas were calculated. Using 0.05m UAV imagery as the reference, the Root Mean Square Error (RMSE) was computed to evaluate 0.1, 0.5, and 1m orthomosaics' accuracy for boundary delineation across the six geographical contexts. The Normalized Parcel Area Error (NPAE) was also calculated to determine how the multi-resolution imageries affected the property areas. Results showed a consistent increase in error with increasing resolution (0.1m to 1m) in formal areas (Igedora, Maryland, Okebola) with physical demarcations, from 0.117m to 1.747m RMSE. In contrast, informal settlements exhibited inconsistent error values, indicating a differing impact of resolution on error between formal and informal areas. The research demonstrates the role of physical demarcations in facilitating cadastral mapping, with formal settlements demonstrating the highest suitability. This study recommends the use of imagery resolution specific to each context, offering guidance for improved land governance. Implications and recommendations include promoting awareness of settlement typologies in aerial imagery use for cadastral mapping, leveraging UAVs for high-resolution aerial imagery, and addressing regulatory challenges to promote UAV adoption. Finally, the RMSE and NPAE values can serve as metrics for assessing different resolution images’ fitness for cadastral mapping.

Preprint ARTICLE | doi:10.20944/preprints202408.1289.v1

Method of AI-Based Precipitation Estimation by Using FY-4B Satellite Data

Nianqing Liu, Jianying Jiang, Dongyan Mao, Meng Fang, Yun Li, Bowei Han, Suling Ren

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: FY-4B; artificial intelligence; multi-temporal; precipitation estimation; evaluation

Online: 19 August 2024 (10:03:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1232.v1

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

Hong Zhu, Jian Meng, Jiaqi Yao, Nan Xu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Multi-source heterogeneity; Flood disaster; Social media information; TextCNN-Attention; Transportation roads; Loss assessment

Online: 19 August 2024 (04:33:27 CEST)

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In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Preprint ARTICLE | doi:10.20944/preprints202408.1223.v1

Evaluation of the Usability of UAV LiDAR for Analysis of Karst(Doline) Terrain Morphology

Juneseok Kim, Ilyoung Hong

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV; LiDAR; doline; DEM

Online: 16 August 2024 (10:03:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1102.v1

Can SAR Data Predict Harvesting Residues Mass?

Alberto Udali, Henrik J. Persson, Bruce Talbot, Stefano Grigolato

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Interferometry; harvesting residues; SAR; sustainable forest management

Online: 15 August 2024 (07:29:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1118.v1

Delineation Protocol of Agricultural Management Zones (Olive Trees, Alfalfa) at Field Scale (Crete, Greece)

David Chatzidavid, Eleni Kokinou, Nikolaos Gerarchakis, Ioannis Kontogiorgakis, Alessio Bucaioni, Milos Bogdanovic

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Precision Agriculture; Soil and crop response; Remote Sensing; Geophysical mapping; Machine learning; IRRIGOPTIMAL®; system

Online: 15 August 2024 (06:28:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.1033.v1

Enhanced Mapping for Ecosystem Management: Evaluating the Accuracy of Sentinel-1 and Sentinel-2 Data Fusion Compared to Sole Sentinel-2 Using Random Forest Classification

Sajib Sarker, Xin Wang, Abul Azad

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Data Fusion; Ecosystem Mapping; Random Forest Classifier; Multi-Sensor Imagery; Classification Accuracy

Online: 15 August 2024 (03:00:00 CEST)

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Recent advances in satellite technology have brought enormous potential to ecosystem mapping, which is one of the fundamental components of environmental studies. In this paper, a Random Forest classifier is applied for the strict assessment of the efficiency of ecosystem mapping through a detailed comparative analysis between combined Sentinel-1 and Sentinel-2 data and stand-alone Sentinel-2 imagery over three priority ecosystems, including wetlands, riverine areas, and mangroves in Bangladesh. The collocated images, based on the integration of Sentinel-1 data with Sentinel-2 data, would do better than Sentinel-2 imagery alone over various ecosystems. Particularly, in this study, attention focused on the Hakaluki Haor area for the wetlands, the Padma-Jamuna River confluence for the riverine ecosystem, and the Sundarban forest for mangroves. By leveraging Synthetic Aperture Radar (SAR) data in C-band dual-polarization from Sentinel-1 and four spectral bands (blue, green, red, and near-infrared) from Sentinel-2, the study analyzes imagery from December 2022 to February 2023. A 5% cloud masking filter is applied to optical data to enhance accuracy. In this methodology, 70% of the total signature values are used for training the classification model and the remaining 30% for testing. It can be noticed from the results that with the use of fused data, remarkably high accuracy in classification has been improved, such as overall accuracies of 94.17% for mangroves, 87.30% for riverine, and 85.96% for wetland ecosystems. In contrast, the use of singular Sentinel-2 imagery yields lower accuracies of 91.56%, 85.21%, and 82.51% for the respective ecosystems. The integration of radar data is shown to provide critical information, especially in environments with dense vegetation or cloud cover, where optical data alone may be insufficient. The findings of this study underline the limitations of relying on Sentinel-2 imagery to capture complex details of diverse ecosystems and highlight the need to include Sentinel-1 data for a more holistic analysis. This fusion allows improved accuracy to be achieved, which not only brings in more depth of ecological knowledge but also underpins more effective conservation strategies.

Preprint ARTICLE | doi:10.20944/preprints202408.1010.v1

Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI

Sassan Mohammady, Kevin J. Erratt, Irena F. Creed

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: chlorophyll-a; freshwater; phytoplankton; remote sensing; wildfire

Online: 14 August 2024 (08:21:08 CEST)

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The increasing frequency and severity of phytoplankton blooms worldwide highlight the need for advancements in monitoring technologies. Optical remote sensors, such as Landsat, have proven to be a cost-effective method for large-scale, near-real-time assessments of phytoplankton biomass in lakes. However, its effectiveness is often compromised by atmospheric interferences like clouds, dust, and wildfire smoke, which can obscure the clear-sky conditions essential for accurate remote sensing. While partial atmospheric correction (removing only the Rayleigh effect) is commonly applied to address these interferences, it remains inadequate for mitigating the impact of wildfire smoke. This study investigates the potential of Landsat's coastal/aerosol band (B1) for assessing wildfire smoke interference effects on Chlorophyll-a (Chl-a) retrieval models, which serve as proxies for phytoplankton biomass. We employed cluster analysis of B1 values to create a screening system based on aerosol reflectance, categorizing smoke interference into low, moderate, and high levels. Subsequently, we applied both partial (Rayleigh-corrected reflectance) and full (Landsat 8 Level 2 surface reflectance) atmospheric corrections before developing the Chl-a retrieval models. Excluding high wildfire smoke interference (B1 > 0.07) from the Chl-a calibration dataset significantly enhanced model performance, increasing the r-squared value from approximately 0.55 to 0.80. Moderate smoke interference (0.05 < B1 < 0.07) yielded results comparable to low-interference conditions. Notably, Rayleigh-corrected reflectance, even without additional aerosol band filtering, achieved higher r-squared values for the Chl-a retrieval model than full atmospheric correction. B1 thus proves to be a valuable tool for identifying low smoke-impacted observations, offering an effective method to monitor phytoplankton biomass amid increasing wildfire activity and improving the capacity to monitor aquatic environments in a changing global landscape.

Preprint ARTICLE | doi:10.20944/preprints202408.0962.v1

Remote Sensing of Chlorophyll-A in Clear vs. Turbid Waters in Lakes

Forough Fendereski, Irena F. Creed, Charles G. Trick

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: phytoplankton blooms; chlorophyll-a; optical properties; Landsat; Google Earth Engine; Lake Winnipeg.

Online: 14 August 2024 (07:23:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0865.v1

Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms

Madeleine Gillespie, Gregory S. Okin, Thoralf Meyer, Francisco Ochoa

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Random Forest; burn severity; UAV; Kalahari; remote sensing; savanna; machine learning; wildfire

Online: 13 August 2024 (11:32:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0739.v1

INterpolated FLOod Surface (INFLOS), a Rapid and Operational Tool to Estimate Flood Depths from Earth Observation Data for Emergency Management

Alessandro Caretto, Quentin Poterek, Rémi Braun, Stephen Clandillon, Claire Huber, Pietro Ceccato

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: flood depth; interpolation; digital terrain model; remote sensing; emergency management

Online: 12 August 2024 (14:08:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0804.v1

Uav Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting Field-Scale Yield of Spring Maize

Yue Zhang, Yansong Wang, Hang Hao, Ziqi Li, Yumei Long, Xingyu Zhang, Chenzhen Xia

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: unmanned aerial vehicles; hyperspectral imagery; spectral indices; contour map; yield

Online: 12 August 2024 (11:41:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0803.v1

Cascading Landslide: Kinematic and Fem Analysis through Re-2 Mote Sensing Techniques

Nicola Sciarra, Mirko Francioni, Massimo Mangifesta, Diego Di Martire, Domenico Calcaterra, Luigi Guerriero

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Cascading landslides; Slope stability; InSAR interferometry; Pixel Offset; Finite element 31 Analysis; Numerical Modelling

Online: 12 August 2024 (11:37:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0734.v1

Validation of OLCI Sentinel-3 water products in the Baltic Sea and evaluating the effect of System Vicarious Calibration (SVC)

Sean O’Kane, Tim McCarthy, Rowan Fealy, Susanne Kratzer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: OLCI; Sentinel-3; C2RCC; System Vicarious Calibration (SVC); Baltic Sea; Coloured Dissolved Organic Matter (CDOM); Chlorophyll-a; Total Suspended Matter (TSM)

Online: 12 August 2024 (11:25:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0372.v1

Enhancing Grassland Management Sustainability Through Drone And Satellite: A Comparative Study

Michael Gbenga Ogungbuyi, Caroline Mohammed, Andrew M. Fischer, Darren Turner, Jason Whitehead, Matthew Tom Harrison

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Machine learning; artificial intelligence; drone; photogrammetry; pasture; grassland; monitoring; agricultural sustainability; adoption; management

Online: 6 August 2024 (12:11:17 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0409.v1

Automated Cloud Shadow Detection from Satellite Orthoimages with Un-corrected Cloud Relief Displacements

Hyeong-Gyu Kim, Wansang Yoon, Taejung Kim

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Cloud shadow detection; Shadow direction; Cloud relief displacement; Orthoimage; Directional vector of cloud shadow

Online: 6 August 2024 (09:50:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0387.v1

Coastal Sea Ice Concentration Derived from Marine Radar Images: A Case Study from Utqiaġvik, Alaska

Félix St-Denis, Bruno Louis Tremblay, Andrew R. Mahoney, Pacifica Askitrea Leona Mai Takata-Glushkoff

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Coastal Radar; Sea Ice Concentration; Remote sensing; Utqiaġvik; Arctic

Online: 6 August 2024 (06:20:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0030.v1

Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust

Tino Nerger, Patrick P. Neumann, Michael G. Weller

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Passive smart dust; remote sensing; drone; UAV; hazard; optical detection; chemosensor; pH indicator; paper-based sensors; confetti-like sensor; harmful chemicals

Online: 1 August 2024 (12:40:21 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.2411.v1

Peatland Transformation: Land Cover Changes and Driving Factors in the Kampar Peninsula (1990-2020)

Dian Novarina, Jatna Supriatna, Iman Santoso, Mahawan Karuniasa

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Kampar Peninsula; Peatland; NDFI; Spatial Analysis; Riau Province

Online: 30 July 2024 (11:03:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.2426.v1

Identification and Characterization of Reclaimed and Unreclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas

Victoria Grace Stengel, Tanya J. Gallegos, Bernard E. Hubbard, Steven M Cahan, David S Wallace

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Uranium; abandoned mines; reclaimed mines,; Landsat; lidar; Time series; Google Earth Engine

Online: 30 July 2024 (09:06:32 CEST)

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Open-pit uranium mining in the south Texas Gulf Coast region was active during the second half of the 20th century. Although some mines have extensive records documenting the locations and extents with descriptions of reclamation procedures and timelines, abandoned and reclaimed mines with minimal documentation concurrently exist where little is known about locations, mine waste volumes, and reclamation status. Advanced mapping of these mining operations is helpful for producing basic mine land inventories and assessments of mine waste; identifying potential undeveloped resources and environmental health issues; and for developing proper management, restoration, and reclamation strategies. We studied known reclaimed and unreclaimed mine sites to develop a spatiotemporal mapping approach utilizing multiple techniques in remote sensing as a proof of concept. A mapping classification system was developed to identify uranium mine waste-rock piles, mine pits (with and without waste water), the total area of land disturbance due to mining, and to differentiate abandoned and reclaimed mine features using lidar (Light Detection and Ranging) analysis and multispectral temporal analysis. Visual interpretation of Landsat imagery and time-series analysis revealed the temporal life-cycle of mining, historical areas of disturbance, and reclamation; however, mine features below vegetation canopies are not always visible in satellite imagery. Lidar laser scanning penetrates the vegetation canopy to measure the topographic elevation and exposes anthropogenic modifications to the landscape. Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features, and expansive areas of mining disturbance. The spatiotemporal mapping approach proved to be useful for identifying and characterizing known mine features, areas of disturbance, and reclamation status. Historical pre-mining topographic data were available at only 4 of the mines for comparison where the cut-and-fill tool estimated 21% less waste-rock volume, indicating that information about pre-mining terrain topography and/or subsurface mine waste geometry is needed to improve accuracies for quantifying buried mine waste volume at reclaimed mine sites.

Preprint ARTICLE | doi:10.20944/preprints202407.2039.v1

Assessment of Hygroscopic Behavior of Arctic Aerosol by contemporary Lidar and Radiosonde Observations

Nele Eggers, Sandra Graßl, Christoph Ritter

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Arctic Aerosol; Lidar; hygroscopic growth

Online: 25 July 2024 (12:05:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1931.v1

Using Contrast Boundary Concentration of LST for Earthquake Approach Assessment in Turkey, 6-8 February 2023

Serhii Nikulin, Kateryna Sergieieva, Olga Korobko, Vita Kashtan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land surface temperature; earthquake; contrast boundaries; East Anatolian Fault zone; Landsat 8

Online: 24 July 2024 (12:42:30 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1784.v1

Emergency Mapping for Flood Events Using Satellite Imagery (Optical and/or Synthetic Aperture Radar) and Benchmarking between Open Source and Commercial Geospatial Analysis Tools

Nancy Alvan Romero, Wilson A. Suarez Alayza

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: weather; cyclone; emergency map; flood; satellite image; Peru

Online: 23 July 2024 (13:39:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1797.v1

Development of a UAV-Based Multi-Sensor Deep Learning Model for Predicting Napa Cabbage Fresh Weight and Determining Optimal Harvest Time

Jong-Hwa Park, Dong-Ho Lee

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Napa cabbage; fresh weight prediction; unmanned aerial vehicle (UAV); multi-sensor fusion; deep learning; precision agriculture

Online: 23 July 2024 (12:26:16 CEST)

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Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Preprint ARTICLE | doi:10.20944/preprints202407.1727.v1

Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico

Rufino Sandoval-García, Joel Rascón-Solano, Eduardo Alanís-Rodríguez, Samuel García-García, José A. Sigala, Oscar Aguirre-Calderón

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sustainable forest exploitation; Flora and Fauna Protection Area; Vegetation cover; Multitemporal analysis; QGIS.

Online: 22 July 2024 (16:28:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1718.v1

Early Prediction of Kimchi Cabbage Height using Drone Imagery and Long Short-Term Memory (LSTM) Model

Seung-hwan Go, Jong-hwa Park

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Drone Imagery; Long Short-Term Memory; Kimchi Cabbage; Growth Prediction; Precision Agriculture

Online: 22 July 2024 (11:52:14 CEST)

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This study introduces a novel method for early prediction of Kimchi cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) height, utilizing drone imagery and a long short-term memory (LSTM) model. The research was conducted on a testbed at the National Institute of Agricultural Sciences (NAS) in South Korea, encompassing two distinct soil types (loam and sandy loam) to investigate their impact on growth. High-resolution drone images were captured throughout the growing season to generate a canopy height model (CHM) for estimating plant height at various stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this data to predict the final height of Kimchi cabbage at harvest. Three LSTM models were developed using time-series data collected at 29, 36, and 44 days after planting (DAP). The model trained on data from DAP 44 demonstrated the highest accuracy with a coefficient of determination (R²) of 0.83, a mean absolute error (MAE) of 2.48 cm, and a root mean square error (RMSE) of 3.26 cm, outperforming models trained on earlier data. Color-coded maps were generated to visualize the spatial distribution of predicted Kimchi cabbage heights, revealing variations in growth patterns across the testbed and confirming the model's potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed optimal for informing cultivation management decisions. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for early and accurate prediction of Kimchi cabbage height. The proposed technology enables data-driven decision-making for farmers, facilitating timely interventions based on predicted growth patterns. This could lead to improved crop yields, resource optimization, and a more sustainable agricultural future. Future research will focus on refining the model's accuracy and exploring its applicability to other crops, further expanding the potential of precision agriculture technologies.

Preprint ARTICLE | doi:10.20944/preprints202407.1631.v1

Prescribed Fire Reduces Wildfire Damages to Valued Resources Within the Okefenokee National Wildlife Refuge

C. Wade Ross, E. Louise Loudermilk, Joseph J. O’Brien, Steven A. Flanagan, Grant Snitker, J. Kevin Hiers

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: burn severity; controlled burn; dNBR; fuels treatments; GAM; LANDFIRE; prescribed fire; valued resources; wildfire; wildland fire

Online: 22 July 2024 (05:02:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1467.v1

Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resources Management

Hafiz Md-Tahir, Hafiz Sultan Mahmood, Muzammil Husain, Ayesha Khalil, Muhammad Shoaib, Mahmood Ali, Muhammad Mohsin Ali, Muhammad Tasawar, Yasir Ali Khan, Usman Khalid Awan, Muhammad Jehanzeb Masud Cheema

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing; Geographical information system; Vegetation index; LULC classification; Agricultural mechanization; Strategic planning; Resources management; Artificial intelligence algorithms; Crop phenology, Satellite data; Precision agriculture

Online: 18 July 2024 (11:28:24 CEST)

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Preprint REVIEW | doi:10.20944/preprints202407.1407.v1

Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications

Friederike Magdalena Koerting, Saeid Asadzadeh, Justus Constantin Hildebrand, Ekaterina Savinova, Evlampia Kouzeli, Konstantinos G. Nikolakopoulos, David Lindblom, Nicole Koellner, Simon J. Buckley, Miranda R. Lehman, Daniel Schläpfer, Steven Micklethwaite

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: hyperspectral imaging; UAS; mining; visible near-infrared; shortwave infrared; mineral mapping; waste remediation; environmental monitoring; drone

Online: 17 July 2024 (10:56:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0085.v2

Digital Documentation and Conjectural Reconstruction of Raya Gopura Using Geospatial Technologies

Suhas Muralidhar, Prithvi N Prabhu, Neethu Urs B A, Ashutosh Bhardwaj

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Digital Documentation; Photogrammetry; UAV; Cultural Heritage; Architecture; LiDAR; Reality Capture

Online: 17 July 2024 (09:57:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1304.v1

Tidal Flat Extraction and Analysis in China Based on Multi-source Remote Sensing Image Collection and MSIC-OA Algorithm

JiXiang Sun, Cheng Tang, Ke Mu, Yanfang Li, Xiangyang Zheng, Tao Zou

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; Google Earth Engine; MSIC-OA; tidal flat resources; shoreline

Online: 16 July 2024 (10:39:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202406.1256.v2

Groundwater Potential Zone Mapping to Understand the Sustainability of Springs in a Micro Watershed of Kosi River, Kumaun Lesser Himalaya, Uttarakhand, India

Puthiyottil Nijesh, Radha Dixit, Abhinav Patel, Abhinesh Kumar Singh, Anant Gautam, Raju Rai, Sury Kant Singh, Neeraj Pant, Meera Soman, Shive Prakash Rai

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Springs; Khulgad Micro watershed; AHP; Lineaments; Groundwater potential zone

Online: 15 July 2024 (19:57:23 CEST)

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Springs play a significant role in maintaining hydrological balance in the mountain ecosystem. The communities living in the mountain tops rely entirely on spring water for their daily needs. In recent decades, spring water discharges have decreased drastically in the Himalayan regions and become seasonal. Remote sensing and geographical information system techniques have proved to be essential for understanding the sustainable development of groundwater, particularly where people rely solely on spring water in the mountainous regions of the Himalayas. This study employs the Analytical Hierarchy Process (AHP) to identify groundwater potential zones. It involves overlaying seven thematic layers concerning elements of physical features and land-use/land-cover of the region, using the weighted overlay toolbox in ArcGIS, with each layer being assigned a weight based on its importance. Based on the results, groundwater potential zones have been classified into five categories: poor, fair, good, very good, and excellent. The study found that poor groundwater potential zones cover 1.1% of the area, fair zones cover 27.8%, good zones cover 23.3%, very good zones cover 26.6%, and excellent zones cover 21.2% of the total area. The excellent to very good potential zones are associated with perennial springs that have higher discharge (4.30-30.92 l/m), lineaments, rainfall, forested land, and porous soils, favoring the potentiality of the springs. The fair and poor groundwater potential zones are associated with seasonal flowing springs, consisting of barren land, less rainfall, and low fracture density, which influence the potentiality of groundwater. The validation of the groundwater potential zones indicated an Area Under Curve (AUC) value of 0.88, which shows good agreement with observed spring water locations. The identified groundwater potential zones are valuable for prioritizing the area for the sustainable development of spring water. Particularly, it is helpful for policymakers and government bodies to be involved in developing strategies for sustainable water management at the micro-watershed level and safeguarding spring water resources using integrated watershed management in the headwater region of the Himalayas.

Preprint ARTICLE | doi:10.20944/preprints202407.1106.v1

Land Use Change and its Impacts on a Critical African Watershed: Lessons from the Ruhuhu River Sub-Basin

Sosteness Jerome Nakamo, Hellen Francis, Fadhili Mgumia

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land Use and Land Cover Change; Ruhuhu River Sub-Basin; Remote Sensing; Lake Nyasa/Niassa/Malawi

Online: 15 July 2024 (04:54:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.1095.v1

Impact of Image Size and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification

Calimanut-Ionut Cira, Miguel Angel Manso Callejo, Naoto Yokota, Tudor Sălăgean, Ana-Cornelia Badea

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: road classification; aerial orthoimagery; deep learning; image size; image overlap; convolutional neural networks; statistical performance evaluation

Online: 14 July 2024 (03:11:57 CEST)

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Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Preprint ARTICLE | doi:10.20944/preprints202407.1060.v1

Assessment of Earthquake Damage and Economic Recovery in Hatay Province, Turkey Using Nighttime Light Data

Feng Li, Shunbao Liao, Xiaobo Zhang, Tianxuan Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: rapid assessment method; Turkey earthquake damage; economic recovery assessment; Hatay Province; nighttime light; NPP-VIIRS; SDGSAT-1

Online: 12 July 2024 (13:50:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0799.v1

An Image Compensation-based Range–Doppler model for SAR High-Precision Positioning

Kexin Cheng, Youqiang Dong

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Range–Doppler model; Synthetic Aperture Radar; SAR Photogrammetry; image geolocation; geometric correction; accuracy improvement

Online: 10 July 2024 (14:20:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0796.v1

StarDICE II: calibration of an uncooled infrared thermal camera for atmospheric gray extinction characterization

Kélian Sommer, Bertrand Plez, Johann Cohen-Tanugi, Sylvie Dagoret-Campagne, Marc Moniez, Jérémy Neveu, Marc Betoule, Sébastien Bongard, Fabrice Feinstein, Laurent Le Guillou, Claire Juramy, Eduardo Sepulveda, Thierry Souverin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: uncooled infrared thermal camera calibration; cirrus cloud; atmosphere monitoring; gray extinction; low radiances

Online: 10 July 2024 (11:35:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0702.v1

Attention Semantic Segmentation Network for Remote Sensing Images of Water Area

SEUNG JUN LEE, Hong Sik Yoon, Sang Woo Kwak

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing; semantic segmentation; deep convolutional neural networks

Online: 9 July 2024 (05:45:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0631.v1

Delineating and Sub-classifying Depressional Wetlands with Gradient Analysis of Littoral Vegetation Using Sentinel-2A Vege-Tation Indices

Basanda Xhantilomzi Nondlazi, Moses Azong Cho, Heidi van Deventer, Erwin Sieben

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Aichi Target 11; Sentinel-2A; Remote Sensing Vegetation Indices; Salinity; NDSI; NDVI; NDWI; RENDVI; Sustainable Development Goal (SDG) 6.6 & 15.1

Online: 8 July 2024 (12:32:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0572.v1

Spatio-Temporal Analysis and Impact of Land Use Land Cover in Eco-Environmental Quality Based on Remote Sensing Ecological Index (RSEI): A Case Study of Rupandehi District, Nepal

Gaurav Parajuli, Yogesh Regmi, Bimal Pokhrel, Tri Dev Acharya

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote Sensing Ecological Index (RSEI); Land Use Land Cover Change; Ecological Quality; Principal Component Analysis (PCA); Google Earth Engine (GEE)

Online: 8 July 2024 (09:04:55 CEST)

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Preprint REVIEW | doi:10.20944/preprints202407.0269.v2

Understanding Tree Mortality Patterns: A Comprehensive Review of Remote Sensing and Ground-Based Studies

Filippos Eliades, Dimitrios Sarris, Felix Bachofer, Silas Michaelides, Diofantos Hadjimitsis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: tree mortality; land degradation; desertification; Remote Sensing; meteorology; review; Earth Observation; climate change

Online: 8 July 2024 (08:35:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0571.v1

Quantifying Carbon Sequestration of Forest Ecosystem Using GIS and Remote Sensing; the Case of Yeraba State Forest East Gojjam Zone Amhara Regional State

Tilahun Abie Fetene

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Carbon sink; Yeraba state forest; Environmental factors; vegetation indices

Online: 8 July 2024 (08:32:22 CEST)

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By sinking and storing carbon from the atmosphere via photosynthesis, forests play a significant role in combating climate change. Global warming is currently the most pressing global issue. Forests play an important role in the global carbon cycle, covering 30% of the world's terrestrial land area and conserving 81% of the earth's terrestrial carbon biomass. The aim of the study was to quantify and map the Carbon Sink and Stock Values of the Yeraba state forest ecosystem. Primary and secondary data sources were used in this study. Primary data such as plot length and width, DBH (diameter at breast height), soil data, and ground control point (GCP) were obtained in the field using a Garmin 72 GPS, a clinometer, and a diameter tape. Diameter tape, caliper and Soil ogre materials. Beside from this sentimental 2 satellite image, there was also another important primary data. In addition to the primary data source, secondary data sources were used. It includes published books, journals and studies, reports, website sources, and other materials that are publicly available. The systematic sampling technique for identifying each intersection point in the area at regular intervals was the most appropriate sampling design for this study. The study used data from four forest carbon pools, including AGB, BGB, soil, and litter, to calculate the carbon sink and stock values of the Yeraba state forest ecosystem. A total of 17 quadrats, each measuring 20m x 20m and spaced 428 m apart, were used to collect vegetation data. For litter and soil sample collection, five 1 m x 1 m sub-quadrats were established at each quadrat's four corners and center. This data was correlated with five Sentinel 2A level 1c imagery data derived vegetation indices of 2023 to obtain a model and estimate of AGB and AGC at the study site. The total mean carbon stock density of Yeraba state Forest was 404.895 t/ha, with 223.656t/ha, 58.15t/ha, 3.41t/ha, and 119.679 t/ha in the above ground carbon, below ground carbon, litter carbon, and soil carbon pools, respectively. The three environmental factors that influence the distribution of different carbon pools in the forest are altitudinal gradient, slope, and aspect. Standing from the result and inputs the researcher recommended that, Future research may find that using LiDAR and Radar imageries, which can address the saturation and canopy penetration issues, will increase the accuracy of carbon stock estimation.

Preprint ARTICLE | doi:10.20944/preprints202407.0385.v1

SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification

Mohammed Q. Alkhatib, M. Sami Zitouni, Mina Al-Saad, Nour Abura’ed, Hussain Al-Ahmad

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Complex-Valued Convolutional Neural Network (CV-CNN); Polarimetric Synthetic Aperture Radar (PolSAR) Image Classification, Attention Mechanism; Feature Fusion

Online: 4 July 2024 (14:46:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0442.v1

Assessing Land Use and Land Cover Dynamics of Irrawaddy Delta: Remote Sensing Analysis and Future Projections

Hafez Ahmad, Felix Jose, M M Nabi, Shakila Islam Jhara, Frank Juma Ong’ondo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing; LULC; Irrawaddy Delta; artificial neural networks; cellular automata; Land Modeling; Deforestation; Coastal Flooding

Online: 4 July 2024 (12:35:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0292.v1

The Impact of the Expansion and Contraction of China Cities on Carbon Emissions, 2002–2021, Evidence from Integrated Nighttime Light Data

Jiaqi Qian, Yanning Guan, Tao Yang, Aoming Ruan, Wutao Yao, Rui Deng, Zhishou Wei, Chunyan Zhang, Shan Guo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Spatial-temporal analysis of urbanization; Nighttime light data; Carbon emissions; City classi-fication

Online: 3 July 2024 (11:53:46 CEST)

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Preprint REVIEW | doi:10.20944/preprints202407.0269.v1

Understanding Tree Mortality Patterns: A Comprehensive Review of Remote Sensing and Ground-based studies

Filippos Eliades, Dimitrios Sarris, Felix Bachofer, Silas Michaelides, Diofantos Hadjimitsis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: tree mortality; land degradation; desertification; Remote Sensing; meteorology; review; Earth Observation; climate change

Online: 3 July 2024 (02:49:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202312.0500.v3

A Modification to the Remotely Sensed Active Layer Thickness (ReSALT) Algorithm to have Self-Consistency

Jatin Mathur

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: synthetic aperture radar; interferometric synthetic aperture radar; permafrost thaw; climate change

Online: 2 July 2024 (13:21:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0199.v1

DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image

Chen Zhijie, Chen Yu, Wang Yuan, Wang Xiaoyan, Wang Xinsheng, Xiang Zhouru

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: deep learning; hyperspectral images; classification; hyperspectral feature map

Online: 2 July 2024 (11:21:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202407.0077.v1

Enhancing Regional Quasi-Geoid Refinement Precision: An Analytical Approach Employing ADS80 Tri-linear Array Stereoscopic Imagery for Aerial Triangulation Densification and GNSS Gravity-Potential Leveling

Wei Xu, Gang Chen, Defang Yang, Kaihua Ding, Rendong Dong, Xuyan Ma, Sipeng Han, Shengpeng Zhang, Yongyin Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ADS80; InSAR; Aerial triangulation; GNSS gravity-potential Leveling; Quasi-geoid surface

Online: 1 July 2024 (13:07:50 CEST)

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Environmental and Earth Sciences