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Preprint ARTICLE | doi:10.20944/preprints202404.1215.v1

Evaluating the VIIRS Imagery for Developing Near Real-Time Nationwide Vegetation Cover Monitoring in Indonesia

Yudi Setiawan, Kustiyo Kustiyo, Sahid Agustian Hudjimartsu, Judin Purwanto, Riva Rovani, Anna Tosiani, Basyiruddin Usman, Tatik Kartika, Novie Indriasari, Lilik Budi Prasetyo, Belinda Arunarwati Margono

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: change detection; temporal vegetation dynamics; open area index; multi temporal VIIRS

Online: 18 April 2024 (10:57:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0945.v1

Coastal Copper Tailings Dispersal: 3D Mapping And Shoreline Impacts, Particle Migration, Leaching, And Toxicity

W. Charles Kerfoot, Gary Swain, Robert Regis, Varsha K. Raman, Colin N. Brooks, Chris Cook, Molly Reif

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Coastal Mine Tailings, Keweenaw Peninsula, LiDAR 3D Bathymetry, UAS drone studies, Particle Dispersal, Specific Gravity, Copper Concentrations, Buffalo Reef, Mohawkite Arsenic, Toxicity, Daphnia

Online: 15 April 2024 (10:32:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0803.v1

Feasibility of Using SWIR Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Mohammad Abuzar, Kathryn Sheffield, Andy McAllister

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Irrigated landcover; thresholding method; Central Goulburn District

Online: 11 April 2024 (10:03:23 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0724.v1

Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery

Stefan Peters, Jixue Liu, Gunnar Keppel, Anna Wendleder, Peiliang Xu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: landslide detection; satellite remote sensing; machine learning; classification; Google Earth Engine; transfer learning

Online: 11 April 2024 (04:12:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0705.v1

A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images

Zeenat Khadim Hussain, Jiang Congshir, Yuan Xin Xin, Muhammad Raza e Mustafa

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Deep Learning; Remote Sensing; PaddlePaddle; Rooftop; Semantic Segmentation; EISeg; Asymmetric Neural Network; PP-LiteSeg; Deeplab3; Dual Attention Network

Online: 10 April 2024 (12:10:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0713.v1

Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Orlando Bhungeni, Ashadevi Ramjatan, Michael Gebreslasie

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: uMngeni River Catchment; Machine learning; LULC; Landsat 8; Remote sensing

Online: 10 April 2024 (08:18:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0642.v1

Near-Surface Dispersion and Current Observations by Dye, Drifter, and HF Radar in Coastal Water

Keunyong Kim, Tran Thi My Hong, Kyu‐Min Song, Yeong Baek Son, Young-Gyu Park, Geun-Ho Kwak, Jun Myoung Choi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Surface current; contaminant dispersion; dye; drifter; HF radar

Online: 9 April 2024 (11:51:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0666.v1

Remotely Sensed Changes in the Qinghai-Tibet Plateau Wetland Ecosystems and Their Response to Drought

Aodi Fu, Wenzheng Yu, Bashar Bashir, Xin Yao, Yawen Zhou, Jiwei Sun, Abdullah Alsalman, Karam Alsafadi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; Qinghai-Tibet plateau; wetland ecosystems; climate warming; landsat; SPEI

Online: 9 April 2024 (11:47:44 CEST)

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Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.

Preprint REVIEW | doi:10.20944/preprints202404.0593.v1

Spatial Data Infrastructure for Remote Sensing: A Comprehensive Analysis

Paola Carrara, Gloria Bordogna, Giacomo De Carolis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: spatial data infrastructure; remote sensing

Online: 9 April 2024 (11:15:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0606.v1

Mapping Quaking Aspen (Populus tremuloides Michx.) Using Seasonal Sentinel-1 and Sentinel-2 Composite Imagery across the Southern Rockies, USA

Maxwell Cook, Teresa Chapman, Sarah Hart, Asha Paudel, Jennifer Balch

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Forest species classification; Sentinel-1; Sentinel-2; seasonal imagery; phenology; spatial block cross-validation; Random Forests; Southern Rockies; landscape patch dynamics; Google Earth Engine

Online: 9 April 2024 (08:49:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0594.v1

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

Shuaiying Zhang, Lizhen Cui, Zhen Dong, Wentao An

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Polarimetric Synthetic Aperture Radar (PolSAR); Reflection Symmetric Decomposition (RSD); Data Input Scheme; Land Classification; Polarimetric Scattering Characteristics.

Online: 9 April 2024 (00:29:24 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.0569.v1

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods

Lihui Zhong, Zhengquan Dai, Panfei Fang, Yong Cao, Leiguang Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; tree species classification; unimodal and multimodal remote sensing data; classic deep learning-based methods

Online: 8 April 2024 (15:05:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0307.v1

40-Year Fire History Reconstruction from Landsat Data in a Mediterranean Area of North Africa Following International Standards

Mostefa E. Kouachi, Amin Khairoun, Aymen Moghli, Souad Rahmani, Florent Mouillot, M. Jaime Baeza, Hassane Moutahir

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Algeria; BA products; Mediterranean Basin; Landsat; BAMTs; Google Earth Engine.

Online: 3 April 2024 (14:45:28 CEST)

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Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30-m spatial resolution in the typical Mediterranean Ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10-m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10 %; commission and omission errors < 8.20 %; Dice coefficients > 91.90 %, and relative biases < 3.44 %. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most extensive and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution for further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

Preprint ARTICLE | doi:10.20944/preprints202404.0244.v1

Supplementary Dam Site Selection Using GIS-Remote Sensing Approach: A Case Study of Wivenhoe Dam

Aseel Zytoon, Zahra Gharineiat, Omar Alajarmeh

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Climate Change; Flooding; Dam Surveying; GIS, DEM; Selection criteria

Online: 3 April 2024 (11:06:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0217.v1

ERNet: A Rapid Road Cracks Detection Method from Low-Altitude UAV Remote Sensing Image

Zexian Duan, Jiahang Liu, Xinpeng Ling, Jinlong Zhang, Zhiheng Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV remote sensing; Road Cracks; Semantic segmentation; Crack quantification; Edge detection

Online: 2 April 2024 (14:33:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0066.v1

Information Value Model Based Mapping of Updated Spatial and Temporal Landslide Susceptibility: A Case Study from East Sikkim District, India’s Northeastern Himalayas

Md Fariduddin Rafique, Varun Joshi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: East Sikkim; Landslide susceptibility; Information Value Model

Online: 2 April 2024 (12:48:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0027.v1

Framework to Create Dataset for Disaster Behavior Analysis using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis

Yee Jian Chew, Shih Yin Ooi, Ying Han Pang, Zheng You Lim

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Disaster behavior; forest fire behavior; forest fire dataset; data extraction framework; Google Earth Engine; remote sensing; Malaysia; ChatGPT; Noteable; Large Language Model

Online: 1 April 2024 (10:35:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202403.1809.v1

Evaluation of Ocean Color Algorithms to Retrieve Chlorophyll-A Concentration in the Mexican Pacific Ocean off the Baja California Peninsula, Mexico

Patricia Alvarado-Graef, Beatriz Martin-Atienza, Ramón Sosa-Ávalos, Dr. Reginaldo Durazo, Rafael Hernandez-Walls

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing; algoriths; ocean color; Chlorophyll-a

Online: 29 March 2024 (10:01:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1782.v1

Examination of the Spatial-Temporal Variations in Terrestrial Water Reserves and the Efficiency of Green Water Usage in China’s Three Northeastern Provinces

Yanying Wang, Xianzhi Wang, Longxue Zhao

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: terrestrial water reserves; GRACE satellite data; water use efficiency; spatial and temporal variations; three Northeastern provinces of China

Online: 28 March 2024 (14:26:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1494.v1

Polar Stratospheric Clouds Observations at Concordia Station by the Remotely Controlled Lidar Observatory

Luca Di Liberto, Francesco Colao, Federico Serva, Alessandro Bracci, Francesco Cairo, Marcel Snels

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: climate; ozone; stratosphere; polar stratospheric clouds; lidar

Online: 26 March 2024 (10:26:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1468.v1

The Potential Impact of Rising Sea Levels and Subsidence on Coastal Flooding Along the Northern Coast of Saint-Louis, Senegal

Cheikh Omar Tidjani CISSE, Emmanuel K BREMPONG, Rafael ALMAR, Bapentire Donatus ANGNUURENG, Boubou Aldiouma SY

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: sea level rise; subsidence; InSAR; coastal vulnerability; coastal flooding; RCP 2.6

Online: 25 March 2024 (11:19:08 CET)

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Preprint REVIEW | doi:10.20944/preprints202403.1430.v1

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

Friederike Koerting, Saeid Asadzadeh, Justus Constantin Hildebrand, Ekaterina Savinova, Evlampia Kouzeli, Konstantinos Nikolakopoulos, David Lindblom, Nicole Koellner, Simon J. Buckley, Miranda 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: 25 March 2024 (08:47:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1279.v1

Characterization of River Width Measurement Capability by Space Borne GNSS-Reflectometry

April Warnock, Christopher S. Ruf, Arie Knoll

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Riverine remote sensing; GNSS Reflectometry; CYGNSS; flood prediction; Water masks

Online: 21 March 2024 (08:22:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1201.v1

Use of Drones with Multispectral and Thermal Cameras to Assess the Biometric Characteristics and Water Status of Different Hazelnut Cultivars

Alessandra Vinci, Valerio Baiocchi, Raffaella Brigante, Chiara Traini, Daniela Farinelli

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: CSWI; Irrigation; LAI; NDVI; Tonda Francescana©; Tonda Gentile delle Langhe; Tonda Romana; Tonda Giffoni; Water stress; UAVs

Online: 20 March 2024 (11:30:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1141.v1

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

William Gareth Rees, Liliia Hebryn-Baidy, Vadym Belenok

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land surface temperature; land use/land cover; Landsat; Modis; air temperature; urban heat island; surface urban heat island; linear trend

Online: 19 March 2024 (11:19:07 CET)

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Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, the study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation R² = 0.879, compared to Landsat R² = 0.663. The application of supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations, manifested as a 70.3% increase in urban land, concurrently with a decrement in vegetative cover, especially 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09°C and 2.16°C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30°C and 2.24°C, with the bare land class showing the highest fluctuation 2.46°C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual data sets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as warmest classes with 39.51°C, 38.20°C and water by 35.96°C and dense vegetation 35.52°C, sparse vegetation 37.71°C as coldest, a trend consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Preprint ARTICLE | doi:10.20944/preprints202403.1045.v1

A Machine Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures

Athos Agapiou, Elias Gravanis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: archaeological proxies; cropmarks formation; subsurface archaeological remains; detection; machine-learning; spectral signatures

Online: 19 March 2024 (06:49:47 CET)

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Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Preprint ARTICLE | doi:10.20944/preprints202403.0959.v1

Magnetic Prospecting as a Tool for the Characterization of Mining Dams in the La Carolina Mining District (South of Spain)

Rosendo Mendoza, Javier Rey, Jose Vílchez, M. Carmen Hidalgo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Tailings dams; Environmental risk; Magnetometry; Magnetic susceptibility; La Carolina mining district; Spain

Online: 18 March 2024 (09:53:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0969.v1

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Nisit Pukrongta, Attaphongse Taparugssanagorn, Kiattisak Sangpradit

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ML-based precision agriculture; IoT-based precision agriculture; sustainable agriculture; PEnsemble 4; maize yield prediction; spatial prediction; remote sensing)

Online: 18 March 2024 (08:21:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0833.v1

SAINet: Spatially Adaptive Interaction Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Weidong Song, Huan He, Jiguang Dai, Guohui Jia, Jiakai Fan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; spatially adaptive; semantic segmentation; high-resolution; interaction

Online: 14 March 2024 (09:37:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0453.v1

Zagros Grass Index – a New Vegetation Index to Enhance Fire Fuel Mapping: Case Study in Zagros Mountains

Iraj Rahimi, Lia Duarte, Ana Claudia Teodoro

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote Sensing; Vegetation Index; NDVI Zagros Grass Index (ZGI); Forest Fire; Kurdo-Zagrosian

Online: 7 March 2024 (13:47:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0379.v1

Farmer Perceptions of Land Cover Classification of UAS Imagery of Coffee Agroecosystems in Puerto Rico

Gwendolyn Klenke, Shannon Brines, Nayethzi Hernandez, Kevin Li, Riley Glancy, Jose Cabrera, Blake Hendrix Neal, Kevin Adkins, Ronny Schroeder, Ivette Perfecto

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: agroecosystem; drones; farm management

Online: 7 March 2024 (10:11:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0411.v1

Methodology for identifying natural phenomena that form large floods using satellite data

Eugeniy Savchenko, Sergey Maklakov

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: floods, climate study, Meteosat satellites, data visualization, remote sensing, satellite sensing.

Online: 7 March 2024 (07:50:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0333.v1

Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges

Ute C. Herzfeld, Lawrence Hessburg, Thomas Trantow, Adam Hayes

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Physically Constrained Neural Networks; connectionist-geostatistical classification; crevasse classification; glacier surging; satellite image classification; machine learning

Online: 6 March 2024 (10:50:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0104.v1

Towards a Long-Term UAV Monitoring Framework/Strategy for Post-mining Effects: Prosper-Haniel Case

Marcin Pawlik, Benjamin Haske, Hernan Flores, Bodo Bernsdorf, Tobias Rudolph

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Geomonitoring; Post-Mining; UAV

Online: 4 March 2024 (10:36:43 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202403.0090.v1

SERVIR West Africa Land Use and Land Cover Task Force: Building a Collaborative Network to Support Capacity Development in the Harmonization of Land Cover and Land Use Mapping and Monitoring in West Africa

Foster Mensah, Paul Bartel, Jacob Abramowitz, Emil A. Cherrington, Mansour Mahamane, Bako Mamane, Matieu Henry, Fatima Mushtaq, Antonio Di Gregorio, Amadou Moctar Dieye, Patrice Sanou, Glory Enaruvbe, Ndeye Fatou Mar

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land cover and land use; land cover classification; data harmonization; semantic interoperability; ISO-19144-2; West Africa, geospatial.

Online: 4 March 2024 (09:53:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0085.v1

Enhancing Precision in Crop Residue Cover Estimation: A Comparative Analysis of Sentinel-2 MSI and Landsat 8 OLI Data

Jia Du, Yiwei Zhang, Zhengwei Liang, Kaishan Song

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Remote sensing, crop residue cover; partial least squares regression; Sentinel-2 MSI; Landsat 8 OLI

Online: 4 March 2024 (05:51:04 CET)

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Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Preprint ARTICLE | doi:10.20944/preprints202402.1637.v1

Characterization of two vineyards in Mexico based on Sentinel-2 and meteorological data

Maria S. Del Rio, Víctor Cicuéndez, Carlos Yagüe

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: NDVI; MSAVI; Thermal indices; vineyard´; s phenology; grape varieties

Online: 28 February 2024 (17:01:29 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1582.v1

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-sensor Consistency Using Radiative Transfer Modeling

Feng Zhang, Xi Shao, Changyong Cao, Yong Chen, Wenhui Wang, Tung-Chang Liu, Xin Jing

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Visible Infrared Imaging Radiometer Suite (VIIRS); NOAA-21; NOAA-20; S-NPP; radiative transfer modeling; community radiative transfer model (CRTM); thermal emissive band (TEB); calibration; observation minus background difference (O-B); double-difference method.

Online: 27 February 2024 (17:18:19 CET)

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This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Preprint ARTICLE | doi:10.20944/preprints202402.1528.v1

Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact

Yaoshuai Dang, Leiku Yang, Jinling Song

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: flooding; crop damage assessment; analytic hierarchy process

Online: 27 February 2024 (10:28:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1516.v1

Concept of a Satellite Cross-Calibration Radiometer for in-orbit Calibration of Commercial Optical Satellites

Medhavy Thankappan, Jon Christopherson, Simon Cantrell, Robert Ryan, Mary Pagnutti, Courtney Bright, Denis Naughton, Kathryn Ruslander, Lan-Wei Wang, David Hudson, Jerad Shaw, Shankar Nag Ramaseri Chandra, Cody Anderson

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Keywords Cross-calibration; hyperspectral; multi-sensor interoperability; radiometric calibration

Online: 27 February 2024 (09:31:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1414.v1

Combining Hydroacoustics Data with Landsat Images to Map Seagrass Cover and to Identify Spatial Pattern Predictors (Historically Low Rainfall, Benthic Topography and Hurricanes) of Long-Term Change (1984 to 2021) in a Jamaican Marine Protected Area

Kurt McLaren, Jasmine Sedman, Karen McIntyre, Kurt Prospere

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: climate change; storms; submerged aquatic vegetation; random forest

Online: 26 February 2024 (11:35:43 CET)

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Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with > 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

Preprint ARTICLE | doi:10.20944/preprints202402.1324.v1

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

Thitimar Chongtaku, Attaphongse Taparugssanagorn, Hiroyuki Miyazaki, Takuji W Tsusaka

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: heat wave; heatwaves detection; land surface heatwaves; data gap-filling; machine learning algorithm; random forest regression; spatio-temporal databases; geospatial analysis; air temperature; land surface temperature

Online: 23 February 2024 (08:40:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1277.v1

Assessment of Vegetation Dynamics at a Hyper Arid Region Using Remote Sensing, a Case Study from The Arava Valley

Ariel Mordechai Meroz, He Yin, Noam Levin

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyper arid environment; rainfall trend; vegetation dynamics; time lag; land management

Online: 22 February 2024 (11:51:27 CET)

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The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

Preprint ARTICLE | doi:10.20944/preprints202402.1181.v1

Evaluation of Different Soil Salinity Indices Using Remote Sensing Techniques in Siwa Oasis, Egypt

Omnia H Salem, Zhonghua Jia

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Soil Salinity; Remote sensing; Landsat; Spectral Indices; Correlation; Land cover; Siwa; Egypt

Online: 21 February 2024 (11:38:31 CET)

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Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This investigation, carried out in Siwa Oasis, Egypt, aimed to assess the efficacy of several soil salinity indices derived from Landsat 5 and 7 satellite images. To achieve this, 56 on-site ground measurements were utilized for evaluation purposes. The study aimed to improve the correlation between EC and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months from August to December. The first approach involved stacking the data to identify the index with the strongest correlation with ground EC values, without the need for complex analysis or EC value ranging. In the second approach, the ground EC measurements were classified into seven different salinity categories, and the correlation coefficient was calculated between each index and each salinity category. The third approach analyzed the data temporally, considering different salinity levels. Lastly, a spatial correlation analysis was conducted between EC and index values in the fourth scenario. The initial approach revealed a weak correlation due to substantial variation in EC values. However, the SI index demonstrated the highest correlation coefficient of 0.38. In the second scenario, the S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the S1 index achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients compared to both the first and fourth scenarios. The study highlighted significant variations in EC levels related to land cover types and pond elevation. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

Preprint ARTICLE | doi:10.20944/preprints202402.1169.v1

Global Overview of Usable Landsat and Sentinel-2 Data for 1982–2023

Katarzyna Lewińska, Stefan Ernst, David Frantz, Ulf Leser, Patrick Hostert

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Data availability; aggregation; long-term analyses; terrestrial, vegetation, time series

Online: 21 February 2024 (04:17:00 CET)

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Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

Preprint ARTICLE | doi:10.20944/preprints202402.1125.v1

Water Quality of Eastern Mediterranean Catchments Subject to Different Environmental Pressures Employing Autonomous Unmanned Surface Vehicles (USVs)

Georgios Katsouras, Elias Dimitriou, Sotirios Karavoltsos, Stylianos Samios, Aikaterini Sakellari, Angeliki Mentzafou, Nikolaos Tsalas, Michael Scoullos

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; water quality; autonomous USV; inland and coastal waters; telemetric stations

Online: 20 February 2024 (11:49:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1854.v2

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Victor Oliveira Santos, Bruna Monallize Duarte Moura Guimarães, Iran Eduardo Lima Neto, Francisco de Assis De Souza Filho, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Chlorophyll-a; Sentinel-2 satellite; Machine learning; Freshwater Reservoirs; Eutrophication

Online: 20 February 2024 (11:44:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0987.v1

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-pixel Accuracy: Application to Digital Elevation Models

Serge Riazanoff, Axel Corseaux, Clément Albinet, Peter A. Strobl, Carlos López-Vázquez, Peter L. Guth, Takeo Tadono

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: linear correlation; NCC coefficient; image matching; disparity analysis; Digital Elevation Model; DEM

Online: 19 February 2024 (11:35:35 CET)

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Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

Preprint ARTICLE | doi:10.20944/preprints202402.0786.v1

AI-Based Tree Species Classification Using Pseudo Tree Crown Derived From UAV Imagery

Shengjie Miao, Kongwen Frank Zhang, Hongda Zeng, Jane Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Pseudo Tree Crown (PTC); PyTorch; Artificial Intelligence (AI); Unmanned Aerial Vehicle (UAV); individual tree species (ITS) classification

Online: 15 February 2024 (09:11:04 CET)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202402.0743.v1

Rubber Planting and Deforestation

Douglas Sheil, Adrià Descals, Erik Meijaard, David Gaveau

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; phenology; land-use-change; SE Asia; validation

Online: 15 February 2024 (03:14:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0821.v1

Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images

Justinas Lekavičius, Valentas Gružauskas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: deep learning; solar panels; semantic segmentation; data augmentation; generative adversarial network; remote sensing; transfer learning

Online: 14 February 2024 (16:32:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0616.v1

Shoreline Analysis of Sandbar Topography in the Nakdong River Estuary using SPOT Series Satellite Imagery

Sang-Hee Lee, Chang-Uk Hyun, Sung-Bo Kim

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: SPOT; satellite imagery; digital shoreline analysis system (DSAS); Nakdong River Estuary; sandbar; shoreline erosion

Online: 12 February 2024 (09:46:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0543.v1

Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product

Tobias Borsdorff, Mari Carmen Martinez-Velarte, Maarten Sneep, Mark ter Linden, Jochen Landgraf

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: TROPOMI; CH4; SUOMI NPP; VIIRS; CLOUDS; Machine Learning; Random Forest Classifier

Online: 9 February 2024 (10:52:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0554.v1

Responses of Geomorphic Indices to the Northward Migration of the Eastern Himalayan Syntaxis

Xuemin Pan, Xiaoming Shen, Zhiyuan He, Xiaoping Yuan, Yukui Ge, Xiong Wu, Yingying Jia, Xiudang Tang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Eastern Himalayan Syntaxis; geomorphic indices; erosion rates; northward migration

Online: 9 February 2024 (07:46:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0528.v1

Investigating the Influence of River Geomorphology on Human Presence Using Night Lights Data: A Case Study in the Indus Basin

Ekta Aggarwal, Alexander C. Whittaker, Sanjeev Gupta

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Night lights; DMSP; Floods; Channel-pattern; Indus; Anthropogenic activities; Geomorphology

Online: 8 February 2024 (14:07:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0356.v1

Performance Evaluation of Precise Point Positioning Based on Multiple Real-Time Augmentation Information

Mengjun Wu, Le Wang, Wei Xie, Fan Yue, Bobin Cui

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: real-time augmentation; evaluation; orbit and clock offset; precise point positioning

Online: 6 February 2024 (13:20:00 CET)

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The most commonly used real-time augmentation services in China are International GNSS Service (IGS) real-time service, PPP-B2b service, and BeiDou Satellite-Based Augmentation System (BDSBAS) dual-frequency augmentation service. However, the research on the performance evaluation, comparison and application scope of these three products is still unknown and unrevealed. This article introduces methods for obtaining real-time augmentation information and real-time orbit and clock offset recovery. Based on real-time orbit and clock offset accuracy, positioning accuracy, and positioning availability, it systematically evaluates the performance of CNES, PPP-B2b, and BDSBAS augmentation information. The results indicate that the radial accuracy of CNES and PPP-B2b real-time orbit products is consistent, and the Root Mean Square (RMS) is better than 5 cm. CNES can achieve centimeter-level accuracy in both along-track and cross-track components, surpassing the decimeter-level accuracy of PPP-B2b. Both services demonstrate consistent accuracy in real-time clock offset, with PPP-B2b showing similar standard deviations (STD) of 0.16 ns for different satellites. However, for CNES, the STD of real-time clock offset varies for GPS, BDS-3 MEO, and BDS-3 IGSO satellites, with values of 0.10 ns, 0.19 ns, and 0.60 ns, respectively. The accuracy and availability of the two PPP services exhibit the same levels, with centimeter-level accuracy achieved after convergence and positioning availability exceeding 99%. Therefore, the difference between the two services in application areas depends on the acquisition of augmentation information. However, BDSBAS, which concentrates on code observations, demonstrates lower accuracy in real-time orbit and clock offset, positioning accuracy, and positioning availability compared to the other two services. Its primary application is in the aviation and maritime domains, where there is a greater need for service integrity, continuity, and reliability.

Preprint ARTICLE | doi:10.20944/preprints202402.0267.v1

Aerial Biomass Estimation in the Cerrado Biome Using Canopy Height Data

Carlos Augusto Zangrando Toneli, Fernando Paiva Scardua, Rosana de Carvalho Cristo Martins, Eraldo Aparecido Trondoli Matricardi, Andressa Ribeiro, Antonio Carlos Ferraz Filho

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Canopy height; Total Biomass; Shrubland; Savanna and Forest

Online: 5 February 2024 (15:28:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0285.v1

UAV Photogrammetric Surveys for Trees’ Height Estimation

Giuseppina Vacca, Enrica Vecchi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV; photogrammetry; Precision Agriculture (PA); Environmental monitoring; DTM; Sfm; QGIS; CHM.

Online: 5 February 2024 (13:27:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0213.v1

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Wanqi Shi, Yeyu Xiang, Yuxuan Ying, Yuqin Jiao, Rui Zhao, Waishan Qiu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Carbon Emission; Residential; Neighborhood level; Street view images; Machine learning; Beijing

Online: 5 February 2024 (07:44:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.2068.v1

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, Robert J.W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Ocean Colour; Chlorophyll-a; Remote Sensing; Bayesian Neural Network

Online: 31 January 2024 (01:55:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.2109.v1

Estimation of Small Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch, Paweł Ćwiąkała

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: UAV; machine learning; deep learning; water surface elevation; photogrammetry

Online: 30 January 2024 (13:05:33 CET)

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Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of a terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and solution based on prediction of the weight mask by an encoder-decoder architecture, subse-quently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. Each sample corresponds to a 10 by 10 meter area of the stream channel and adjacent land. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. The proposed solution was compared with existing methods for measuring WSE in small streams using a drone. The results indicate that the solution outperforms previous photogrammetry-based methods and is second only to the radar-based method, which is considered the most accurate method available. The solution is characterized by a high degree of explainability and generalization.

Preprint ARTICLE | doi:10.20944/preprints202401.2041.v1

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In-Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

John Waczak, Adam Aker, Lakitha O. H. Wijeratne, Shawhin Talebi, Bharana Fernando, Prabuddha Hathurusinghe, Mazhar Iqbal, David Schaefer, David J. Lary

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Water Quality; Robotic Teams; Hyperspectral Imaging; Machine Learning; Conformal Prediction

Online: 29 January 2024 (14:41:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1976.v1

Multiparametric Apparatus for Real-Time Observations of Volcanic Aerosol Optical and Microphysical Properties at Etna (Italy): Calibration Procedures and Measurement Tests

Matteo Manzo, Gianpiero Aiesi, Antonella Boselli, Salvatore Consoli, Riccardo Damiano, Guido Di Donfrancesco, Benedetto Saraceno, Simona Scollo

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Lidar; Aerosol parameters; Calibration.

Online: 29 January 2024 (09:54:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.2006.v1

Spatiotemporal Variability of Vegetation NDVI in the Huaihe River Basin, China: Driving Force Analysis and Ecological Implications

Xinyu Wang, Shibo Fang, Yanling Li, Chao Dong, Li Sun

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Spatiotemporal Variability; Ecological Drivers; NDVI; Geodetector; Huaihe River Basin

Online: 29 January 2024 (09:27:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1879.v1

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

Abhishek Rawat, Prasun Kumar Gupta, Claudio Persello

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: fractional mapping; built-up; deep learning; Global Human Settlement Layer; Sustainability

Online: 26 January 2024 (09:41:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1839.v1

Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data

Carlos E. Campoverde, Mila Koeva, Claudio Persello, Konstantin Maslov, Weiqin Jiao, Dessislava Petrova-Antonova

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: roof structure extraction; image processing; deep-learning; HEAT; 3D modelling; LOD2

Online: 25 January 2024 (14:06:52 CET)

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Mapping building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep learning models have been the main focus of most recent research endeavours aiming to extract pixel-based building roof plane areas from remote sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pix-el-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: Stadsveld – 't Zwering neighbourhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld – 't Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Preprint ARTICLE | doi:10.20944/preprints202401.1765.v1

FCAE-DCAC: A Novel Fully Convolutional Auto-Encoder based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

Rui Zhao, Zhiwei Yang, Xiangchao Meng, Feng Shao

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: hyperspectral imagery; anomaly detection; self-supervised learning; fully convolutional auto-encoder; latent feature adversarial consistency; triplet loss

Online: 24 January 2024 (10:42:19 CET)

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With the development of artificial intelligence, the ability of capturing the background characteristics of hyperspectral imagery (HSI) is improved and promising performance in hyperspectral anomaly detection (HAD) tasks is yielded. However, existing methods proposed in recent years still suffer from certain limitations: 1) constraints are lacking in the deep feature learning process in terms of the issue for absence of prior background and anomaly information. 2) hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. 3) architectures of fully connected deep network in hyperspectral anomaly detectors lead to low utilization of spatial information and destruction for original spatial relationship of hyperspectral imagery, and disregard spectral correlation between adjacent pixels. 4) hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. With the consideration of above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD which is carried out in a self-supervised learning-based processing. Firstly, the density-based spatial clustering of applications with noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with spatial-spectral joint attention (SSJA) is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network is proposed to achieve pure background reconstruction with the ability of learning actual background distribution in hyperspectral imagery. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method on seven groups of real-world hyperspectral datasets, and the experimental results confirmed the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

Preprint ARTICLE | doi:10.20944/preprints202401.1569.v1

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

Dong-Dong Zhang, Jing Xu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: surface water dynamics; random forest classification; driving mechanism; Yangtze River Basin; Landsat remote sensing image; GEE

Online: 23 January 2024 (03:40:10 CET)

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In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Preprint TECHNICAL NOTE | doi:10.20944/preprints202401.1460.v1

A Modified Aeromagnetic Compensation Method Robust to In-Cabin OBE Interferences

Yuxin Liu, Wen Li, Dongyan Wei, Ge Shen

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: aeromagnetic compensation; magnetic survey; magnetic interference; on-board electronic systems; geomagnetic navigation

Online: 19 January 2024 (07:09:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1426.v1

Applications of Ground-Based Infrared Cameras for Remote Sensing of Volcanic Plumes

Fred Prata, Stefano Corradini, Riccardo Biondi, Lorenzo Guerrieri, Luca Merucci, Andrew Prata, Dario Stelitano

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Infrared cameras; volcanic emissions; hazard assessment

Online: 19 January 2024 (04:39:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1362.v1

Temporal and Spatial Analyses of Forest Burnt Area in the Middle Volga Region Based on the Satellite Imagery and Climatic Factors

Eldar Kurbanov, Oleg Vorobev, Sergey Lezhnin, Denis Dergunov, Jinliang Wang, Jinming Sha, Aleksandr Gubaev, Ludmila Tarasova, Yibo Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: wildfires; forest; climatic factors; monitoring; fire recurrence; remote sensing; machine learning; Landsat; time series; trend analyses

Online: 18 January 2024 (10:29:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0500.v2

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: 18 January 2024 (08:05:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1329.v1

Research on Estimation Model for Cotton Canopy Structure Parameters Based on Hyperspectral Vegetation Index

Yaqin Qi, Xi Chen, Zhengchao Chen, Xin Zhang, Congju Shen, Yan Chen, Yuanying Peng, Bing Chen, Qiong Wang, Taijie Liu, Hao Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyper-spectral Remote Sensing; cotton canopy information; Vegetation Index; estimation model

Online: 18 January 2024 (03:24:15 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1274.v1

Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning

Wenhui Song, Xin Zhang, Yijin Chen, Hanghang Xu, Lianchao Wang, Yinggang Wang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: hyperspectral remote sensing; manifold learning; local tangent space alignment; spectral angle mapping; spectral information divergence; dimensionality reduction; feature extraction

Online: 17 January 2024 (06:28:26 CET)

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With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Preprint ARTICLE | doi:10.20944/preprints202401.1272.v1

An Instrument Error Correlation Model for GNSS Reflectometry

C. E. Powell, Christopher S. Ruf, Darren S. McKague, Tianlin Wang, Anthony Russel

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GNSS; reflectometry; observation error; correlated error

Online: 17 January 2024 (06:21:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1237.v1

Spatiotemporal Evolution of the Land Cover over Deception Island, Antarctica, Its Driving Mechanisms and Its Impact on the Shortwave Albedo

Javier F. Calleja, Rubén Muñiz, Jaime Otero, Francisco Navarro, Carleen Reijmer, Miguel Ángel De Pablo, Susana del Carmen Fernández

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: cryosphere; albedo; landscape; air temperature; solar radiation

Online: 16 January 2024 (12:36:52 CET)

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The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300-3000 nm) from a spaceborne sensor and from field surveys to calculate the monthly relative abundance of landscape units. Field albedo data were collected using a portable albedometer over seven landscape units: clean fresh snow; clean old snow; rugged landscape composed of dirty snow with disperse pyroclasts and rocky outcrops; dirty snow; stripes of bare soil and snow; shallow snow with small bare soil patches; bare soil. MODIS MCD43A3 daily albedo product was downloaded using the Google Earth Engine API from the 2000-2001 season to the 2020-2021 season. Each landscape unit was characterized by an albedo normal distribution. The monthly relative abundance of the landscape units were calculated by fitting a linear combination of the normal distributions to the histogram of the MODIS monthly mean albedo. The monthly relative abundance of the landscape unit consisting of rugged landscape composed of dirty snow, with dispersed clasts and small rocky outcrops exhibits a high linear positive correlation with the monthly mean albedo (R2=0.87) and a high linear negative correlation with the monthly mean air temperature (R2= 0.69). The increase of the solar radiation energy flux from September to December coincides with the decrease of the relative abundance of the landscape unit composed of dirty snow with dispersed clasts and small rocky outcrops. We propose a mechanism to describe the evolution of the landscape: uncovered pyroclasts act as melting centres favouring the melting of surrounding snow. Ash does not play a decisive role in the melting of the snow. The results also explain the observed decreasing of the thaw depth of the permafrost on the island in the period 2006-2014, resulting from an increase in the snow cover over the whole island.

Preprint ARTICLE | doi:10.20944/preprints202401.1192.v1

Complex Habitats Deconstruction and Low Altitude Remote Sensing Recognition of Tobacco Cultivation on Karst Mountainous

Youyan Huang, Lihui Yan, Zhongfa Zhou, Denghong Huang, Qianxia Li, Lu Cai, Fuxianmei Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: U-Net model; complex habitat; plant identification; UAV remote sensing; Karst Mountainous

Online: 16 January 2024 (08:26:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1128.v1

Exploring NDVI Variation across Seasons in Parbhani, Maharashtra: A Remote Sensing Perspective

Karnraj Dhavan, Megha Jagtap, Gopal Shinde, Mahesh Palve, Anand Hivre

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Temporal variation; Vegetation Index; NDVI; Landsat 9

Online: 15 January 2024 (13:39:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1080.v1

How Satellite Altimetry Data Can Help Us Understand Sea Level Fluctuations and Classify Water Drainage Areas in the Caspian Sea

Alireza A. Ardalan, Asiyeh Hashemifaraz

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Sea level fluctuations and trend; Inland water bodies; Caspian Sea; TOPEX/Poseidon ; Jason1 ; Jason2 ; Jason3 satellite altimetry data; Water drainage areas; Global warming and local climate changes

Online: 15 January 2024 (08:54:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0892.v1

A New Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream CNN and Attention Module

Seyd Teymoor Seydi, Mahboubeh Boueshagh, Foad Namjoo, Seyed Mohammad Minouei, Zahir Nikraftar, Meisam Amani

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Land cover analysis; remote sensing; change detection; hyperspectral; deep learning; convolutional neural networks (CNN); Squeeze-and-Excitation (SE); AVIRIS

Online: 11 January 2024 (08:03:44 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0819.v1

Geospatial Modeling of Soil Moisture on a Restored Wet Meadow

Yetunde Oladepe Rotimi, Jennie DeMarco, Philip Crossley, Teresa Chapman, Ian K. Breckheimer

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Gravimetric soil moisture; Multispectral images; Satellite images; Uncrewed aircraft systems; Vegetation indices; Volumetric soil moisture; Wetland restoration

Online: 11 January 2024 (02:25:40 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0644.v1

Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Estimated by Thermal-Infrared Remote Sensing

Gengle Zhao, Lisheng Song, Long Zhao, Sinuo Tao

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: ET; TSEB; reconstruction; machine learning; comparison

Online: 9 January 2024 (04:46:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0621.v1

CO₂ Capture Capacity Measurement Using Multitemporal Analysis and Biophysical Variables in a Tropical Humid Forest in Colombian Andes

Lina Patricia Vega, Diego Felipe Garcia, Ronal Sierra-Parada, Ivan Pirazan

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: spatiotemporal analysis; land cover; geographic information systems; aboveground biomass; carbon stock; remote sensing data; vegetation indices; Colombia

Online: 8 January 2024 (13:57:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0341.v2

Assessing Conservation Conditions at La Fortaleza de Kuelap, Peru, based on Integrated Close-Range Remote Sensing and Near-Surface Geophysics

Ivan Ghezzi, Jacek Kościuk, Warren Church, Parker VanValkenburgh, Bartłomiej Ćmielewski, Matthias Kucera, Paweł B. Dąbek, Jeff Contreras, Nilsson Mori, Giovanni Righetti, Stefano Serafini, Carol Rojas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LiDAR, terrestrial laser scanning, hydrological analysis, electrical resistivity, seismic refraction, photogrammetry, GIS, near-surface geophysics, close-range remote sensing, risk map, archaeology, multi-data integration, heritage conservation, Peru

Online: 8 January 2024 (11:35:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0559.v1

SNOWTRAN: A Fast UV-VNIR-SWIR Radiative Transfer Model for Polar Hyperspectral Remote Sensing Applications

Alexander Kokhanovsky, Maximillian Brell, Karl Segl, Sabine Chabriallat

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: remote sensing; snow; radiative transfer

Online: 8 January 2024 (11:15:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0545.v1

Forestry Applications of Space-borne LiDAR Sensors: A Worldwide Bibliometric Analysis

Fernando J. Aguilar, Francisco A Rodríguez, Manuel A. Aguilar, Abderrahim Nemmaoui, Flor Álvarez-Taboada

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: space-borne LiDAR; ICESat; GEDI; forest; AGB; remote sensing; bibliometric analysis; Scopus

Online: 8 January 2024 (10:02:03 CET)

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The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. LiDAR was initially developed to integrate laser-focused imaging with the capability to determine distances through the measurement of signal return times, utilizing suitable sensors and data acquisition electronics. Nowadays, these sensors have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, and estimating above-ground biomass (AGB) across local to regional scales. This work aims to analyze the scientific impact of these sensors on large-scale for-est mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. In this way, a worldwide bibliometric analysis of this topic was carried out based on up to 412 publications in-dexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commis-sioning of two new LiDAR space missions: Ice, Cloud and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions are providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing," and among the leading contributors, the top five countries in terms of publica-tions were the USA, China, the UK, France, and Germany. In the realm of prominent research in-stitutions, France boasted six, the USA had four, China had three, while the UK and Canada each had one. The upward trajectory in the number of publications recorded from 2004 to 2022 catego-rizes the subject under investigation as a highly trending research topic, particularly within the context of enhancing the administration of forest resources and engaging in global climate treaty frameworks mandating the surveillance and reporting of carbon stocks in forests. The recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), along with the planned launch in the coming years of up to three new space sensors, such as the Multi-footprint Observation LiDAR and Im-ager (Japan Aerospace Exploration Agency), the BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and the LiDAR Surface Topography (LIST; NASA), will greatly contribute to expanding the ability to map and monitor forest systems at very large scales. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Preprint ARTICLE | doi:10.20944/preprints202401.0500.v1

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at Daily and Sub-daily scale in the Western Mediterranean.

Eric Peinó, Joan Bech, Mireia Udina, Francesc Polls

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: GPM IMERG; extreme precipitation; cloud microphysics; NWC SAF; PMW sources; precipitation intensity, amount, and duration

Online: 5 January 2024 (14:12:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0413.v1

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

Debasish Roy, Bappa Das, Pooja Rathore, Priyabrata Santra, Shovik Deb, Bimal K. Bhattacharya, Ajit Govind, Raguveer Singh Jatav, Deepak Sethi, Tridiv Ghosh, Joydeep Mukherjee, Vinay Kumar Sehgal, Sheshakumar Goroshi, Debashis Chakraborty

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LST downscaling; TsHARP; TPS; Random forest; spatial downscaling, agro-climate

Online: 5 January 2024 (09:28:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0390.v1

Unmanned Airborne Bistatic InSAR Data Processing Method Using Bi-Directional Synchronization Chain Signals

Jinbiao Zhu, Bei Lin, Jie Pan, Cheng Yao, Xiaolan Qiu, Wen Jiang, Yuquan Liu, Mingqian Liu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: InSAR; Dual-station SAR; UAV-borne SAR; Synchronization signal; SAR imaging

Online: 4 January 2024 (15:35:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0341.v1

Assessing Conservation Conditions at La Fortaleza De Kuelap, Peru Based on Integrated Close-Range Remote Sensing and Near-Surface Geophysics

Ivan Ghezzi, Jacek Kościuk, Warren Church, Parker VanValkenburgh, Bartłomiej Ćmielewski, Matthias Kucera, Paweł B. Dąbek, Jeff Contreras, Nilsson Mori, Giovanni Righetti, Stefano Serafini, Carol Rojas

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: LiDAR; terrestrial laser scanning; hydrological analysis; electrical resistivity; seismic refraction; photogrammetry; GIS; near-surface geophysics; close-range remote sensing; risk map; archaeology

Online: 4 January 2024 (11:25:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0033.v1

Spectral-Spatial Feature Fusion for Hyperspectral Anomaly Detection

Shaocong Liu, Zhen Li, Guangyuan Wang, Xianfei Qiu, Tinghao Liu, Jing Cao, Donghui Zhang

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Hyperspectral image; isolation forest; local saliency detection; anomaly detection; spectralspatial fusion

Online: 3 January 2024 (08:11:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0010.v1

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

Manuel de Figueiredo Meyer, José Alberto Gonçalves, Ana Maria Ferreira Bio

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Carpobrotus edulis; Unoccupied Aerial Vehicle; Above-ground Biomass; GIS; QGIS; Landcover Classification

Online: 3 January 2024 (02:10:53 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202401.0057.v1

Use of Remote Sensing and Biogeochemical Modelling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

Marta Chiesi, Luca Fibbi, Silvana Vanucci, Fabio Maselli

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest ecosystem; GPP; NEP; Modified C-Fix; BIOME-BGC.

Online: 2 January 2024 (08:36:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1950.v1

A Rapid Geolocation Algorithm for Three-axis Stabilization Geostationary Remote Sensing Satellite

He Li, Xiaochong Tong, Chunping Qiu, Congzhou Guo, Jian Shang, Zhichao Wang, Yuekun Sun

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Three-axis Stabilization; Geostationary orbit; Remote Sensing Satellite; Nominal Grid; Coordination Group for Meteorological Satellites; Rapid Geolocation Algorithm

Online: 28 December 2023 (02:44:00 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1821.v1

Urban–Rural Boundary Delineation Based on Population Spatialization: A Case Study of Guizhou Province, China

Hong Wang, Tian Xiao Yu, Yin Lv Luo, Rong Li

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: urban– rural boundary demarcation; population spatialization; dasymetric mapping; breaking point

Online: 27 December 2023 (05:26:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2001.v1

Comparison Between Post-Fire Analysis and Pre-Fire Risk Assessment Produced from Various Geospatial Data

Cumhur Güngöroğlu, İrem İsmailoğlu, Bekir Kapukaya, Orkan Ozcan, Mustafa Yanalak, Nebiye Musaoğlu

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: forest fire; remote sensing; risk assessment; burnt analysis; fuzzy analytic hierarchy
process (FAHP)

Online: 26 December 2023 (14:31:29 CET)

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Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Preprint ARTICLE | doi:10.20944/preprints202312.1906.v1

Evaluation of Handheld Mobile Laser Scanner Systems for the Definition of Fuel Types in Structurally Complex Mediterranean Forest Stands

Raúl Hoffrén, María Teresa Lamelas, Juan de la Riva

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Wildfires; Fuel heterogeneity; HMLS; Prometheus fuel model; Fire modelling; Voxels

Online: 26 December 2023 (11:27:47 CET)

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The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow understanding and modelling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from different types and limitations of qualitative field observation and passive and active airborne remote sensing. This can impact the performance of classification models that rely on in situ identification of fuel type as ground-truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system were used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m2) which were discretized into voxels of 0.05 m3. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m3/m2) for each plot at centimetric scale. Additionally, fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) for each plot. The Prometheus fuel types were satisfactory identified for each plot, which was compared with the fuel type estimated in the field. This led to the modification of the ground-truth in 10 out of the 43 plots, with estimation errors between types 2–3, 5–6, and 6–7. These results demonstrate the ability of HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in Mediterranean forests, making them powerful tools for fuel mapping and fire modelling, and ultimately for improving wildfire prevention and forest management.

Preprint ARTICLE | doi:10.20944/preprints202310.0901.v2

Can Land-Use Land-Cover Change Explain the Reduced Resilience in Forests?

Sara Alibakhshi, Hossein Azadi, Leonardo Espinosa-Leal

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: land-use and land cover-change; monitoring ecosystem dynamics; remote sensing; Mangrove forests

Online: 26 December 2023 (08:50:59 CET)

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Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.

Preprint ARTICLE | doi:10.20944/preprints202312.1854.v1

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Victor Oliveira Santos, Bruna Monallize Duarte Moura Guimarães, Iran Eduardo Lima Neto, Francisco de Assis Souza Filho, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: Chlorophyll-a; Sentinel-2 satellite; Machine learning; Freshwater Reservoirs; Eutrophication

Online: 25 December 2023 (13:20:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1791.v1

Interconnections of Coal and Forest Fires in Siberia and Australia: Satellite CO Measurements

Leonid Yurganov

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: wildfires; coal fires; carbon monoxide; remote sensing

Online: 25 December 2023 (11:22:50 CET)

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Environmental and Earth Sciences