Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud
The estimation of Forest above-ground biomass (AGB) is critical for comprehending forest ecosystems and promoting biodiversity restoration. The study was conducted to develop an effective approach to predict Forest Above-Ground Biomass (AGB) using Machine Learning, Image Classification, and GEE open...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Forest Science and Technology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21580103.2025.2481122 |
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| author | Chiranjit Singha Kishore Chandra Swain Satiprasad Sahoo Ayad M. Fadhil Al-Quraishi Joseph Omeiza Alao Hussein Almohamad Mohamed Fatahalla Mohamed Ahmed Hazem Ghassan Abdo |
| author_facet | Chiranjit Singha Kishore Chandra Swain Satiprasad Sahoo Ayad M. Fadhil Al-Quraishi Joseph Omeiza Alao Hussein Almohamad Mohamed Fatahalla Mohamed Ahmed Hazem Ghassan Abdo |
| author_sort | Chiranjit Singha |
| collection | DOAJ |
| description | The estimation of Forest above-ground biomass (AGB) is critical for comprehending forest ecosystems and promoting biodiversity restoration. The study was conducted to develop an effective approach to predict Forest Above-Ground Biomass (AGB) using Machine Learning, Image Classification, and GEE open-source fast processing system in the Similipal Tiger Reserve (STR), India. The study utilized six machine learning models and integrated various data sources, including Sentinel-1 and Sentinel-2 imagery, forest canopy height data from NASA’s GEDI Global Ecosystems Dynamics Investigation-LiDAR, geo-environmental Shuttle Radar Topography Mission (SRTM) data, and Climate Hazards Group Infrared precipitation with Station (CHIRPS) data. Sentinel-based optical and Synthetic Aperture Radar (SAR) signatures were also extracted before and after the monsoon season to evaluate AGB in a subtropical region. The Random forest-based Boruta method was used to examine the importance of multiple factors contributing to the prediction’s accuracy. In addition, the assessment of multicollinearity, which is accomplished by measuring the variance inflation factor (VIF), was carried out to address the issue of interrelatedness among variables that could affect the accuracy of the AGB mapping. The Random Forest model exhibited superior accuracy compared to other models, achieving an R2 of 0.71, MAE of 16.12Mg/ha, RMSD of 22.27Mg/ha, NRMSD of 0.212, and AUROC of 75%. The scatterplot analysis revealed a positive correlation between forest biomass and factors, such as forest canopy height, elevation, normalized differential vegetation index, normalized differential moisture index, and the ratio of VV/VH after the monsoon season. The VIF values ranged between 1.12 and 8.73, with VH_Jan_Mar having the maximum VIF and forest canopy height having the minimum VIF. As per the Boruta algorithm, 16 attributes were deemed important, while 14 attributes had less influence on AGB. The study presented a novel approach for estimating biomass in subtropical regions using remote sensing data set and machine learning models in Google platform. These results can be successfully used by the planners of STR for monitoring variation in AGB ensuring better habitat for wild animals. |
| format | Article |
| id | doaj-art-51ee3db6409141d5bfa363659e7b74f4 |
| institution | Kabale University |
| issn | 2158-0103 2158-0715 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Forest Science and Technology |
| spelling | doaj-art-51ee3db6409141d5bfa363659e7b74f42025-08-20T03:45:15ZengTaylor & Francis GroupForest Science and Technology2158-01032158-07152025-04-0121218720610.1080/21580103.2025.2481122Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloudChiranjit Singha0Kishore Chandra Swain1Satiprasad Sahoo2Ayad M. Fadhil Al-Quraishi3Joseph Omeiza Alao4Hussein Almohamad5Mohamed Fatahalla Mohamed Ahmed6Hazem Ghassan Abdo7Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, Birbhum, West Bengal, IndiaDepartment of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, Birbhum, West Bengal, IndiaInternational Center for Agricultural Research in the Dry Areas (ICARDA), Maadi, Cairo, EgyptPetroleum and Mining Engineering Department, Faculty of Engineering, Tishk International University, Erbil, Kurdistan Region, IRAQDepartment of Physics, Air Force Institute of Technology, Kaduna, NigeriaDepartment of Geography, College of Languages and Human Sciences, Qassim University, Buraydah, Saudi ArabiaDepartment of Social Sciences, Faculty of Arts, University of Hail, Hail, Saudi ArabiaGeography Department, Faculty of Arts and Humanities, Tartous University, Tartous, Syria.The estimation of Forest above-ground biomass (AGB) is critical for comprehending forest ecosystems and promoting biodiversity restoration. The study was conducted to develop an effective approach to predict Forest Above-Ground Biomass (AGB) using Machine Learning, Image Classification, and GEE open-source fast processing system in the Similipal Tiger Reserve (STR), India. The study utilized six machine learning models and integrated various data sources, including Sentinel-1 and Sentinel-2 imagery, forest canopy height data from NASA’s GEDI Global Ecosystems Dynamics Investigation-LiDAR, geo-environmental Shuttle Radar Topography Mission (SRTM) data, and Climate Hazards Group Infrared precipitation with Station (CHIRPS) data. Sentinel-based optical and Synthetic Aperture Radar (SAR) signatures were also extracted before and after the monsoon season to evaluate AGB in a subtropical region. The Random forest-based Boruta method was used to examine the importance of multiple factors contributing to the prediction’s accuracy. In addition, the assessment of multicollinearity, which is accomplished by measuring the variance inflation factor (VIF), was carried out to address the issue of interrelatedness among variables that could affect the accuracy of the AGB mapping. The Random Forest model exhibited superior accuracy compared to other models, achieving an R2 of 0.71, MAE of 16.12Mg/ha, RMSD of 22.27Mg/ha, NRMSD of 0.212, and AUROC of 75%. The scatterplot analysis revealed a positive correlation between forest biomass and factors, such as forest canopy height, elevation, normalized differential vegetation index, normalized differential moisture index, and the ratio of VV/VH after the monsoon season. The VIF values ranged between 1.12 and 8.73, with VH_Jan_Mar having the maximum VIF and forest canopy height having the minimum VIF. As per the Boruta algorithm, 16 attributes were deemed important, while 14 attributes had less influence on AGB. The study presented a novel approach for estimating biomass in subtropical regions using remote sensing data set and machine learning models in Google platform. These results can be successfully used by the planners of STR for monitoring variation in AGB ensuring better habitat for wild animals.https://www.tandfonline.com/doi/10.1080/21580103.2025.2481122Forest above-ground biomassbiospheremachine learningsentinel imageryenvironment |
| spellingShingle | Chiranjit Singha Kishore Chandra Swain Satiprasad Sahoo Ayad M. Fadhil Al-Quraishi Joseph Omeiza Alao Hussein Almohamad Mohamed Fatahalla Mohamed Ahmed Hazem Ghassan Abdo Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud Forest Science and Technology Forest above-ground biomass biosphere machine learning sentinel imagery environment |
| title | Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud |
| title_full | Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud |
| title_fullStr | Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud |
| title_full_unstemmed | Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud |
| title_short | Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud |
| title_sort | predicting forest above ground biomass using sar imagery and gedi data through machine learning in gee cloud |
| topic | Forest above-ground biomass biosphere machine learning sentinel imagery environment |
| url | https://www.tandfonline.com/doi/10.1080/21580103.2025.2481122 |
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