DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images
Accurate estimation of forest above-ground biomass (AGB) is essential for ecosystem conservation, sustainable forest management, and mitigating climate change and wildfire risks. Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioF...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002869 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233618209079296 |
|---|---|
| author | Abdul Hanan Mehak Khan Nieves Fernandez-Anez Reza Arghandeh |
| author_facet | Abdul Hanan Mehak Khan Nieves Fernandez-Anez Reza Arghandeh |
| author_sort | Abdul Hanan |
| collection | DOAJ |
| description | Accurate estimation of forest above-ground biomass (AGB) is essential for ecosystem conservation, sustainable forest management, and mitigating climate change and wildfire risks. Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioFusion, a multi-modal deep learning framework that first estimates AGB for validation as ground truth generation by using LiDAR-derived tree heights and a Tree Species map, employing allometry equations to relate tree height to Diameter at Breast Height (DBH). After this initial estimation, the framework is trained to predict AGB using high-resolution optical imagery and multiple bands of Synthetic Aperture Radar (SAR), including X, C, and L bands. The use of SAR bands enables improved canopy penetration, particularly in dense and cloud-covered forests. DeepBioFusion leverages the complementary strengths of SAR and optical data to enhance the accuracy of biomass predictions. Benchmarking against models like ResNet50 and Transformer, the proposed model demonstrates superior performance in AGB estimation across diverse forest environments. This study offers a scalable, cutting-edge approach to biomass monitoring, advancing efforts in climate change mitigation and sustainable forest management. |
| format | Article |
| id | doaj-art-e0096455a5964be8a26b99b87c3a6150 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-e0096455a5964be8a26b99b87c3a61502025-08-20T05:05:31ZengElsevierEcological Informatics1574-95412025-12-019010327710.1016/j.ecoinf.2025.103277DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite imagesAbdul Hanan0Mehak Khan1Nieves Fernandez-Anez2Reza Arghandeh3Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, 5020, Norway; Corresponding author.Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, 5020, NorwayDepartment of Safety, Chemistry and Biomedical Laboratory Sciences, Western Norway University of Applied Sciences, Haugesund, 5528, NorwayDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, 5020, NorwayAccurate estimation of forest above-ground biomass (AGB) is essential for ecosystem conservation, sustainable forest management, and mitigating climate change and wildfire risks. Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioFusion, a multi-modal deep learning framework that first estimates AGB for validation as ground truth generation by using LiDAR-derived tree heights and a Tree Species map, employing allometry equations to relate tree height to Diameter at Breast Height (DBH). After this initial estimation, the framework is trained to predict AGB using high-resolution optical imagery and multiple bands of Synthetic Aperture Radar (SAR), including X, C, and L bands. The use of SAR bands enables improved canopy penetration, particularly in dense and cloud-covered forests. DeepBioFusion leverages the complementary strengths of SAR and optical data to enhance the accuracy of biomass predictions. Benchmarking against models like ResNet50 and Transformer, the proposed model demonstrates superior performance in AGB estimation across diverse forest environments. This study offers a scalable, cutting-edge approach to biomass monitoring, advancing efforts in climate change mitigation and sustainable forest management.http://www.sciencedirect.com/science/article/pii/S1574954125002869Above Ground BiomassSynthetic Aperture Radar (SAR)Machine learningMulti-modalDiameter at Breast Height (DBH)Optical and SAR fusion |
| spellingShingle | Abdul Hanan Mehak Khan Nieves Fernandez-Anez Reza Arghandeh DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images Ecological Informatics Above Ground Biomass Synthetic Aperture Radar (SAR) Machine learning Multi-modal Diameter at Breast Height (DBH) Optical and SAR fusion |
| title | DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images |
| title_full | DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images |
| title_fullStr | DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images |
| title_full_unstemmed | DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images |
| title_short | DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images |
| title_sort | deepbiofusion multi modal deep learning based above ground biomass estimation using sar and optical satellite images |
| topic | Above Ground Biomass Synthetic Aperture Radar (SAR) Machine learning Multi-modal Diameter at Breast Height (DBH) Optical and SAR fusion |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002869 |
| work_keys_str_mv | AT abdulhanan deepbiofusionmultimodaldeeplearningbasedabovegroundbiomassestimationusingsarandopticalsatelliteimages AT mehakkhan deepbiofusionmultimodaldeeplearningbasedabovegroundbiomassestimationusingsarandopticalsatelliteimages AT nievesfernandezanez deepbiofusionmultimodaldeeplearningbasedabovegroundbiomassestimationusingsarandopticalsatelliteimages AT rezaarghandeh deepbiofusionmultimodaldeeplearningbasedabovegroundbiomassestimationusingsarandopticalsatelliteimages |