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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/18253
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dc.contributor.authorLeena, Chumbithaen_US
dc.contributor.authorKhati, Unmeshen_US
dc.date.accessioned2026-05-14T12:28:20Z-
dc.date.available2026-05-14T12:28:20Z-
dc.date.issued2025-
dc.identifier.citationLeena, C., Khati, U., & Kumar, S. (2025). ESTIMATION OF LOW BIOMASS IN FOREST ECOSYSTEMS FOR LARGE-SCALE MAPPING USING SENTINEL-1 C-BAND SAR AND LIDAR DATA. International Geoscience and Remote Sensing Symposium (IGARSS)  , 3572–3575. https://doi.org/10.1109/IGARSS55030.2025.11242587en_US
dc.identifier.issn2153-6996-
dc.identifier.otherEID(2-s2.0-105033538926)-
dc.identifier.urihttps://dx.doi.org/10.1109/IGARSS55030.2025.11242587-
dc.identifier.urihttps://dspace.iiti.ac.in:8080/jspui/handle/123456789/18253-
dc.description.abstractAccurate estimation of low biomass in forest ecosystems is crucial for understanding forest dynamics, biodiversity, and carbon cycles. This study explores the use of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to estimate above-ground biomass (AGB) in low biomass forests. By integrating field data, LiDAR-derived metrics, and SAR backscatter, we apply the Water Cloud Model (WCM) and a linear regression approach to characterize biomass distribution. Validation results highlight the strengths and limitations of both methods, emphasizing their applicability for large-scale biomass mapping. ©2025 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceInternational Geoscience and Remote Sensing Symposium (IGARSS)en_US
dc.titleESTIMATION OF LOW BIOMASS IN FOREST ECOSYSTEMS FOR LARGE-SCALE MAPPING USING SENTINEL-1 C-BAND SAR AND LIDAR DATAen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Astronomy, Astrophysics and Space Engineering

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