Spaceborne LiDAR Reveals Anthropogenic and Biophysical Drivers Shaping the Spatial Distribution of Forest Aboveground Biomass in Eastern Himalayas

Abhilash Dutta Roy (), Abraham Ranglong, Sandeep Timilsina, Sumit Kumar Das, Michael S. Watt, Sergio de-Miguel, Sourabh Deb, Uttam Kumar Sahoo and Midhun Mohan
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Abhilash Dutta Roy: Ecoresolve, San Francisco, CA 94105, USA
Abraham Ranglong: Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, Tripura 799202, India
Sandeep Timilsina: Mediterranean Forestry and Natural Resources Management, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Sumit Kumar Das: Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, Tripura 799202, India
Michael S. Watt: Scion, Christchurch 8011, New Zealand
Sergio de-Miguel: Department of Agricultural and Forest Sciences and Engineering, University of Lleida, 25198 Lleida, Spain
Sourabh Deb: Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, Tripura 799202, India
Uttam Kumar Sahoo: Department of Forestry, Mizoram University, Aizawl 796004, India
Midhun Mohan: Ecoresolve, San Francisco, CA 94105, USA

Land, 2025, vol. 14, issue 8, 1-25

Abstract: The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha −1 ) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha −1 ) and Manipur the lowest (102.8 Mg ha −1 ). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha −1 ). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R 2 = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation.

Keywords: GEDI; remote sensing; biomass mapping; Northeast India; carbon sequestration; disturbance impacted forest productivity; random forest; South Asia (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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