Allometric Models for Estimation of Forest Biomass in North East India

• Main Authors: Arun Jyoti Nath, Brajesh Kumar Tiwari, Gudeta W Sileshi, Uttam Kumar Sahoo, Biplab Brahma, Sourabh Deb, Ningthoujam Bijayalaxmi Devi, Ashesh Kumar Das, Demsai Reang, Shiva Shankar Chaturvedi, Om Prakash Tripathi, Dhruba Jyoti Das, Asha Gupta
• Format: Article
• Language: English
• Published: MDPI AG 2019-01-01
• Series: Forests
• Subjects: Biomass estimation models; forest ecosystems; remote sensing; winners curse
• Online Access: https://www.mdpi.com/1999-4907/10/2/103

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balances in such systems. The objective of this study was to develop generalized biomass models using harvest data covering tropical semi-evergreen, tropical wet evergreen, sub-tropical broad leaved, and sub-tropical pine forest in North East India (NEI). Among the four biomass estimation models (BEMs) tested <i>AGB_est</i> = 0.32(D²Hδ)^0.75 × 1.34 and <i>AGB_est</i> = 0.18<i>D</i>^2.16 × 1.32 were found to be the first and second best models for the different forest types in NEI. The study also revealed that four commonly used generic models developed by Chambers (2001), Brown (1989), Chave (2005) and Chave (2014) overestimated biomass stocks by 300–591 kg tree⁻¹, while our highest rated model overestimated biomass by 197 kg tree⁻¹. We believe the BEMs we developed will be useful for practitioners involved in remote sensing, biomass estimation and in projects on climate change mitigation, and payment for ecosystem services. We recommend future studies to address country scale estimation of forest biomass covering different forest types.