Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar

Remote Sensing

Cຝric Véga²  Udayalakshmi Vepakomma³  Jules Morel¹  Jean-Luc Bader¹  Gopalakrishnan Rajashekar⁶  Chandra Shekhar Jha⁶  Jérôme Ferêt¹  Christophe Proisy⁴  Raphaël Pélissier¹  Vinay Kumar Dadhwal⁶  Parth Sarathi Roy⁵ 

[1] Institut Français de Pondichéry, UMIFRE CNRS-MAEE 21, Pondicherry 605001, India; E-Mails:;Laboratoire de l’Inventaire Forestier, Institut National de l’Information Géographique et Forestière, 54000 Nancy, France;FPInnovations, 570 Saint-Jean Boulevard, Pointe-Claire, Montrea, QC H9R 3J9, Canada; E-Mail:;IRD, UMR AMAP, F-34000 Montpellier, France; E-Mails:;id="af1-remotesensing-07-10607">1Laboratoire de l’Inventaire Forestier, Institut National de l’Information Géographique et Forestière, 54000 Nancy, Fran;National Remote Sensing Center, Balanagar, Hyderabad 500037, India; E-Mails:

关键词: aboveground biomass;    Lidar;    volume profile;    canopy grain;    texture;    tropical forests;   

DOI  :  10.3390/rs70810607

来源: mdpi

【 摘 要 】

Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure.

【 授权许可】

CC BY   
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