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Open AccessArticle

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

Laboratoire de l'Inventaire Forestier, Institut National de l'Information Géographique et Forestière, 54000 Nancy, France
Institut Français de Pondichéry, UMIFRE CNRS-MAEE 21, Pondicherry 605001, India
FPInnovations, 570 Saint-Jean Boulevard, Pointe-Claire, Montrea, QC H9R 3J9, Canada
National Remote Sensing Center, Balanagar, Hyderabad 500037, India
IRD, UMR AMAP, F-34000 Montpellier, France
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2015, 7(8), 10607-10625;
Received: 14 April 2015 / Revised: 4 August 2015 / Accepted: 12 August 2015 / Published: 18 August 2015
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. View Full-Text
Keywords: aboveground biomass; Lidar; volume profile; canopy grain; texture; tropical forests aboveground biomass; Lidar; volume profile; canopy grain; texture; tropical forests
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Véga, C.; Vepakomma, U.; Morel, J.; Bader, J.-L.; Rajashekar, G.; Jha, C.S.; Ferêt, J.; Proisy, C.; Pélissier, R.; Dadhwal, V.K. Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar. Remote Sens. 2015, 7, 10607-10625.

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