Next Article in Journal
New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis
Previous Article in Journal
Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(8), 10607-10625;

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
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 14 April 2015 / Revised: 4 August 2015 / Accepted: 12 August 2015 / Published: 18 August 2015
Full-Text   |   PDF [1052 KB, uploaded 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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top