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

Cédric Véga,Raphaël Pélissier,Gopalakrishnan Rajashekar,Vinay Dadhwal,Christophe Proisy,Jérôme Ferêt,Jean-Luc Bader,Chandra Jha,Jules Morel,Udayalakshmi Vepakomma

doi:10.3390/rs70810607

Abstract

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.

Highlights

Tropical forests store over 40% of the terrestrial carbon and play a major role in the global carbon cycle
Light Detection and Ranging (Lidar)-based models of forest parameters relied on variables describing the vertical properties of the point data at either plot or stand level, neglecting information about the horizontal arrangement of forest canopies, which was largely used in 2D optical remote sensing
In dense and multilayered canopies, like those found in the humid tropical forest, the limited capabilities of Lidar signal to penetrate to the ground could limit the potential of such approaches

Summary

Introduction

Tropical forests store over 40% of the terrestrial carbon and play a major role in the global carbon cycle. Accurate estimation of AGB, especially of tropical forests, is necessary to understand their influence on water and energy fluxes but assess impacts of carbon losses due to deforestation and forest degradation on global change and environmental degradation [4,5,6,7]. Sensor limitation, including spatial resolution, and vegetation complexity have been mainly attributed to poor performance and saturated biomass estimates in tropical forests with both optical and radar data [12,13,14,15]. These limitations prevent from assessing rates of forest degradation which are often characterized by subtle changes in the canopy structure. Improving accuracy of biomass mapping could constrain these uncertainties to some extent

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