Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

Juan Caamal-Sosa,Richard Birdsey,David López-Merlín,Juan Dupuy,Gonzalo Sánchez-Santos,Fernando Tun-Dzul,Kristofer Johnson,Alicia Peduzzi,José Hernández-Stefanoni

doi:10.3390/rs6064741

Abstract

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

Highlights

Tropical forests are one of the most diverse terrestrial communities in the world
The number of sample plots employed provided adequate representations of species richness at the landscape level, both for the Kiuic and for the Felipe Carrillo Puerto (FCP) sites, as shown in [27,53], through the use of species accumulation curves. Both species richness and above-ground biomass (AGB) were consistently higher in FCP than in Kiuic across sampling areas (Table 1)
Species richness consistently increased as the sampled area increased, whereas AGB failed to show a clear trend with the total area sampled (Table 1)

Summary

Introduction

Tropical forests are one of the most diverse terrestrial communities in the world. They provide goods and ecological services to human populations, store more carbon than any other terrestrial biome and play a crucial role for mitigating future global warming [1]. Understanding and identifying the main factors that affect biodiversity and biomass is critical to mapping these variables [7] This would allow us to develop appropriate methods for predicting these variables if we found some parameters or indicators measured from remotely sensed data that can be used as proxies for these factors [8,9]

Objectives

Results

Discussion

Conclusion