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

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.

Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest

“…In addition to pulse density, performance of ALS based biomass estimation is also affected by the field plot size [16,41,42]. Combined effects of pulse density and plot size were assessed by Watt et al [16] who concluded that reduced pulse density and plot size had little effect on model fit for pulse densities >0.1 pulses·m and plot sizes of >0.03 ha.…”

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

“…Both co-registration errors and boundary effects are reduced with reducing the ratio of field plot periphery to plot area. Accordingly, several studies on modelling of forest biomass using remotely sensed data have documented that increased plot size increased the model precision [13][14][15][16].…”

Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR

“…The 1-ha grid scheme and the horizontal precision of the 50-ha plot corners (0.050 m) helped to reduce co-registration errors related to misalignment between field subplots and LiDAR data, as well as plot-edge effects. There is a tendency for errors to decrease in biomass estimates with increasing plot size, because large plots reduce the likelihood of plot-edge effects, which occur when the canopy of trees are found along the plot boundary [21]. Edge effects are likely more pronounced in less dense stands and where plot sizes are smaller [56].…”