Doctoral Dissertation Research: Active and Passive Remote Sensing for Predicting Tropical Tree Species Richness across Spatial Scales

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

This doctoral dissertation research project investigates how remotely sensed biophysical variables can be used to predict and model tropical tree species richness and forest structure across a range of spatial scales. Ecologists long have sought to be able to use remote sensing to extend the spatial extent of their analysis beyond the plot-level spatial scale at which tree species richness and forest structure traditionally have been studied. The doctoral student will test the hypothesis that environmental variability in the biophysical environment contains explanatory power in determining how different species of tropical trees are able to partition resources and co-exist in limited forested space. He will use a range of remotely sensed explanatory variables, including vertical vegetation structure, light availability, topographic variation, and spectral reflectance, to explain the variance in species richness, diversity, and forest structure (basal area and stem density) calculated from existing forest census plot data. He will use generalized least-squares regression modeling to determine the proportion of explained variance in tree species richness from the remotely sensed variables. He anticipates enhancing understanding of which remotely sensed variables explain the largest proportion of variance in species richness and forest structure and at which spatial scales these predictions are statistically significant and ecologically relevant. Based on preliminary analysis, he has determined that prediction model accuracy increases as spatial resolution gets coarser (1-ha plot size) and has the highest predictive power when all stems in the census are included. The student will include additional tree plots that focus solely on large trees (+20 cm DBH) to extend the predictions to large canopy trees that cover a larger proportion of the landscape. The proposed research will provide a framework that can be applied to any similar forested environment with field inventory plot data and remote sensing data. The proposed research is site specific, but the ecological and biogeographic principles being tested have much wider-ranging significance. The proposed research will occur in the Barro Colorado Nature Monument of Panama due to the availability of extensive ground forest census and remote sensing data, but the methods developed during the project will have utility at other Center for Tropical Forest Science (CTFS) sites that have existing forest census data. Testing these new methods at other CTFS sites will provide additional opportunities for forest researchers globally. By testing these hypotheses on a global scale, ecologists and biogeographers can investigate the potential causes of high-density tree species co-existence and signals to detect richness remotely. The project will provide informative data about predicted tree richness and forest structure to policy organizations concerned with forest conservation, and it will inform scientists and policy makers about the limitations and errors associated with such remote sensing derived predictions. By using tree species richness prediction maps, conversation groups can maximize available funding to target species rich forests over large spatial scales. This proposed Doctoral Dissertation Research Improvement award will provide support to enable a promising student to establish an independent research career.