Understanding the spatial patterns of plant diversity across vernal pool complexes remains challenging, as plant communities change rapidly in time and concurrent collection of relevant data for modeling remain logistically elusive. In the absence of coupled ecohydrological data, we demonstrate that the application of drone-mounted light detection and ranging (LiDAR) systems to vernal pools enables estimation of species richness using hydrological proxies and spatial modeling. Parameters related to hydrologic connectivity, soil moisture, and hydroperiod describe substantial variation in species richness patterns (r2 = 0.28 ± 0.03) across vernal pool complexes. Converging factors, such as proximity to areas of hydrologic connectivity with low surface roughness, tend to promote forb richness but describe less of the variation in grasses. Estimates of species richness are accurate to within 2-3 species using models derived from UAV-LiDAR, providing an approximation of potential biodiversity hotspots in lieu of in-situ surveys.

A document by Gustavo Facincani Dourado. Click on the document to view its contents.

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (< 0.05) for five out of six units and similar RMSE for the remaining one. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.