Laser Remote Sensing of Canopy Habitat Heterogeneity as a Predictor of Bird Diversity in Maryland
Scott
Goetz, Woods Hole Research Center, sgoetz@whrc.org
(Presenting)
Dan
Steinberg, Woods Hole Research Center, dsteinberg@whrc.org
Ralph
Dubayah, Univ of Maryland, dubayah@umd.edu
Bryan
Blair, NASA-GSFC, James.B.Blair@nasa.gov
Habitat heterogeneity has long been recognized as a fundamental variable indicative of species diversity, in terms of both richness and abundance. Satellite remote sensing data sets can be useful for quantifying habitat heterogeneity across a range of spatial scales. Past remote sensing analyses of species diversity have been largely limited to correlative studies based on the use of vegetation indices or derived land cover maps. A relatively new form of laser remote sensing (Lidar) provides another means to acquire information on habitat heterogeneity. Here we examine the efficacy of Lidar metrics of canopy structural diversity as predictors of bird species richness and abundance in suburban forests of Maryland. Canopy height, topography and the vertical distribution of biomass were derived from Lidar imagery and compared to bird survey data collected at referenced grid locations. The vertical distribution of biomass was found to be the strongest predictor of both total richness and abundance. Species richness was predicted best when stratified by guilds dominated by forest, grassland, scrub, suburban and wetland species, with different variables selected as primary predictors across guilds. Generalized linear and additive models, as well as binary hierarchical regression trees produced essentially similar results. The Lidar metrics were consistently better predictors than traditional remotely sensed variables such as canopy cover, indicating that Lidar provides a valuable resource for biodiversity research applications - particularly in complex and highly modified environments.