The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island, Mississippi

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

“…In line with previous reports (Carter et al, 2005;Lucas & Carter, 2008;Rocchini et al, 2013Rocchini et al, , 2014, the spectral variation, quantified as CV averaged over all bands, showed a significant (p b 0.001) positive correlation with species richness or alpha diversity in our study area (residual deviance = 1.01; Fig. 2).…”

“…Carter, Knapp, Anderson, Hoch, & Smith, 2005;Duro et al, 2014;Levin, Shmida, Levanoni, Tamari, & Kark, 2007;Lucas & Carter, 2008). Here we employed the CV (Eq.…”

The Use of Aerial RGB Imagery and LIDAR in Comparing Ecological Habitats and Geomorphic Features on a Natural versus Man-Made Barrier Island

“…Ground surveyed data were taken using randomly-selected points at a mean density of one point per 6 ha of island area. The method was based on Lucas and Carter's [24] previous study, which utilized a similar sampling density for line transects. Sample points were positioned randomly (ENVI v4.3) from SPOT-5 (10 m) Ground Sampling Distance (GSD) multispectral coverage (April-July 2010 image acquisitions, North American Data Purchase, USGS EROS Data Center, Sioux Falls, SD, USA).…”

“…islands [3,22,24,26]. Classifications were created for East Ship and Sand Island initially for 2010 because the ground survey took place that year.…”

Remote Sensing and GIS for Biodiversity Conservation

“…While these approaches provide a basic understanding of patterns and can be used to create predictive diversity maps for a landscape, region or continent, increasingly more sophisticated statistical and spatial techniques are being examined and developed to model patterns of diversity (Foody 2004(Foody , 2005 such as use of hyperspectral imagery for predicting species richness and abundance using univariate statistics (Lucas and Carter 2008;Oldeland et al 2010); explicit mapping of uncertainty in species diversity prediction by spectral variability (Gould 2000;Oindo and Skidmore 2002); use of neural networks for predicting species diversity (Foody and Cutler 2006); or use of spectral distances between sampling units for estimating turnover in species composition (Tuomisto et al 2003;He and Zhang 2009;Rocchini et al 2009). Spatial statistics such as geographically weighted regression analyses have also resulted in improved models of diversity (Foody 2005).…”