Combining high resolution satellite imagery and lidar data to model woody species diversity of tropical dry forests

Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

“…Even when a large number of remote sensing variables achieved statistically significant correlations, as suggests the assumption of the variation in the tree species composition will be related to variations in canopy traits such as reflectance spectra or canopy form [26,48], the relative superiority of the RapidEye derived metrics were evident. Other research that compares the performance of multisensor metrics for predicting compositional ordination [26] and taxonomic indices of biodiversity [20] also found the textural variables of multispectral imagery more useful. Those metrics by themselves has proved to be an invaluable tool for the study of biodiversity, as other studies had been reported in temperate [14,25] and tropical landscapes [39,78].…”

“…The LiDAR strongest correlation (Canopy relief ratio) is a quantitative descriptor of the relative shape of the canopy surface inherited from topography studies previous to the LiDAR blooming, defined as the mean height minus the minimum height divided by the maximum height minus the minimum height [80]. This metric reflects if the canopy is mostly in the upper or lower portions of the height range, and has also been considered as important variable on other models of community composition [28] and biodiversity [20,45].…”

“…It denotes the variation in species composition among sites, giving the possibility of identifying areas with high species turnover, and planing areas of conservation not exclusively based on α-diversity, but considering simultaneously the minimum overlap in species composition, and maximizing the number of species preserved in the whole region [17][18][19]). While α-diversity is helpful for identifying areas with high local species richness and is amply documented (even for the study area [20]), β-diversity has seldom been studied and it is as important as α-diversity.…”

“…Many remote sensing covariates are recognized as valuable for biodiversity mapping [8,[35][36][37][38]. Among them, multispectral textures have proven to be a tool of great worth [20,25,39], because they quantify the variability in reflectance values among pixels and this spectral heterogeneity is expected to be related to the heterogeneity of resource distribution or ecological variability, so that an increase in complexity allows more variability of habitat niches, as well as a higher number of species to coexist (spectral variation hypothesis) [18,40,41].…”

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

“…The all-possible-subset regression approach that creates alternative estimation models was used in this study to correlate field data (expressed in Shannon index) and remotely-sensed data. [51,[79][80][81] utilised all-possible subsets in species diversity estimation and reported its efficacy in evaluating all possible combinations of explanatory variables to select the best model. We believe that this approach is useful for exploring the suitability of the multiple sets of predictors (spectral bands, GLCM statistics and image window levels) to estimate species diversity.…”