Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics

Abstract

AbstractQuestion: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species?Location: Quintana Roo, Yucatan peninsula, Mexico.Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model‐averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions.Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases.Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation.