Determining Pleiades satellite data capability for tree diversity modeling

Abstract

Modeling of the spatial distribution of tree species based on survey data has recently been applied to conservation planning. Numerous methods have been developed for building species habitat suitability models. The aim of this study was to investigate the suitability of Pleiades satellite data for modeling tree species diversity of Hyrcanian forests in northern Iran (Mazandaran Province). One-hundred sample plots were established over an area of 2,600 ha and surveyed for tree diversity, and the Simpson’s index (D), Shannon’s index (H’) and the reciprocal of Simpson’s index (1/D) were calculated for each plot. Spectral variables and several parameters derived by texture analysis were obtained from multispectral images of the study area and used as predictors of tree diversity of sample plots. Two different methods, including generalized additive models (GAMs) and multivariate adaptive regression splines (MARS), were used for modeling. The results revealed a fairly good prediction of plot tree diversity obtained using the developed models (adj-R2 = 0.542-0.731). Shannon’s H’ and Simpson’s 1/D indices were more accurately predicted using GAM-based methods, while MARS models were more suitable for predicting Simpson’s D. We concluded that Pleiades satellite data can be conveniently used for estimating, assessing and monitoring tree species diversity in the mixed hardwood Hyrcanian forest of northern Iran.