FLORISTIC DIVERSITY AND EQUITABILITY IN FOREST FRAGMENTS USING ARTIFICIAL NEURAL NETWORKS

Abstract

This study aimed to evaluate the predictive efficiency of Shannon index (H’) and Pielou Equitability index (J) in forest fragments from the Brazilian Cerrado biome, from the vegetation indices and landscape metrics using artificial neural networks (ANN). Feedforward networks were used and they were trained through a back propagation error algorithm. The variables used as ANN input for simultaneous estimation of indices were: the categorical (H’ and J) and the numbers related to the mean and standard deviation of vegetation indices (NDVI, SAVI, EVI, and MVI5, MVI7) and landscape metrics (AREA, GYRATE, SHAPE, CONTIG, CORE and ENN). It was generated five models of ANN from the functional relationships between numerical variables inherent to vegetation indices in two seasons, a dry season (June) and a rainy season (February). The architecture of the networks was the Multilayer Perceptron (MLP), to estimate simultaneously the H’ and J: 500 using vegetation indices in the wet season (100 for each vegetation index) and 500 in dry (100 for each vegetation index). The precision, accuracy and realism of biological ANN were assessed. The nets built during the rainy season and dry season that used vegetation indices MVI5 (Moisture Vegetation Index) and SAVI (Soil Adjusted Vegetation Index), respectively, were more appropriate, accurate and biologically realistic to estimate both indices H’ and J. The ANN modeling demonstrated to be adequate to estimate the diversity index.