Tree Diversity Detection with Mid-Resolution Images and Environmental Data in a Neural Network

Abstract

Biodiversity maps are crucial to conservation management. The present study assesses the accuracy of detecting tree diversity in an Italian forest site by combining mid-resolution images from Landsat-TM or Advanced Land Observation Satellite (ALOS)’s Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensors with environmental data namely elevation, slope, aspect and solar radiation in an artificial Neural Network (NN) classifier. The map accuracies obtained for Landsat-TM and ALOS images are 60 % and 53 % respectively. Use of environmental data increases accuracies to 91 % and 81 % respectively. Landsat-TM detects tree diversity more accurately than ALOS. Both the coarser pixel size and finer spectral resolution of Landsat-TM contributed to its higher accuracy.