Estimating the road edge effect on adjacent Eucalyptus grandis forests in KwaZulu-Natal, South Africa, using texture measures and an artificial neural network

Abstract

SPOT-5 multispectral and panchromatic image data were used to compute texture measures to estimate the road edge effect on adjacent Eucalyptus grandis forests. Employing a stepwise selection algorithm enabled the selection of optimal texture measures that were input into a backpropagation artificial neural network. The R2 of best models ranged from 0.67 to 0.89 for DBH, TH, BA, Volume and LAI on an independent test data set, with a root mean square error (RMSE) range of 0.01–5.36% for the respective variables. The result is critical for understanding and spatially predicting the road edge effect on adjacent vegetation.