Forward and inverse modelling of canopy directional reflectance using a neural network

Abstract

This article explores the use of artificial neural networks for both forward and inverse canopy modelling. The forward neural modelling paradigm involved training a network for predicting the bidirectional reflectance distribution function (BRDF) of a canopy given the density of the trees, their height, crown shape, viewing, and illumination geometry. The neural network model was able to predict the BRDF of unseen canopy sites with 90% accuracy. Analysis of the signal captured by the model indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model involved learning the underlying relationship between canopy structural parameters and their corresponding bidirectional reflectance. The inversion results show that the R2 between the network predicted canopy parameters and the actual canopy parameters was 0.85 for density and 0.75 for both the crown shape and the height parameters. The results of both forward and inverse modelling suggest that neural networks can model accurately the BRDF of vegetated canopies.