Modeling training site vegetation coverage probability with a random optimization procedure: An artificial neural network approach

The main objective of this study is to examine the feasibility of applying feed-forward neural networks to estimate training site vegetation coverage probability based on past disturbance pattern and vegetation coverage history. The rationale behind this study is the excellent approximation and generalization ability of feed-forward neural networks. The data used to train the networks were collected from Fort Sill, Oklahoma, using the U. S. Army's Land Condition-Trend Analysis (LCTA) standard data collection methodology. The basic unit in this study is a transect point. Spatial independence between transect point's vegetation cover, as well as disturbance, was assumed. Two types of vegetation covers were modeled in this study: ground cover and canopy cover. For both types of vegetation cover, the input vector of a transect point consisted of seven variables, namely, the disturbance in years 1989, 1990 and 1991, the covers in years 1989 and 1991, transect plot's plant community type, and the vegetation's life form. The target output was whether the transect point was covered in year 1991. The actual output from a neural network was regarded as the estimated conditional probability of a transect point having vegetation cover in 1991.