Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients

Abstract

Predictive vegetation mapping can be defined as predicting the geographic distribution of the vegetation composition across a landscape from mapped environmental variables. Comput erized predictive vegetation mapping is made possible by the availability of digital maps of topography and other environmental variables such as soils, geology and climate variables, and geographic information system software for manipulating these data. Especially important to predictive vegetation mapping are interpolated climatic variables related to physiological tolerances, and topographic variables, derived from digital elevation grids, related to site energy and moisture balance. Predictive vegetation mapping is founded in ecological niche theory and gradient analysis, and driven by the need to map vegetation patterns over large areas for resource conservation planning, and to predict the effects of environmental change on vegetation distributions. Predictive vegetation mapping has advanced over the past two decades especially in conjunction with the development of remote sensing-based vegetation mapping and digital geographic information analysis. A number of statistical and, more recently, machine-learning methods have been used to develop and implement predictive vegetation models.