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 espe...

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

This article illustrates the development and functioning of a dynamic spatial model that simulates the effects of gradual sea-level rise on coastal areas. Emphasis is given to identifying the dynamics of coastal systems and translating them into a spatially explicit dynamic simulation model. The model is designed to be flexible enough to serve as a base model for interdisciplinary research on sea-level rise and coastal dynamics. An application is provided for a coastal area on Cape Cod, Massachusetts.

Modeling spatial dynamics of sea-level rise in a coastal area

Linking a GIS to a spatially distributed, physically-based environmental model offers many advantages. However, the implementation of such linkages is generally problematic. Many problems arise because the relationship between the reality being represented by the mathematical model and the data model used to organize the spatial data in the GIS has not been rigorously defined. In particular, while many environmental models are based on theories that assume continuity and incorporate physical fields as independent variables, current GISs can only represent continuous phenomena in a variety of discrete data models. This document develops and outlines a strategy in which field variables are used to enable modelers to work directly with the spatial data as spatially continuous phenomena. Several outcomes from the use of this strategy are explored. Modelers can express their spatial data needs as representations of reality, rather than as elements of a GIS database, and a GIS-independent language for model development results. By providing a formal linkage between the various models of spatial phenomena, a mechanism is created for the explicit expression of transformation rules between the models of spatial data stored and manipulated by GIS. The incorporation of field variables allows several operations (such as determining integrals, slope and aspect) and reserved variables (such as latitude and longitude) which are commonly used in environmental models to be defined. While scalar fields are the focus of this document, consideration of the potential for the definition of vector fields and related operations (such as divergence and gradient) using this strategy is also included.

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Environmental modeling with GIS: A strategy for dealing with spatial continuity (93-3)

South florida: the reality of change and the prospects for sustainability

Acomprehensive, mechanistic simulation of wildland fire and ecosystem dynamics across a landscape may not be possible because of computer limitations, inadequate research, inconsistent data, and extensive parameterization. Therefore empirical and stochastic approaches must be substituted for many mechanistic modules until research and technology improve. Unfortunately, nonmechanistic approaches limit the scope and applicability of spatial ecosystem process models. Ecosystem dynamics models need to be refined so that appropriate compartments that model fire spread and effects are explicitly represented in their structure. Landscape disturbance models need to simulate fire growth unconstrained by vegetation polygon delineations. Most important, these models must be designed in the context of the simulation objective to ensure that appropriate simulation modules are included.

The Simulation of Landscape Fire, Climate, and Ecosystem Dynamics

This overview paper provides a foundation for the discussion of methods and techniques to integrate spatial ecological models with the technology of geographic information systems (GISs). A brief review is provided of historical modeling approaches for terrestrial, freshwater, and marine ecosystems that have some spatial component or can be extended into space. Examples of current spatial models are discussed for these ecosystems, and the use of distributional mosaic models for the simulation of broad-scale landscape disturbances such as fire and pests is examined. Spatial modeling is terrestrial and freshwater ecosystems in more advanced than in marine systems; however, marine systems are well suited for the application of GIS's and spatial models. Future needs and directions that will enable ecological models to capitalize on the capabilities of GISs are discussed with regard to data base management; remotely sensed data; landscape-level models; model-based management systems; data and model sensitivity, errors, and uncertainties; and environmental risk assessment.

Spatial models of ecological systems and processes: The role of GIS

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Spatial models of ecological systems and processes: The role of GIS