Trends in spatial forest planning
Palavras-chave:
Spatial forest planning, Mathematical programming, Heuristics, Modeling techniques, GISResumo
Both mathematical and heuristic methods have advanced rapidly in spatial forest planning over the past 20 years. The review presented here is broader in both scope and depth (more analysis within spatial forest planning models). We conduct here a world-wide literature review and extensive analysis of the status and trends over the past two decades in spatial forest planning. In our investigation, we hope to understand the roles of objective and constraint functions in spatial forest planning. The literature review results suggest that methods used in forest planning have shifted somewhat from exact analytical solution techniques to heuristic techniques. In an effort to incorporate complex relationship into forest plans, other solution methods have also been evaluated for adoption in the planning process. Besides the economic and commodity production objectives, there is a noticeable increase in the proportion of ecological and social concerns in objective functions. In Europe, multi-parameter objective functions now seem to be in vogue, containing little or no constraints. In the U.S., single-parameter objective functions are still common, with multiple concerns recognized as constraints. In addition to the economic and commodity production constraints, adjacency and green-up relationships have recently been considered as important constraints in many areas of the world. Vector data are found to be more popular than raster data in the forest planning process, particularly in real-life applications of methods. In theoretical applications of methods, both vector and raster data are commonly used. Limitations in mixed integer programming, heuristic parameter selection processes, modification and enhancements to heuristics, and measurements of heuristic solution quality are some of the gaps we have identified.
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