Predictive modelling of forest site quality on landscape and regional scaleIn forestry science, site quality assessment is the discipline concerned with the analysis and prediction of the causal relationships between environmental variables such as climate, geomorphology, soil, land-use history and atmospheric deposition on the one hand and forest performance in terms of renewable resource production and environmental and socioeconomic services on the other. Because these environmental variables are difficult to measure with sufficient spatial and temporal resolution, the site quality for forestry in Europe and North-America was empirically derived from the tree species specific dominant height of an even-aged tree population of known age. For several applications, however, it is not possible to measure this site index in a direct way, e.g. in mixed, unevenaged stands, after afforestation of non-forested land and stand conversion to another tree species, or because site conditions changed over time. But by linking dominant height to environmental variables, landscape characteristics and vegetation data, site quality can be estimated at non-monitored sites and predictive maps of potential productivity can be produced. These recent evolutions in forest site quality assessment are strongly linked with the domain of spatial modeling by means of GIS-technology, more particularly with spatial techniques of predictive mapping, also called regionalization. The overall aim of this research is to contribute to the development of a generic, GIS-based approach for multifactor forest site classification. The objective of this study is to use location based, attribute based and hybrid regionalization techniques to construct predictive maps representing the spatial variation in forest site quality for the three main species (Pedunculate oak, Common beech and Scots pine) of Flanders region (13.500 km²), Belgium, at two nested spatial scales: the regional scale and the landscape scale.
|Forest Ecology and Management||member||2007-01-01||2011-01-01|
created:2011-12-14 14:18:59 UTC, source:web