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CHAPTER 7 GIS and Predictive Modelling: A Comparison of Methods for Forest Management and Decision-Making A. Felicísimo and A. Gómez-Muæoz 7.1 INTRODUCTION GIS can be a useful tool for spatial or land-use planning, but only if several conditions are fulfilled. The key conditions are related to 1) the quality of basic spatial information, and 2) the statistical methods applied to the spatial nature of the data. Appropriate information and methods allow the generation of robust models that guarantee objective and methodologically sound decisions. In this study we apply several multivariate statistical methods and test their usefulness to provide robust solutions in forestry planning using GIS. We must emphasize that in our Iberian study area, where forests have progressively decreased in extent over centuries, the main aims of forestry planning are the reduction of forest fragmentation, biodiversity conservation, and restoration of degraded biotopes. The research develops a set of likelihood or suitability models for the presence of tree species that are widely distributed over a study area of 41,000 km2. The utility of suitability models has been demonstrated in some previous studies1, but they are still not as widely employed as might be expected. A suitability model is a raster map in which each pixel is assigned a value reflecting suitability for a given use (e.g., presence of a tree species). Suitability models can be generated through diverse techniques, such as logistic regression or non-parametric CART (classification and regression trees) and MARS (multiple adaptive regression splines)2-4. All of these techniques require a vegetation map (dependent variable) and a set of environmental variables (climate, topography, geology, etc.) which potentially influence the vegetation distribution. The foundation of the method is to establish relationships between the environmental variables and the spatial distribution of the vegetation. Typically, each vegetation type will respond in a different way as a consequence of its contrasting environmental requirements. Suitability is commonly expressed on a 0-1 scale (incompatible-ideal). The precise value depends on a set of physical and biological factors that favor or limit the growth of each type of vegetation. Once the distribution of suitability values across a region is known, decisions on land use and management can be made on the basis of objective criteria. 117 © 2008 by Taylor & Francis Group, LLC 118 GIS for environmental decision-making The set of suitability values for a region can be considered as the potential distribution model if presented as a map: the area defined as ‘suitable’ in a model should reflect the potential area for the vegetation type under consideration. Such a model also represents the relationships between presence/absence of each forest type and the values of the potentially influential environmental variables in a given region. Usually, current forest distributions are significantly smaller than the potential spatial extents because they have been systematically logged. Potential distribution models allow the recognition and delineation of such former distribution areas in order to direct current and future management plans, provide valuable data for restoration initiatives and highlight areas where such actions should be considered a priority. 7.2 OBJECTIVES The main objectives of the study were to 1) use several different statistical methods to generate maps of potential distributions and suitability for each of three species of Quercus (oak) in the study area, and 2) identify the most appropriate method and assess its advantages and limitations. In order to fulfill these objectives, we developed a workflow that included sampling strategies, GIS implementation of statistical models and validation of results. 7.3 STUDY AREA The study area was Extremadura, one of the 17 Autonomous Communities of Spain, covering 41,680 km2, and located in the west of the Iberian Peninsula (Figure 7.1). It has a Mediterranean climate, somewhat softened by the relative proximity to the sea and the passage of frontal systems from the Atlantic. The study subjects, which partially cover this area, were three species of the genus Quercus that grow in forests or ‘dehesas’. Dehesas are artificial ecotypes derived from original forest clearings (Figure 7.2). Continuous forest cover disappeared centuries ago and currently only scattered patches remain over a large potential area. In some places deforestation was complete and not even the most open dehesas remain. Trees from the genus Quercus are the dominant constituents of forests in the area, the most important species (and those considered in the analysis) being Quercus rotundifolia Lam. (holm oak, 12,680 km2, synonym: Quercus ilex L. ssp. ballota (Desf.) Samp.), Quercus suber L. (cork oak, 2,130 km2) and Quercus pyrenaica Wild. (Pyrenean oak, 950 km2). With some exceptions, Pyrenean oak appears most commonly in forests, while cork and holm oaks preferentially occur in dehesas. © 2008 by Taylor & Francis Group, LLC Predictive modelling of tree species 119 Figure 7.1 Location of Extremadura in the Iberian Peninsula. Figure 7.2 Dehesas are artificial ecotypes comparable to savannas: a Mediterranean (seasonal) grassland containing scattered trees of the genus Quercus. © 2008 by Taylor & Francis Group, LLC 120 GIS for environmental decision-making 7.4 DATA A set of raster maps was compiled to reflect the spatial distribution of dependent and independent (predictive) variables. 7.4.1 Quercus Distributions Current Quercus species distribution maps were taken from the Forestry Map of Spain (scale 1:50,000), produced by the Spanish General Directorate for Nature Conservation during the period 1986-96. We used the digital version of the map to identify the main vegetation classes and the current spatial distributions (Figure 7.3). Figure 7.3 Current distribution of Quercus species in the study area (black represents Pyrenean oak, Q. pyrenaica; dark gray, cork oak, Q. suber; and pale gray, holm oak, Q. rotundifolia). © 2008 by Taylor & Francis Group, LLC Predictive modelling of tree species 121 7.4.2 Predictive Variables Raster maps were generated to represent the following independent variables: ! Elevation. A digital elevation model (DEM) was constructed using Delaunay triangulation of spot height and contour data from the 1:50,000 scale topographic map of the Army Geographical Service, followed by transformation to a regular 100 m resolution grid. ! Slope angle was calculated from the DEM by applying Sobel’s algorithm5. ! Potential insolation. A measure was derived following the method proposed by FernÆndez Cepedal and Felicísimo6. This used the DEM to assess the extent of topographical shading given the position of the sun at different standard date periods7. The result was an estimate of the time that each point on the terrain surface was directly illuminated by solar radiation. The temporal resolution was 20 minutes and the spatial resolution 100 m. ! Temperature maps of the annual maxima and minima were interpolated from data for 140 meteorological monitoring points (National Institute of Meteorology, Spain) using the thin-plate spline method8,9 with a spatial resolution of 500 m. ! Quarterly rainfall maps were interpolated from data for 276 meteorological monitoring points (National Institute of Meteorology, Spain) using the thin-plate spline method with a 500 m spatial resolution. These variables were selected because of their potential influence on the distribution of the vegetation and the availability of sufficient data to generate GIS digital layers. Lack of data eliminated other variables (e.g., soils) commonly used in ecological modelling. 7.5 METHODS 7.5.1 Statistical Methods The methods used in predictive modelling are usually of two main types: global parametric and local non-parametric. Global parametric models adopt an approach where each entered predictor has a universal relationship with the response variable. An advantage of global parametric models, such as linear and logistic regression, is that they are easy and quick to compute, and their integration with a GIS is straightforward. As an example of such a model we used logistic multiple regression (LMR). This is widely employed in predictive modelling10, but has several important limitations. For instance, ecologists frequently assume a © 2008 by Taylor & Francis Group, LLC ... - tailieumienphi.vn
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