The objective of this paper is to simulate the location of land-use change, specifically forest disturbance, in Costa Rica over several decades. This paper presents a GIS-based model, GEOMOD2, which quantifies factors associated with land-use, and simulates the spatial pattern of land-use forward and backward in time. GEOMOD2 reads rasterized maps of land-use and other biogeophysical attributes to determine empirically the attributes of land that humans tend to use. Then GEOMOD2 uses the patterns of those biogeophysical attributes to simulate the spatial pattern of land-use change. GEOMOD2 can select locations for land-use change according to any of three decision rules based on (1) nearest neighbors, (2) stratification by political sub-region, and/or (3) the pattern of biogeophysical attributes. GEOMOD2 simulates the progressive loss of closed-canopy forest in Costa Rica for 1940, 1961 and 1983, which are the years for which maps of land-use are available. Also, GEOMOD2 extrapolates the pattern of land-use to the year 2010. When GEOMOD2 extrapolates land-use change over several decades, it is able to classify correctly between 74 and 88% of the grid cells, for two categories: forest versus non-forest. Over various simulation runs, Kappa ranges from 0.31 to 0.53. The model's ability to predict the location of disturbance is best when the model is driven by the location of biogeophysical characteristics, most importantly lifezones.
Abstract. Although forest conservation activities particularly in the tropics offer significant potential for mitigating carbon emissions, these types of activities have faced obstacles in the policy arena caused by the difficulty in determining key elements of the project cycle, particularly the baseline. A baseline for forest conservation has two main components: the projected land-use change and the corresponding carbon stocks in the applicable pools such as vegetation, detritus, products and soil, with land-use change being the most difficult to address analytically. In this paper we focus on developing and comparing three models, ranging from relatively simple extrapolations of past trends in land use based on simple drivers such as population growth to more complex extrapolations of past trends using spatially explicit models of land-use change driven by biophysical and socioeconomic factors. A comparison of all model outputs across all six regions shows that each model produced quite different deforestation baseline. In general, the simplest FAC model, applied at the national administrative-unit scale, projected the highest amount of forest loss (four out of six) and the LUCS model the least amount of loss (four out of five). Based on simulations of GEOMOD, we found that readily observable physical and biological factors as well as distance to areas of past disturbance were each about twice as important as either sociological/demographic or economic/infrastructure factors (less observable) in explaining empirical land-use patterns.We propose from the lessons learned, a methodology comprised of three main steps and six tasks can be used to begin developing credible baselines. We also propose that the baselines be projected over a 10-year period because, although projections beyond 10 years are feasible, they are likely to be unrealistic for policy purposes. In the first step, an historic land-use change and deforestation estimate is made by determining the analytic domain (size of the region relative to the size of proposed project), obtaining historic data, analyzing candidate historic baseline drivers, and identifying three to four major drivers. In the second step, a baseline of where deforestation is likely to occur --a potential land-use change (PLUC) map-is produced using a spatial model such as GEOMOD that uses the key drivers from step one. Then rates of deforestation are projected over a 10-year baseline period using any of the three models. Using the PLUC maps, projected rates of deforestation, and carbon stock estimates, baseline projections are developed that can be used for project GHG accounting and crediting purposes: The final step proposes that, at agreed interval (eg, +10 years), the baseline assumptions about baseline drivers be re-assessed. This step reviews the viability of the 10-year baseline in light of changes in one or more key baseline drivers (e.g., new roads, new communities, new protected area, etc.).The potential land-use change map and estimates of rates of deforestation cou...
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