Modeling deforestation baselines using GEOMOD - Instituto ...

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Finalizing Avoided Deforestation Baselines boundary information includes the three ejido forest extension zones of Dzibalchen, Hopelchen and Champoton . In the North the highest agreement between the simulated and the actual maps was achieved in the municipio of Champoton, which is also the region with the highest deforestation rate. Most of the area of analysis in the southern region lies in the municipio of Calakmul. In both the Calakmul (north and south) and the small portion of Escarcega intersecting our study area the percent of cells simulated correctly (see Appendix Table 2), was very high (98.09 – 98.68), but the kappa statistic very low (– 0.0079 to 0.0498). A kappa of 0 indicates the GEOMOD with the information provided by that driver map is not able to forecast the year 2000 landscape any better than if the model had no information, i.e. was operating on chance alone. A kappa less than 0 indicates that the model is doing worse than it would do with no information. Normally a low kappa or one less than zero is an indication that the pattern of deforestation is random and not predictable. Frequently this is seen in areas of clandestine deforestation or chaotic settlement patterns, such as those with a high refugee influx. Table 4. ‘Kappa-for-location’ results for each deforestation pattern driver and combination of drivers tested, for a) northern analysis, b) southern analysis (the cells colored red, blue and yellow indicate the three drivers, in importance from high to low respectively, giving the highest Kappa. Total Region Hopelchen Champoton Calakmul Escarcega DRIVERS Map Name Kappa Kappa Kappa Kappa Kappa NORTH 1 Dist. Agric_70 0.13 0.10 0.16 0.00 0.01 2 Dist. All Wat. Srcs. 0.04 0.07 0.03 0.00 0.02 3 Elevation 0.17 0.05 0.23 0.00 0.00 4 Ejidos 0.05 0.01 0.05 0.05 0.01 5 Dist. Arq.Sites 0.23 0.09 0.30 0.00 0.02 6 Dist. Roads 0.05 0.03 0.06 0.00 0.04 7 Dens. Sect.1 Pop. 0.07 0.03 0.09 0.00 0.01 8 Dist.Perm. Strms 0.12 0.19 0.12 0.01 -0.01 9 Dist. Towns 0.17 0.16 0.19 0.01 0.02 10 Dist. Perm. Wat. Src. 0.02 0.00 0.03 -0.01 0.01 11 Perm. Wetl. 0.00 0.00 0.00 0.00 0.01 12 Seas. Wetl. 0.02 0.09 0.00 0.00 0.01 13 All Wetl. 0.02 0.09 0.01 0.00 0.01 Combinations 5, 3 2 drivers 0.26 0.04 0.35 0.02 0.02 5, 3, 9 3 drivers 0.25 0.17 0.29 0.03 0.05 5, 3, 9, 1 4 drivers 0.26 0.15 0.32 0.03 0.05 5, 3, 9, 1, 8 5 drivers 0.28 0.17 0.34 0.05 0.03 5, 3, 9, 1, 8, 7 6 drivers 0.27 0.17 0.33 0.06 0.03 5, 3, 9, 1, 8, 7, 6, 4 8 drivers 0.27 0.18 0.33 0.05 0.03 SOUTH Map Name Kappa Kappa Kappa 1 Dist. Agric_70 0.0121 0.0782 0.0098 2 Dist. All Wat. Srcs. 0.0064 0.1257 0.0022 3 Elevation 0.0161 0.0753 0.0140 4 Ejidos 0.0927 0.1043 0.0923 5 Dist. Arq.Sites 0.0229 0.1682 0.0177 6 Dist. Roads 0.0244 0.0301 0.0242 A- 16 Winrock International
Finalizing Avoided Deforestation Baselines 7 Dens. Sect.1 Pop. 0.0222 0.0502 0.0212 8 Perm. Strms 0.0637 0.1257 0.0615 9 Dist. Towns 0.0223 0.0074 0.0228 10 Dist. Perm. Wat. Src. 0.0035 0.1125 -0.0004 12 Seas. Wetl. -0.0039 0.0161 -0.0046 Combinations 4, 8 2 drivers 0.0862 0.1441 0.0841 4, 8, 6 3 drivers 0.0876 0.1420 0.0857 4, 8, 6, 5 4 drivers 0.0984 0.2793 0.0920 4, 8, 6, 9 4 drivers 0.0951 1, 3, 5, 8, 9 5 drivers of north 0.0552 4, 8, 6, 5, 9 5 drivers of south 0.1001 0.2824 0.0936 4, 8, 6, 5, 9, 7 6 drivers 0.0998 0.2824 0.0933 We found improved ability to replicate the actual 2000 landscape with each additional driver, but the addition of a 6 th driver (density of people employed in sector 1 activities) brought down the kappa in both the north and south. The best kappa-for-location statistic achieved was 0.2755 in the north using distance to archeological sites, which was overwhelmingly the most significant determinant of deforestation pattern, followed by elevation, distance to towns, distance to 1970s agriculture, and distance to perennial streams. In the south where settlement is sparse and patterns appear very random we achieved a kappa-for-location of 0.10 using ejidos, distance to perennial streams, distance to roads, distance to archeological sites, and distance to towns. The percent correct in the south (97.8) with a low kappa indicates that so little change is happening in the south in the five year time period of the validation run that the model cannot help but get a high number correct, but due to the random pattern is doing only 10% better than a random simulation would do selecting the newly deforested cells. The inability to get a higher kappa in the South may also be caused by the little change that is happening there compared to areas in the north. This provides a high percent correct in the simulated map, but so few cells to improve upon that value that the kappa is of necessity low. This low rate of deforestation may be explained by the fact that much of the area is occupied by the Biosphere Reserve. Also over the five years of analysis, the area reforesting is 1.74 times the area deforesting. This reforesting land may be land that is cycling in and out of milpa cultivation, and for the time being the pressure on the mature forest is low. Without a third land use map it is difficult to tell if this reforestation is permanent or cycling. What is clear is that the type of forest conversion found in this region is very different from that found in the northwest corner of the study area, the planned agricultural settlement areas of Champoton. Results of driver assessment – identification of areas most likely to be cleared The final risk map (Figure 6) created through the iterative process of testing drivers and measuring ‘goodness of fit’ represents those areas that exhibit the greatest likelihood of deforesting in the future, based on the empirical land-use change pattern. The model selects forested cells during simulation based on the risk rating of each cell compared to all other cells. Factors have been scaled between 1 and 255. Class 0 represents land already deforested or non-forested. How fast any cell is converted is determined by the deforestation rate predicted. For ease of interpretation we have reclassified this map into 3 major categories, high to low risk (Figure 7). A histogram of the map in Figure 7 shows how unevenly the risk factors are distributed across the 1-255 range. The most vulnerable one third of the cells fall between values 15 and 255 on the Figure 7 map, the next one-third between 7 and 14, and the lowest between 1 and 6. This indicates a considerable difference in weighting of the cells in this region due to the many different forces affecting human settlement and use of the forest and forest land. The risk maps for each driver alone can be viewed in the Appendix (Maps 1 – 11). Winrock International A- 17
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