Modeling deforestation baselines using GEOMOD - Instituto ...

Finalizing Avoided Deforestation Baselines 

Appendix 2. Modeling deforestation baselines using 

GEOMOD for the Calakmul and Meseta Purépecha 

regions in Mexico 

By 

Myrna Hall 

Geographic Modeling Services 

4090 Barker Hill Road 

Jamesville, NY 13078 

(315) 469-7271 

geomod@twcny.rr.com 

and 

Gabriela Guerrero and Omar Masera 

UNAM, 

Morelia, Michoacan 

gguerrer@oikos.unam.mx 

omasera@oikos.unam.mx 

Winrock International 

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I. INTRODUCTION 

The Spatial Modeling Approach 

Spatial modeling, as we define it (M. Hall et al. 2000) is the application of a FORTRAN (or other computer 

language)-based model that uses spatially distributed data to simulate landscape dynamics. In the case of 

land use change modeling it implies that the spatial distribution of various factors, such as topography, 

plays an important role in determining where humans exploit the landscape. We apply GEOMOD2, 

developed by researchers at SUNY College of Environmental Science and Forestry (Hall, et al. 1995a; 

1995b with funding from the US Department of Energy, Carbon Dioxide Research Program, Atmospheric 

and Climatic Change Division), to estimate the business-as-usual baseline for deforestation in two states of 

Mexico—the Calakmul region of Campeche and the Meseta Purépecha region of Michoacan. 

Based on the “maximum power principle” (Odum 1983), the model assumes and tests whether those 

factors which are most likely to impact an individual farmer’s energy return on investment (EROI) are 

statistically significant (Hall, Cleveland and Kaufmann, 1986). These factors include topographic position 

(elevation and steepness of slope), distance from rivers, roads and already established settlements, and 

socio-economic factors such as infrastructure. The model allows for regional stratification to capture 

different government policies in different political units that would affect land clearing and land tenure. It 

also includes the option to impose, or not, the contiguity rule in simulation. This rule limits land clearing to 

cells that are adjacent to cells already cleared. The search window distance used to impose contiguity 

around already-developed land can be adjusted to capture the difference between clearing for shifting 

cultivation, and clearing for industrialized agriculture. The model is calibrated by assigning weights to 

candidate map cells based on the empirical importance of each driver in explaining the spatial pattern of 

land use change at one point in time. It then simulates that change between that point in time and a later 

time. Change can be from forest to non-forest, from non-forest to forest, from forest to degraded forest, 

pasture to urban; in other words any pathway of change can be analyzed by the model. The model then 

compares these preliminary simulation results against a validation (real) data set (i.e. a map of time two) 

which is not used in weighting the pattern drivers. The test of goodness of fit for each simulated map 

produced is based on both the percent of cells simulated correctly and the Kappa Index of Agreement 

(Pontius et al. 2001). We use the ‘kappa-for-location’ index, rather than the ‘kappa-for-quantity’ because in 

all cases for validation we already know from the satellite imagery the quantity of cells that have been 

deforested or degraded between the two time periods. 

II. HYPOTHESIS 

We test three hypotheses. (1) GEOMOD should generate a more accurate estimate of the carbon benefits 

derived from a region than a non-spatial approach because the vegetation types, and hence carbon 

content, varies across a landscape and GEOMOD has the ability to specify WHERE deforestation is “most 

likely” to occur. (2) We can predict regional or project-specific results in the same model simulations. And 

(3) the use of GEOMOD, in particular, removes some of the “counterfactual uncertainty” (Moura-Costa, 

2001; Kerr, 2001) inherent in other methods and other spatial models used to estimate the baseline 

scenarios because GEOMOD employs a standardized procedure of assessment of this “counterfactual 

uncertainty (‘Kappa-for-location’ statistic, Pontius, 2000). 

III. How GEOMOD works 

III. A. Data Collection and Preparation 

The model requires as input the following mapped information: 


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1) Land use/land cover maps for preferably two points in time, classified to identify areas of human 

disturbance, versus areas susceptible to human disturbance. 

2) A map of political sub-regions if significantly different patterns of land use are evident due to 

different government policies, rules or regulations that effect how people use the land. 

3) A map indicating areas to be excluded from analysis, often called the “desert” map. 

4) Multiple potential candidate driver maps that may explain where people have selected to clear 

forest for agriculture or other human-dominated land use, e.g. elevation, slope of the terrain, 

distance from town, markets, roads, rivers, navigable water, etc. 

5) A vegetation biomass map for which the carbon content per vegetation type is known. 

All digital map data required by the model must be collected, corrected for projection differences, and 

converted to multiple row, column grid data layers of the same origin and extents (see GEOMOD Step-by- 

Step Manual in the Appendix for the specific steps required to prepare data inputs for input to the model). 

III. B. Calibration 

The best analyses of land use change are accomplished when data are available from at least three points 

in time. GEOMOD begins by categorizing each potential pattern driver map into categories or classes, of 

e.g. slope, elevation, distance from roads, etc. and summing the number of cells of each class that exist in 

the entire geographic region being analyzed. The model then sums how many cells of each of these 

categories lie in areas deforested between the first and second points in time. Finally GEOMOD calculates 

the proportion of this sum for each class versus the sum of all cells of that class that exist in the region. 

This proportion indicates the degree to which land of that property had been sought out by farmers for 

clearing in the past, and is assigned to all forested cells of that class to indicate their ‘risk’ or ‘likelihood’ of 

being deforested in the future. All driver ‘risk’ maps are added together to create a final map of cells ranked 

according to their likelihood of being cleared. GEOMOD uses this map of ranked ‘likelihoods’ or ‘risk’ to 

simulate deforestation for a third point in time. The results are validated against the actual map of that 

same time period to test how well the drivers succeeded in predicting the spatial pattern of deforestation 

(Figure 1). This ‘test’ is called the validation process and is discussed further below. 

The standard way that GEOMOD creates the risk map is to assign weights based on areas of empirical 

deforestation. Another possible approach, called heuristic weighting, can also be tested. The user, rather 

than the model assigns likelihood monotonically based on e.g. distance from roads or towns, without 

consideration of the empirical pattern. The need to turn to heuristic weight assignments is normally done 

only when there is a lack of information that would allow the model to clearly sort out the signal evidenced 

in the empirical pattern of forest disturbance. This is especially true when we have fewer than three points 

in time to analyze, and would like to assess the significance of ‘distance from areas of previous 

disturbance.’ Heuristically-based driver weights should be applied only on a case-by-case basis, and are 

normally used only in situations where not much data exist. 


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Driver 4 

Driver 3 

Driver 2 

Driver 1 

Calibration 

Procedure 

Likelihood 

map 

T1 Landuse 

Map 

cells deforested = y 

Simulated T2 

Map 

cells deforested = y 

T2 Landuse 

Map 

Validation 

Procedure 

Kappa for 

Location 

Figure 1. Flowchart for GEOMOD showing how empirical knowledge (maps) is used to weight 

drivers and how, through comparison of simulated results versus the actual landscape, we use the 

Kappa-for-location measurement of ‘goodness of fit’ to validate which driver set explains most of 

the spatial variation of land use change. 

III. C. Validation 

To validate the results created by GEOMOD, the actual land-use map at a known point in time is compared 

to the simulated land-use map of that same time period based on analysis and projection of the pattern of 

land use from the earlier point in time. It was common in the past to measure ‘goodness of fit’ using a 

simple percent correct measure or, at best, a multiple-resolution percent correct (Costanza, 1989, Hall et al. 

1995) but this provides little assessment of a model’s ability to predict the correct quantity of change versus 

its ability to identify the correct location of change. Spatial measures of ‘goodness of fit’ have been 

developed that measure the degree to which a simulated map agrees with a reality map with respect to 

both location (Kappa-for-location) and quantity of cells correct (Kappa-for-quantity) (Pontius, 2000). 

How then do we explicitly measure success? First we determine how many cells did GEOMOD get right. 

This is the model’s success rate. Then, what is the expected success due to chance alone? This we call 

the success rate based on random cell selection and is calculated as: 

R = (C2 + (N – C) 2) / N 

where: 

R = the rate of success expected from random cell selection 

C = number of grid cells that change 


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N = the total number of grid cells 

The next question is how much did GEOMOD improve upon Chance alone? This is the Kappa Coefficient 

or Kappa Index of Agreement, and is calculated as: 

K = (model success – random success) 

100% - random success 

Since we are validating with a known quantity of deforested cells, we are calculating the Kappa for location, 

which is calculated the same as the standard Kappa. The highest possible value for the Kappa Index of 

Agreement and all of its different variations is 1.0. A value of 0 indicates that simulation results are only as 

good as if they had been determined randomly. A 1.0 is perfect classification in all respects. A negative 

value would indicate that the simulation was worse than ‘chance alone,’ or that the pattern of land use 

change displays no rational pattern, i.e. is completely random. (For details on the use and derivation of this 

statistic see Pontius (2000), Pontius, et al. (2000), and Pontius and Schneider (in press)). As the interest of 

this research is finding a means to estimate the most precise carbon baseline possible, and because 

carbon storage varies across the landscape, we have used the Kappa-for-location, or K location statistic, to 

measure ‘goodness of fit.’ 

With ‘Kappa-for-location,’ we can compare baseline-modeling results for a given year, based on a variety of 

driver combinations, against an actual map of land use change for that same year. Then, depending on the 

simulated map’s performance, we can select the best driver combination to use to simulate future land use 

change beyond the validation period. By doing this, we are not wedded to one model for the duration of the 

project but rather are given the option of periodically updating the baseline throughout its lifetime, as has 

been suggested by Kerr (2001) and Moura-Costa (2001). We can also use changing rates, and derivatives 

of rates, to update our assessments. 


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III. D. Simulation of the carbon baseline 

When we model deforestation under a ‘business-as-usual’ scenario (BAU) we allocate the predicted 

quantity of deforestation to the highest-ranking candidate cells in the risk map. The location of each cell 

deforested within the boundary of the climate action project is then referenced to the carbon map to find the 

amount of carbon emitted per year. These yearly quantities of carbon emissions, over the course of the 

project’s lifetime, are the baseline estimate of carbon released, assuming the absence of a carbon set-aside 

project. 

IV. The APPLICATION of GEOMOD to predict the ‘business-asusual’ 

carbon baseline for the Calakmul region of the State of 

Campeche 

IV. A. Project Area Description 

We analyzed deforestation within the State of Campeche, Mexico located on the Yucatan Peninsula. The 

area analyzed (16,662 km 2 ) includes 6,597 km 2 of the Calakmul Biosphere Reserve plus an additional 

10,025 km 2 to the west of the reserve, an area that includes both heavily impacted regions and remote 

areas of the Mayan Forest (la Selva Maya). Vegetation in the region according to the Instituto Nacional de 

Estadística Geografía e Informática (INEGI) year 2000 classification consists primarily of selva alta, 

mediana, y baja subperrenifolia (tall, medium and low evergreen forest) (Figure 2). 


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Figure 2. Year 2000 Vegetation Map for the State of Campeche (INEGI 2000). Yellow box illustrates 

area of analysis. 

IV. B. Data Inputs 

The data required to make this analysis possible were contributed by many organizations and individuals 

working in the area, such as Ben DeJong, and Miguel-Angel Castillo of El Colegio de la Frontera Sur 

(ECOSUR); Daniel Juhn of Conservation International (CI); Kim Batchelder, The Nature Conservancy 

(TNC); Gabriela Guerrero UNAM Michoacan; Larry Gorenflo of Argonne National Laboratories; Billy Turner, 

Clark University; or purchased from the Mexican government’s Instituto Nacional de Estadística Geografía e 

Informática (INEGI), or were created through satellite image analysis and digitizing of INEGI paper maps. 

Data, data sources, metadata 

Data for this analysis was selected to capture those factors, which we assume may influence the rate and 

pattern of forest conversion. Biophysical factors include elevation, hydrography, presence of natural water 

bodies both seasonal and constant, and wetlands; economic factors are those related to investments in 

infrastructure that give people increased access to forested lands or increased access to markets, or make 

areas more desirable to live, e.g. railroads, roads, agricultural crop delivery points, ports, towns with 

commercial importance, schools, medical clinics. Political factors are incorporated by 1) analyzing 

individual political units, in this case ‘municipios,’ and 2) incorporating the ‘ejido’ boundaries as a potential 

determinant of the quantity and pattern of change across the landscape. Such political boundaries 

delineate different decision-making units, where the policies effected by governing bodies may affect the 


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pattern and quantity of land conversion within the boundary of that unit. Naturally from a list of ideal factors 

to analyze it is not always possible to obtain the desired data, nor to obtain it in a complete coverage of the 

region to be analyzed. For this analysis we were able to obtain and employ the following factors: 

• Roads (we attempted to separate paved from non-paved) (source INEGI 1:50,000 maps) 

• Hydrography – perennial and seasonal streams (INEGI 1:50,000 and 1:250,000 maps) 

• All water sources as indicated by the INEGI 1:50,000 map series (we had hoped to identify water 

delivery points as well but these are not currently mapped). 

• Perennial water bodies (INEGI 1:50,000) 

• Wetlands – perennial and seasonal (INEGI 1:50,000) 

• Elevation in the form of a digital elevation model (INEGI 1:50,000 contours and the Mexican 

Government’s 1:250,000 DEM accessed by the agreement with the United States Geologic Society’s 

North American Land Cover program. 

• Community locations (INEGI 1:50,000) 

• Municipio boundaries 

• Ejido boundaries 

• Location of arqueological sites (INEGI 1:50,000) 

• Agriculture/Pasture Lands in 1970 (Mexican Government 1970’s Forest Inventory or INEGI 1970s 

vegetation map (1:250,000 scale). 

• Density of Population working in the Forestry/Agriculture Sector (INEGI 1990 and 2000 census) 

Daniel Juhn of CI provided a 1995 reclassification mosaic of the Selva Maya. Starting with diverse 

classifications from many agencies working in different parts of the Selva Maya he created an aggregated 

land use map in which the following categories lie within the region of analysis: 

1. Selva Alta (High Evergreen Forest) 

2. Selva Baja (Low Evergreen Forest) 

3. Areas inundables (lowland flood areas, herb. veg.) 

4. Sabana (savannah) 

5. Vegetación Secundaria (secondary vegetation) 

6. Urbano/Agricultura/Potrero (urban/agriculture/pasture) 

These classes were further reduced to 2 classes (forested and deforested) as follows: 

1. Forest (types 1, 2, 3,and 5 above) 

2. Deforested (types 4 and 6 above) 

A second aggregation was done that excluded the herbaceous vegetation and secondary vegetation from 

the ‘forest’ class as follows: 

1. Forest (types 1 and 2) 

2. Deforested (types 3, 4, 5,and 6) 

This was done with the intent of comparing the two deforestation rates and hopefully the projection results 

to see if we could detect “reforestation” through the overlay process. In this document we report the results 

of our analysis using the first classification only. ‘Savanna is assumed to be predominately pasture 

misclassified, as it is seen moving in and out of forest. Furthermore, the INEGI vegetation maps indicate 

very little natural savanna in this region. Thirty-three percent of the land that was reforested between 1995 

and 2000 (34,462 ha) had been classified as ‘sabana’ in 1995. 


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Table 1: Mapped vegetation categories for the Selva Maya (Juhn 2000) found in the Campeche 

study area and the classes to which they were assigned for modeling the baseline. (1 = forest, 

2 = deforested, 0 = not included in the analysis) 

Cat_ID-Land Use/Land Cover Model Category Scen. 1 Scen. 2 

0 – No data 0 0 

1 – Selva Alta (High Evergreen Forest): 1 1 

2 – Selva Baja (Low Evergreen Forest): 1 1 

4 – Areas inundables (lowland flood areas, herb. veg.): 1 2 

8 – Sabana (savannah): 2 2 

9 – Vegetación Secundaria (secondary vgetation): 1 2 

10 – Urbano/Agricultura/Potrero (urban/agriculture/pasture): 2 2 

12 – Cuerpos de água (water): 0 0 

Data Preparation 

All maps were analyzed for spatial congruence with each other and converted where necessary. One third 

of the area analyzed lies within UTM zone 15, while 2/3 lies in zone 16. We projected the Selva Maya map, 

which traverses several zones to UTM zone 16, NAD83. Where necessary we resampled all other gridded 

land cover/land use data, including the TM 2000 satellite classification, to match the 1995 Selva Maya map 

and reprojected all zone 15 and zone 16 data to 

the following parameters: 

ref. system : Universal Transverse Mercator Zone 16 

projection : Transverse Mercator 

datum : WGS84 

delta WGS84 : 0 0 0 

ellipsoid : WGS 84 

major s-ax : 6378137.000 

minor s-ax : 6356752.314 

origin long : -87 

origin lat : 0 

origin X : 500000 

origin Y : 0 

scale fac : 0.9996 

units : m 

parameters : 0 

All vector data such as roads, streams, wetlands, etc. were reprojected and gridded in ESRI’s ARCVIEW 

3.2 to match the grid extents and resolution of the selected area of analysis. These consist of 3554 columns 

and 5209 rows, with a grid cell resolution of 30 by 30 meters and the following UTM zone 16 bounding 

coordinates. 

min. X : 146615.0000000 

max. X : 253235.0000000 

min. Y : 1972619.0000000 

max. Y : 2128889.0000000 

The extent of the area of analysis was determined based on 1) the decision to limit the analysis to the state 

of Campeche, 2) to try to capture some of the advancing deforestation front coming from the west, 3) the 

availability and cost of data and 4) computer memory limitations. 


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Using IDRISI32 software, we created grids representing ‘distance from’ communities, rivers (perennial and 

seasonal), roads, historical sites, 1970 agriculture, and water sources (perennial and seasonal). The final 

set of candidate driver maps (Table 2) included seven ‘distance from’ maps, plus presence or absence of 

wetlands (seasonal and perennial), density of persons involved in sector 1 economic activity (agriculture 

and forestry), ejido boundaries and elevation (Table 2). Due to the high-resolution of the data employed for 

this analysis and hence the large number of rows and columns, the computer memory to run the analysis 

exceeded 1 Gb RAM. To realize our objective we split the region in half north and south. The northern half 

consisted of 2605 rows by 3554 columns, the southern 2604 by 3554. Memory limitations also required use 

of GEOMOD’s ‘non-neighborhood’ search option only. This greatly decreases model run time but sacrifices 

some spatial accuracy. In order to preserve the rationale behind the neighborhood function we created a 

“distance from prior disturbance” driver, in this case ‘distance from 1970s agriculture.’ If this factor is found 

to be important then each cell could be weighted heuristically (see section III. B.) in order to force the model 

to deforest in the neighborhood first. Similarly ‘distance from towns,’ if important, also gives greater weight 

to forest lands lying proximate to ‘deforested’ population centers. 


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Table 2. Category Delineation for Candidate Spatial Pattern Determinants 

Driver # Classes Class Width 

Dist. From Historic Sites 22 3000 m 

Elevation 19 20 m 

Dist. From Towns 19 2000 m 

Dist. From 1970 Ag. 25 3000 m 

Dist. From Perennial Streams 28 1000 m 

1990 Dens. Of Ag/For Pop 10 10 pers/km2 . 

Dist. From Roads 25 1000 m 

Ejidos 115 

Dist. From All Water 21 1000 m 

Dist. From Perennial Water Source 25 1000 m 

All Wetlands 2 Wetl/Non_Wetl 

Seasonal Wetlands 2 Wetl/Non_Wetl 

Perennial Wetlands 2 Wetl/Non_Wetl 

IV. C. Calibration and Validation of Spatial Pattern Drivers 

Calibrating the candidate drivers of deforestation 

To understand those factors that explain the spatial distribution of forest clearing for human uses in the 

Selva Maya portion of the State of Campeche, we analyzed four regions. These are portions of four 

municipios that intersect the selected study area boundary (Figure 3). They include Hopelchen, 

Champoton, Calakmul, and a small portion of Escarcega. 


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Figure 3. Municipalities intersecting the region analyzed. All are located within the State of 

Campeche. 

GEOMOD calculates the portion of each driver class that coincides with deforested cells in 1995 and then 

simulates a year 2000 landscape. The 1995 land against which the drivers were analyzed is shown in 

Figure 4. Through visual comparison with the mapped drivers (Figure 5) one can often get an intuitive 

sense of those factors that appear to be the most important. GEOMOD’s SR1.Out file (Table 3) reports the 

percent of each driver class that is already deforested in 1995. These percentages are the weights applied 

to each remaining forested cell in that driver class. In each calibration run the final weight applied to each 

forested cell in the map is a combination of the weights (or percentages) of all the drivers being tested in 

that run. 


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Figure 4. 1995 Land Use. Deforested includes urban, pasture (including savanna), agriculture. 

Forested lands include selva alta and baja and secondary vegetation. Percent of each driver class 

lying in deforested (yellow) cells signifies the importance of that driver class in explaining the 

pattern of deforestation in 1995. Lands that reforested between 1995 and 2000 are orange. 


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Figure 5. Four spatially distributed factors that may explain deforestation patterns in Campeche – 

Ejido land use history, distance from communities, distance from year-round bodies of water, 

elevation. 


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Table 3. Percent of each class of each driver already deforested in 1995. The set shown here is for 

the five drivers used in the analysis of the northern portion and are for the municipio of Champoton 

only. Results for each municipio for the final set of drivers used are reported in full in the Appendix 

(Table 1 ). A value of 0.0 indicates no cells in that class are deforested. A value of –1.0 

indicates that there are no cells of that class. 

REGION 2: Champoton 

CATEGORY MAP_1 MAP_2 MAP_3 MAP_4 MAP_5 

1 1.79 39.13 48.30 35.83 24.38 

2 0.71 51.09 20.01 8.68 10.14 

3 3.69 10.24 5.93 5.80 5.90 

4 3.14 5.23 3.77 3.14 0.19 

5 2.66 5.80 3.30 0.65 0.11 

6 1.80 1.39 1.75 0.29 0.21 

7 4.00 0.41 0.10 0.35 0.83 

8 2.66 1.67 0.44 0.44 0.65 

9 4.88 0.58 0.69 0.04 1.10 

10 9.43 0.11 0.75 0.02 1.60 

11 11.95 0.01 0.01 1.92 4.54 

12 12.79 0.00 0.00 0.00 5.58 

13 14.23 0.00 0.00 0.00 8.70 

14 18.66 0.00 0.00 0.00 9.90 

15 17.63 0.00 0.00 -1.00 10.21 

16 18.77 0.00 0.00 -1.00 7.12 

17 24.26 0.00 0.00 -1.00 8.14 

18 47.12 -1.00 0.00 -1.00 8.02 

19 93.28 -1.00 0.00 -1.00 12.70 

20 -1.00 -1.00 -1.00 -1.00 18.94 

21 -1.00 -1.00 -1.00 -1.00 27.91 

22 -1.00 -1.00 -1.00 -1.00 29.15 

23 -1.00 -1.00 -1.00 -1.00 27.34 

24 -1.00 -1.00 -1.00 -1.00 38.58 

25 -1.00 -1.00 -1.00 -1.00 44.94 

26 -1.00 -1.00 -1.00 -1.00 22.72 

27 -1.00 -1.00 -1.00 -1.00 3.99 

28 -1.00 -1.00 -1.00 -1.00 0.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

Map 1 = Distance from Archeological Sites, Map 2 = Elevation, Map 3 = Distance 

from Towns, Map 4 = Distance from 1970’s Agriculture, Map 5 = Distance from 

year-round streams 

Assessing spatial pattern driver significance 

To test statistically how well each candidate driver map (Figure 5) or combination of maps allows us to 

match the actual 2000 landscape, we simulated land-use change between 1995 and 2000 using one 

potential ‘driver’ at time, followed by multiple combinations of driver maps. The success of each driver in 

helping GEOMOD find the “right” cells in 2000 is measured by the kappa-for-location statistic. Different 

pattern drivers exhibit more or less ability to improve on projections depending on the municipio analyzed 

(Table 4). The individual most successful driver across all municipios in the north (kappa = 0.2307) was 

distance to archeological sites, while in the southern half of the region, ejidos were the most significant 

factor (kappa = 0.0927). We did not have complete ejido coverage in the north, but the area missing ejido 


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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 

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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). 


A- 17


Low Risk 

High Risk 

Figure 6. Map showing relative risk of deforestation across the Campeche region based on analysis 

of empirical areas of deforestation versus 13 spatially explicit candidate driver maps. Black (class 

0) represents areas already deforested in 2000, or water. 


A- 18


Figure 7. Risk of deforestation (Figure 7) condensed into 3 classes, high, medium and low based 

solely on effectiveness of a combination of drivers in predicting the validation landscape. No 

deforestation rate factor is incorporated in this schematic. 

IV. 

D. Derivation of land clearing rate 

To model land-use change spatially over time there must be empirical evidence of change in a region. We 

used imagery for only a 5-year time period (Figure 8) because 1) it was the most spatially congruent data 

set available, 2) it is the highest resolution data set available, and 3) we invested in the creation of the year 

2000 classification to the same level as the 1995 to have two maps that used the same classes. Both were 

based on a training set provided by Ron Eastman, Chair of the Geography Department, Clark University. 

To determine the deforestation/degradation pathway we created two land use classes (Figure 9). 

Agriculture, pasture and urban were already lumped in the 1995 classification. To this we added savanna, 

which generally is not found in the region naturally (INEGI 2000). Our own analysis shows land classified 

as ‘savanna’ returning to forest in 5 years, indicating it was pasture; it is often difficult to discriminate 

savanna from pasture in the classification process. The 1995 and 2000 images show different quantities of 

forest degradation depending on whether one includes secondary vegetation in the forested or deforested 

category (Figure 9). More than 395,000 ha of selva alta were converted to selva baja between 1995 and 

2000, while another 31,000 ha were converted to secondary vegetation (Table 6). Determining the ‘from’ 

carbon pool and the ‘to’ carbon pool, when only two pools are allowed by the model at a time is complicated 

by these factors and the various pathways are illustrated in Figure 9. Including secondary vegetation in the 


A- 19


‘to’ (deforested) pool reveals much greater pressure in this 5-year time step than is evident when it is 

considered part of the forest (Figure 9). 

1995 2000 

Figure 8. 1995 and 2000 Land Use/Land Cover classified from TM satellite imagery. 

Figure 8. 1995 and 2000 Land Use/Land Cover classified from TM satellite imagery. 


A- 20


Figure 9. Land use change (with reforesting cells excluded) – top) Class 1, forest, includes 

secondary vegetation; bottom) Class 2, deforested, includes secondary vegetation. 


A- 21


Table 6. Cross-tabulation results illustrate 1995 (columns) – 2000 (rows) land use conversion (ha) in 

Campeche from one land-use class to another. 

Water 

or No 

Selva Flood 

Secondary 

Data* Selva Alta Baja Zones* Savannah Vegetation Urban/Agri/Pasture Total 

Water or No Data 3879.81 0 0 0 0 0 0 3879.81 

Selva Alta 0 723734.37 96455.52 63.9 7194.24 23486.04 7325.82 858259.9 

Selva Baja 0 395437.23 251836.3 75.15 3192.03 4466.88 2530.35 657537.9 

Flood Zones 0 365.58 605.25 14.31 425.79 33.39 166.14 1610.46 

Savannah 0 19153.71 2931.03 151.38 898.83 1198.17 3036.42 27369.54 

Sec. Vegetation 0 31076.82 1889.46 79.65 682.47 12346.29 12945.06 59019.75 

Urban/Agri/Pasture 0 13741.56 2655.81 468.54 2386.89 5313.96 33906.6 58473.36 

Total 3879.81 1183509.27 356373.4 852.93 14780.25 46844.73 59910.39 166615 

*Herb/Shrub 

* All cells in both maps classed to 0 

Table 7. Forest vs. deforested lands 1995 and 2000 for each municipality. 

Summary without separation of north and south 

Rate does not incorporate reforestation observed between 1995 and 2000 

Municipio 1995 Cells 2000 CellsCells Lost per year 

Annual % 

change 

Total Hopelchen 1052971 978906 74065 14813 0.015 

Champoton 3838169 3550112 288057 57611 0.016 

Calakmul 12675743 12531548 144195 28839 0.002 

Escarcega 72898 72391 507 101 0.001 

Total 17639781 17132957 506824 101365 0.006 

Using the more conservative scenario, in which secondary vegetation is evaluated as part of the forest 

class, i.e. a carbon source, we apply a deforestation rate of 1.5% annually in the municipio of Hopelchen, 

1.6 percent in Champoton. 0.2 % in the Calakmul, and 0.1 % in Escarcega based on the five year rate 

observed in the satellite imagery (Table 7). 


A- 22


1,587,580 ha 4,514 ha (2.87%) 

34,462 ha 74,690 ha 

1,540,736 ha 72,148 ha 

48,821 ha 121,535 ha 

1,540,736 ha 39,102 ha 

20,834 ha 74,691 ha 

1,540,736 ha 33,046 ha 

27, 986 46,845 ha 

46,845 ha 6,512 ha 

13,627 ha 74,691 ha 

Figure 10. Changes in land cover in Campeche between selva alta and selva baja combined, secondary vegetation and 

deforested land 1995 – 2000. Arrows represent standing stocks of one 

A- 

cover; 

23 

bracketed number is amount of original 

Winrock 

converted 

International 

to other use.


IV. E. Simulation Results 

Quantity of land predicted to be cleared 

We used the empirical 1995 to 2000 annual rate found in Table 7 to project into the future. We predict a 

loss of 273,685 ha in the region (Table 8). with a total loss 12% more of its forest cover by 2020 and 18 % 

more by 2030 (Figure 11). Calakmul, the largest municipio in the region is projected to lose only 5% of its 

forest cover by year 2020 (Table 9). The model produced simulated maps of land use change for the years 

2000, 2005, 2010, 2015, 2020, 2025, 2030 (Figure 12). 

Table 8. Deforestation Projections 

Annual Rate 

of 

deforestation 

Total Ha 

Municipio (ha) 1995 2000 2005 2010 2015 2020 2025 2030Deforested 

Hopelchen 1333 94767 89925 83259 76593 69927 63262 56596 49930 39995 

Champoton 5185 345435 333102 307177 281252 255327 229402 203477 177552 155551 

Calakmul 259611408171146751 1133773 1120796 1107818 1094841 1081863 1068885 77865 

Escarcega 9 6561 6650 6604 6559 6513 6467 6422 6376 274 

Total 912315875801576428 1530814 1485200 1439586 1393971 1348357 1302743 273685 

Figure 11. Percent 2000 forest cover from 2000 – 2030. 

100 

80 

% 

60 

40 

Hopelchen 

Champoton 

Calakmul 

Escarcega 

Total 

20 

0 

2000 2005 2010 2015 2020 2025 2030 

Year 


A- 24


Table 9. Percent 2000 forest cover change. 

Municipio 2000 2005 2010 2015 2020 2025 2030 

Hopelchen 100 93 85 78 70 63 56 

Champoton 100 92 84 77 69 61 53 

Calakmul 100 99 98 97 95 94 93 

Escarcega 100 99 99 98 97 97 96 

Total 100 97 94 91 88 86 83 

Figure 12. Spatial simulation results for six five-year time periods from 2000 – 2025. 


A- 25


Carbon benefits 

GEOMOD predicts a carbon loss of 14.2 million tons C adjusted for reforestation to a net 

carbon loss of 8.9 million tons C for the deforestation baseline 2000-2030. The carbon 

values (DeJong, personal communication) used per vegetation type are summarized in Table 

10. Reforestation has not been modeled explicitly, but based on the reforestation rate 

observed between 1995 and 2000 (45% of cleared land) we have calculated a reforestation 

of 45% of the cells deforested in each previous time period and subtracted an amount equal 

to a regrowth rate of 1.9 t C/ha.yr (Brown, personal communication) from each 5-year time 

period’s carbon emissions. This reduction occurs only for the five year time period as we 

have no knowledge of the long-term trajectory of this land and assume for now that it is 

cleared cyclically. Total carbon emissions of 14.2 million tons C are thus reduced by 5.2 

million tons, as summarized in Table 11 and Figure 13. Over fifty percent of the carbon loss 

to the atmosphere is derived from the tall evergreen forest (Figure 13). No adjustment has 

been made for the carbon in the remaining vegetation nor for soil carbon. 

Table 10. Carbon values for Calakmul study area vegetation. 

VEGETATION TYPE CAT ID Tons C/ha Tons C /30x30 m CELL 

Selva Alta 1 66.5 5.985 

Selva Baja 2 42.6 3.834 

Secondary Vegetation 3 34.35.35 3.092 

Table 11. Carbon emissions projected for each 5-year period, by vegetation type. 

5-year C 

Emissions 2005 2010 2015 2020 2025 2030 Total 

Selva Alta 1053439 1262161 1298566 1330328 1634936 1327177 7906607 

Selva Baja 829540 828765 901508 890354 745376 964101 5159644 

Veg. Sec. 342980 232434 161664 160257 118927 103537 1119800 

Total 

Emissions 2225959 2323361 2361737 2380940 2499238 2394816 14186051 

Reforestation -369808 -573540 -776863 -981044 -1185998 -1390847 -5278100 

Net Emissions 1856151 1749821 1584874 1399896 1313240 1003969 8907951 


A- 26


5-year Cumulative Carbon Emissions (+) and Reductions (-) 

16 

14 

Selva Alta 

12 

10 

Selva Baja 

Tons C 

Millions 

8 

6 

4 

Veg. Sec. 

Total Emissions 

2 

0 

-2 

-4 

2005 2010 2015 2020 2025 2030 

Reforestation 

Reductions 

Total Carbon 

Sequestered 

Year 

Figure 13. Cumulative carbon emissions (+) and reductions (-) per vegetation type under a 30-year 

deforestation baseline scenario for the Calakmul region. 

V. APPLICATION of GEOMOD to predict the ‘business-asusual’ 

carbon baseline for Meseta Purépecha region of 

the State of Michoacan 

V.A. Project Area Description 

The Purépecha Region is situated in the western state of Michoacan in Mexico with an area of 

approximately 615,000 ha and a population of ca. 732,147, distributed over 927 communities in 19 

municipalities (Figure 14). Purépecha is the name of the dominant ethnic group in the region. The region 

is mountainous with an elevation range of 621–3,860 m (Figure 15), the product of recent volcanic activity 

resulting in Andosols as the dominant soil type. The climate is temperate sub-humid with an average 

rainfall between 800 and 1100 mm mainly concentrated in summer, and average temperatures between 

11°C and 14°C. However, the rough topography of the region results in a wide variety of microclimates. 

The vegetation in the area Purépecha according to INEGI, consists primarily of Pine-Oak forest, Pine 

forest, agriculture and permanent crops (Figure 14). 


A- 27


Figure 14. Vegetation map for the state of Michoacan. The region Meseta Purepecha and 

corresponding municipalities are outlined in black. 

Altitud 

0 10km 

Figure 15. Schematic of Tancitaro volcano, in the subregion Tancitaro with indication of 

elevation. 


A- 28


V.B. Data 

The data acquired mostly from INEGI included: 

• Roads. 

• Hydrography 

• Digital coverage of elevation contours 

• Soils classified by type 

• Community locations, including locations of the main towns in each municipio 

• Municipio boundaries 

• Protected area locations 

• Meteorological data, including mean minimum and mean maximum temperature, and precipitation 

measurements for both times (wet and dry season) 

Table 12: Legend showing vegetation types found in the Purépecha area and the classes 

to which they were assigned for modeling purposes (1 = forest, 2 = deforested, 0 = 

outside the study area) 

Cat ID Land Use/Land Cover Model Category 

1 Agricultura /Agriculture 2 

2 Agricultura de temporal/Permanent crops (orchards) 2 

3 Area sin vegetacion aparente/ without vegetation 2 

4 Area urbana/ Urban areas 2 

5 Bosque con vegetacion secundaria/Sec forest 1 

6 Bosque de Encino/ Oak forest 1 

7 Bosque de Oyamel /Oyamel forest 1 

8 Bosque de Pino /Pine forest 1 

9 Bosque de Pino-encino/ Pine-oak forest 1 

10 Cuerpos de agua/Water bodies 0 

11 Matorral/Shrubs 2 

12 Pastizal/Pasture 2 

13 Plantacion forestal/ Reforestation 1 

14 Otros tipos/Another 0 

15 Sin Clasificacion/ Without classification 0 

V.C. Data preparation 

All maps were analyzed for spatial congruence with each other and converted where necessary. All 

vector data were gridded in ESRI’s ARCVIEW 3.2 to match the grid proportions and resolution of the 

three time periods. These consist of 914 rows by 1245 columns, with a grid cell resolution of 100 meters 

by 100 meters. 

Elevation values ranged from 621 to 3821 meters. From the grid we calculated a slope and aspect map 

of the region. We then reclassified each land use map, the soils map, and the municipio map to 

represent land (1) and non-land (0) in order to create a ‘MASK’ that would ensure that information was 

available for analysis on all input maps. We did this by overlaying the binary maps until only non-zero 

cells coinciding on all maps remained. The final mask, therefore, included only those areas that were 

consistently identified as “land” in all maps. Finally the mask excluded two protected areas not included 

in the analysis. 

We created grid maps in ARCVIEW of the community and municipal seats (sedes) shape files and 

calculated distance from communities, municipal seats, rivers, roads and navigable water. Each ‘distance’ 


A- 29


map was classified into categories representing different distance intervals from each of these potentially 

important physical features (see Table 13). 

The final set of candidate driver maps (Table 13) includes the 5 ‘distance from’ maps, soils, slope, aspect, 

elevation, watersheds, municipalities, and ‘distance from 1976 deforested land”. 

Table 13. Category Delineation for Candidate Spatial Pattern Determinants 

# of 

Spatial Driver Range of Values Units Intervals 

Interval Width 

Conservation Areas 1-5 Nominal 5 1 

Municipios 1-19 Nominal 19 1 

Subregion 1-4 Nominal 4 1 

Aspect 0-360 Degrees 10 1 = flat, 2= 0-22.5°, 3-9=45°, 

10=22.5° 

Slope 52 Degrees 10 1 = 0°, 2-9 = 5°, 

10 = 10° 

Soil Type 12 Nominal 12 1 

Elevation 621.8-3835.7 Alt. (m) 20 200 

Water Dist 15368 Meters 32 500 

Commun. Dist1990 11475 Meters 29 400 

Dist_road 4709.575 Meters 32 150 

Dist. Deforest lands 4876.47 Meters 25 200 

The final maps had the following dimensions: 

columns : 1245 

rows : 914 

ref. system : Utm13n 

ref. units : meters 

unit dist. : 1.0000000 

min. X : 752795.941193 

max. X : 877295.941193 

min. Y : 2121635.935176 

max. Y : 2213035.935176 

cell resol. : 100 

All maps were then converted to ASCII format as required by GEOMOD. 

V.D. Calibration and Validation of Spatial Pattern Drivers 

Calibrating the candidate drivers of deforestation 

To understand those factors that explain the spatial distribution of forest clearing for human uses in 

Purépecha region, we analyzed four sub-regions. These are groups of municipalities that have biophysical 

characteristics in common and conform to the Purépecha region boundary. (Figure 16). 


A- 30


Región Meseta 

Región Uruapan 

Región Tancitaro 

Región 

Patzcuaro 

Figure 16. Map of the Purépecha region showing the four sub-regions used in the analysis. 

GEOMOD calculates the portion of each driver class that coincides with deforested cells in 1993 and then 

simulates a year 2000 landscape. The 1993 land use/vegetation cover map that was used in the analysis 

with the drivers is shown in Figure 17. 


A- 31


Figure 17. Map of deforested (urban, pasture, shrubs, agriculture, other, etc) versus forested 

lands in 1993. 

GEOMOD´s sr1.Out file (Table 14) reports the percent of each driver class that is already deforested in 

1993. These percentages are the weights applied to each remaining forest cell in that driver class. In 

each calibration run the final weight applied to each forested cell in the map is a combination of the 

weights of all the drivers being tested in that run 


A- 32


Table 14. Percent of each class of each driver already deforested in 1993. The set shown here is for 

the subregion Purepecha only. Results for each of the other sub-region are reported in full in the 

Appendix. A value of 0.0 indicates no cells in that class are deforested. A value of -1.0 indicates that 

there are no cells of that class. 

CATEGORY Dist_aga Droad_a Aspect Slope 

Soils dpob2 elev_a ppmo tmaxmo Regiones 

1 75.38 75.66 94.12 87.18 37.9 82.52 92.27 64.59 -1 41.58 

2 63.04 67.11 51.11 71.14 50.6 74.24 93.2 69.19 -1 62.48 

3 55.71 58.66 50.3 42.85 75.1 63.33 68.16 61.74 0 53.54 

4 50.78 51.61 48.02 20.97 71.5 54.84 61.55 44.1 10.98 52.78 

5 48.14 45.31 49.07 14.99 98 47.24 47.71 51.39 33.81 -1 

6 46.51 39.96 49.06 11.31 26.3 40.85 60.35 49.24 52.69 -1 

7 46.79 34.22 51.93 7.8 52.3 38.18 62.69 2.2 58.93 -1 

8 45.86 29.5 53.51 6.09 100 35.34 64.17 -1 60.88 -1 

9 44.04 25.8 53.96 6.41 66.2 31.29 49.55 -1 86.04 -1 

10 43.02 22.88 52.07 13.64 45.3 26.75 33.37 -1 100 -1 

11 45.84 20.28 -1 -1 92.3 25.02 34.57 -1 -1 -1 

12 45.95 18.03 -1 -1 95.4 24.78 16.92 -1 -1 -1 

13 48.15 16.26 -1 -1 -1 23.85 0.62 -1 -1 -1 

14 50.8 14.92 -1 -1 -1 25.12 0 -1 -1 -1 

15 49.22 14.53 -1 -1 -1 28.14 0 -1 -1 -1 

16 46.48 14.23 -1 -1 -1 23.74 -1 -1 -1 -1 

17 44.52 14.09 -1 -1 -1 14.9 -1 -1 -1 -1 

18 40.37 15.16 -1 -1 -1 16.78 -1 -1 -1 -1 

19 30.84 17.03 -1 -1 -1 20 -1 -1 -1 -1 

20 26.94 17.16 -1 -1 -1 6.08 -1 -1 -1 -1 

21 26.77 17.45 -1 -1 -1 0 -1 -1 -1 -1 

22 24.03 18.21 -1 -1 -1 0 -1 -1 -1 -1 

23 23.14 19.68 -1 -1 -1 -1 -1 -1 -1 -1 

24 22.81 21.47 -1 -1 -1 -1 -1 -1 -1 -1 

25 25.15 20 -1 -1 -1 -1 -1 -1 -1 -1 

26 30.31 16.12 -1 -1 -1 -1 -1 -1 -1 -1 

27 45.17 11.36 -1 -1 -1 -1 -1 -1 -1 -1 

28 53.08 9.05 -1 -1 -1 -1 -1 -1 -1 -1 

29 49.7 10.06 -1 -1 -1 -1 -1 -1 -1 -1 

30 34.66 5.1 -1 -1 -1 -1 -1 -1 -1 -1 

31 0.57 0 -1 -1 -1 -1 -1 -1 -1 -1 

32 0 0 -1 -1 -1 -1 -1 -1 -1 -1 

0 -1 -1 59.17 59.17 -1 -1 -1 -1 -1 -1 


A- 33


Assessing spatial pattern driver significance 

To test statistically how well each of a variety of candidate driver maps would allow us to match the actual 

2000 landscape, we simulated the 2000 landscape starting in 1993 creating a vulnerability map for each 

driver, and then for multiple combinations of driver maps. We have good results with the first 

reclassification of each distance driver. (Figure 18). 

Figure 18. Candidate spatial drivers maps 

As shown in Table 15, not enforcing the ‘neighborhood’ function we received the highest kappa-forlocation 

(0.15) using all 9 drivers. To see if we could improve our kappa statistic we again tested one 

driver at a time but this time enforcing the neighbourhood contiguity requirement. For each driver, 

GEOMOD calculated the percent correct and the kappa statistic, which were evaluated to see how well 

each driver performed by itself in explaining the variation (Table 16). Different pattern drivers exhibit 

more or less ability to improve on projections depending on the subregion analyzed (Table 17). The 

highest kappa (0.3529) was achieved in the Patzcuaro sub-region using slope, distance to roads, 

distance to water, soils and elevation. 


A- 34


Table 15. Deforestation driver combinations tested and weights per driver, using neighborhood=0. 

Neighborhood=0 

SPATIAL DRIVER MAPS 

% 

Correct Kappa 

Total # 

drivers Slope_a Dist_aga Droad_a Soils_a elev_a dpob2_a tmaxmo_a Aspect_a ppmo_a 

94.9237 0.1505 9 1 1 1 1 1 1 1 1 1 

94.7996 0.1298 5 0 1 0 1 1 1 1 0 0 

94.7522 0.1218 2 0 0 0 0 0 1 1 0 0 

94.7243 0.1172 3 0 0 1 1 1 0 0 0 0 

94.6775 0.1093 4 0 1 0 0 1 1 1 0 0 

94.6354 0.1023 

1 0 0 0 0 0 0 1 0 0 

Table 16. Deforestation driver combinations tested, weights per driver, % correct and ‘Kappafor-location’ 

for different sets of spatial pattern drivers tested for their ability to predict 

accurately the spatial distribution of deforestation in the 2000 land use map 


% 

Correct Kappa 

SPATIAL DRIVER MAPS 

Total # 

drivers Slope_a Dist_aga Droad_a Soils_a elev_a dpob2_a tmaxmo_a Disd76a** Aspect_a ppmo_a 

95.6265 0.2681 2 1 1 0 0 0 0 0 0 0 0 

95.6059 0.2647 1 1 0 0 0 0 0 0 0 0 0 

95.5866 0.2615 4 1 1 1 1 0 0 0 0 0 0 

95.5784 0.2601 3 1 1 1 0 0 0 0 0 0 0 

95.5638 0.2576 5 1 1 1 1 1 0 0 0 0 0 

95.5412 0.2539 4 1 0 1 0 0 1 0 1 0 0 

95.5319 0.2523 3 0 1 1 0 0 1 0 0 0 0 

95.5239 0.251 9 1 1 1 1 1 1 1 0 1 1 


A- 35


Table 17. Results for each deforestation pattern driver and combinations of drivers tested (the cells 

colored red, blue and yellow indicate the three drivers, in importance from high to low respectively, 

giving the highest Kappa) 

Rank 

Driver 

REGION URUAPAN TANCITA PATZCUARO MESETA 

% 

Correct Kappa 

% 

Correct Kappa 

% 

Correct 

Kappa 

% 

Correct Kappa 

% 

Correct Kappa 

1 Slope_a 95.6059 0.2647 94.646 0.2428 98.1603 0.1619 93.8732 0.3439 96.1122 0.2109 

2 Dist_aga 95.5465 0.2548 94.6017 0.2365 98.0944 0.1319 93.8372 0.3401 96.0307 0.1943 

3 Droad_a 95.5329 0.2525 94.4603 0.2165 98.1434 0.1542 93.784 0.3344 96.0982 0.208 

4 Soils_a 95.5286 0.2518 94.6599 0.2447 98.0925 0.131 93.6272 0.3176 96.0785 0.204 

5 9 drivers 95.5239 0.251 94.4783 0.219 98.23 0.1936 93.9308 0.3501 95.9219 0.1722 

6 elev_a 95.4981 0.2466 94.5532 0.2296 98.2243 0.1911 93.9221 0.3492 95.8065 0.1488 

7 dpob2_a 95.4884 0.245 94.4284 0.212 98.1076 0.1379 93.8185 0.3381 95.9894 0.1859 

8 tmaxmo_a 95.4781 0.2433 94.5448 0.2285 98.2262 0.1919 93.8876 0.3455 95.7775 0.1429 

9 Disd76a** 95.4742 0.2426 94.452 0.2153 98.0812 0.1259 93.6113 0.3159 96.0813 0.2046 

10 Aspect_a 95.4682 0.2416 94.4228 0.2112 98.0455 0.1096 93.5164 0.3057 96.1638 0.2213 

11 ppmo_a 95.4622 0.2406 94.1678 0.1751 98.213 0.1859 93.8416 0.3406 96.0241 0.193 

Combinati 

ons 

Neighborh 

ood=1 

1,2 2 drivers 95.6265 0.2681 94.7209 0.2534 98.1961 0.1782 93.9164 0.3486 96.0738 0.2031 

1,2,3,4 4 drivers 95.5866 0.2615 94.5629 0.231 98.1697 0.1662 93.9005 0.3469 96.0916 0.2067 

1,2,3 3 drivers 95.5784 0.2601 94.5476 0.2288 98.1566 0.1602 93.8905 0.3458 96.0916 0.2067 

1,2,3,4 

,6 5 drivers 95.5638 0.2576 94.5629 0.231 98.2526 0.2039 93.9567 0.3529 95.9491 0.1778 

1,3,7,9 4 drivers 95.5412 0.2539 94.409 0.2092 98.1886 0.1748 93.866 0.3432 96.0804 0.2044 

2,7,3 3 drivers 95.5319 0.2523 94.4464 0.2145 98.1302 0.1482 93.8444 0.3409 96.0719 0.2027 

3,7,6 3 drivers 95.4848 0.2444 94.4062 0.2088 98.1641 0.1636 93.8689 0.3435 95.9331 0.1745 

After calculating individual driver weights we began validating our results through the addition of drivers, 

one driver at a time. When used neighbourhood =0, the combination of each driver improved the ability to 

replicate the actual 2000 landscape. But the best results was obtained with neighbourhood=1, and 

combinations of two of the drivers. The best over all regions kappa-for-location statistic achieved was 

0.2681 with 95.6% of the cells allocated correctly. 


A- 36


Results of driver assessment – identification of areas most likely to be cleared 

GEOMOD driver weighting revealed a number of interesting facts about forest clearing in this region 

(Figure 19, Appendix). 

Figure 19. Map showing relative risk to deforestation based on analysis of empirical areas of 

deforestation versus two spatially explicit candidate drivers. 

V.E. Rate estimation 

Derivation of land clearing rate 

As we see above, GEOMOD can evaluate change in two land-use types at a time. Therefore, each map 

of land-use change must be reclassified similarly. The most common is to classify all undisturbed forest 

as type 1, and all other land-use types, which can be characterized as having undergone some human 

intervention such as urban and agricultural areas, as type 2. In addition land that will not be evaluated 

such as land that is unlikely to ever used by humans for productive activity , can be excluded from the 

analysis. So, a normal map ready to input to GEOMOD will usually consist of four possible values as 

follows (Fig 20 and 21) 

1.Forest 

2.No forest 

3 Areas to be excluded (unlikely to be deforested) 

4 Outside region of interest. 


A- 37


Figure 20. Map showing the 

reclassified land use. This map 

corresponds to year 1993. 

Figure 21. Map corresponds to 2000 

Using an area calculation we can determine how many cells of forest existed in each period. Subtraction 

of the second from the first tells us how many cells were deforested in the interim period. This number 

times the area per cell yields total area deforested. When we divide the area by the number of years we 

have our rate per year (Table 17), which for the region totals 2,790 ha/year between 1993 and 2000. 


A- 38


Table 17. Rate of deforestation for the Meseta Purepecha region and for each of the four 

subregions 

Subregión 

Forest 

cover 

1993 

Forest 

cover 

2000 

Change 

(A1-A2) 

Rate 

Ha/year 

Uruapan 84,274 78,821 5,453 779 

Tancitaro 39,854 38,652 1,202 172 

Patzcuaro 64,578 57,258 7,320 1,045 

Meseta 100,707 95,146 5,561 794 

TOTAL 2,790 

V.F. Simulation Results. 

Quantity of land predicted to be cleared 

We used the empirical annual rate to project into the future. We predict a loss of 83,717 ha in the region, 

with a total loss of 20% more of its forest cover by 2020 and 32% more by 2030 (Figure 22). The 

Patzcuaro region has the highest rate of loss and is projected to lose 54% of its forest cover by year 

2030. Tancitaro has a rate of forest loss of about an order of magnitude less than Patzcuaro (Tables 17 

and 18). 

Table 18. Cumulative percent change in the 2000 forest cover for each time interval 

2000 2005 2010 2015 2020 2025 2030 

URUAPAN 100.0% 95.1% 90.1% 85.2% 80.2% 75.3% 70.3% 

TANCITARO 100.0% 97.8% 95.6% 93.3% 91.1% 88.9% 86.7% 

PATZCUARO 100.0% 90.9% 81.8% 72.6% 63.5% 54.4% 45.3% 

MESETA 100.0% 95.8% 91.6% 87.5% 83.3% 79.1% 74.9% 

Total 100.0% 94.8% 89.7% 84.5% 79.3% 74.1% 69.0% 


A- 39


300000 

250000 

Ha 

200000 

150000 

100000 

50000 

URUAPAN 

TANCITA 

PATZCUARO 

MESETA 

Total region 

0 

2000 2005 2010 2015 2020 2025 2030 

Year 

Figure 22. Rates of deforestation for the Meseta Purepecha, including the four subregions, 

between 2000 and 2030. 

The model produced simulated maps of land use change for the years 2000, 2005, 2010, 2015, 2020, 

2025, 2030 (Figure 23) 


A- 40


No data 

No bosques 

Bosques 

Fuera de Análisis 

Figure 23. Simulated maps of change in forest cover for six 5-year time periods: 2000, 2010, 2015, 

2020,2025, 2030 

The spatial pattern of deforestation illustrates the higher rates of forest loss in Patzcuaro, with both 

outright clearing of smaller forest patches and encroachment into the edges of the remaining forests 

(Figure 23). 


A- 41


Changes in carbon stocks 

The carbon density estimates used per vegetation type are summarized in Table 19 (from A. Ordoñez, 

2003, personal communication). 

Table 19. Carbon density estimates for vegetation found in the region Purepecha. 

Vegetation Type 

T C/ha 

Agriculture 4 

Pasture 5 

Shrubs 7 

Forest Plantation 39 

Permanent Crops (Orchards) 42 

Pine-Oak Forest 77 

Pine Forests 84 

Oak Forests 95 

Oyamel Forest 110 

The change in forest cover was then matched with data on carbon stocks of the various vegetation types. 

A map of carbon densities is shown in Figure 24. 

Figure 24. Biomass carbon density map for the Meseta Purépecha region. 

The map of the rate of change in land-use was combined with the carbon density map to generate a 

graph of carbon stocks through time (Figure 25). Most of the change in stock occurs in the pine-oak 

forest as this forest type is converted to lower stock agriculture or avocado plantations. 


A- 42


Figure 25. Change in carbon stocks in the vegetation of the Meseta Purepecha region in response 

to loss in forest cover and conversion to land uses with lower carbon stocks. 


A- 43


Millones de ton de Carbono 

5.00 

4.50 

4.00 

3.50 

3.00 

2.50 

2.00 

1.50 

1.00 

0.50 

Oyamel Forest 

Forest Plantation 

Oak Forests 

Pine Forests 

Pine-Oak Forest 

0.00 

2000 2005 2010 2015 2020 2025 2030 

Year 

Figure 26. Cumulative carbon emissions from deforestation in the Meseta Purepecha. Over the 

30-year period, about 4.6 million tons of carbon would be emitted. 

Over the 30 year period, 83,717 ha of forest cover was projected to be converted to non-forest uses in the 

region, emitting about 4.6 million tons of carbon. 

VI. 

Conclusions 

Spatial modeling tools have allowed us to evaluate the empirical rate of land-use change and 

corresponding changes in carbon stocks in the States of Campeche and Michoacan. They 

have also provided the means to study the dynamics between land use/land cover types over 

time. The results show that a projected baseline for deforestation in Meseta Purépecha 

would result in about an additional 87,000 ha of loss in forest cover with a corresponding 

carbon emissions of 4.6 million t C. The projected baseline for the Calakmul region of 

Campeche shows a further loss of 273,000 ha of forest and a corresponding net carbon 

emissions of 8.9 million t C. 

Without GEOMOD we would not have been able to specify the location of projected 

deforestation in the regions. We hypothesized that using GEOMOD would allow us to 

estimate carbon emissions with greater certainty than can be done with a simple baseline 

prediction. GEOMOD provides more accurate carbon estimates because of the model’s 

spatial specificity, 2) provides the ability to model regional and project-specific ‘withoutproject’ 

scenarios in one pass, and 3) removes some of the uncertainty inherent in all 

modeling, whether spatial or non-spatial, by its strict adherence to the use of the kappa-forlocation 

statistic applied to empirical patterns of land use change 


A- 44


Literature Cited 

Costanza, R., (1989). “Model goodness of fit: a multiple resolution procedure”. Ecological Modeling, 47: 

199-215. 

Hall, C. A. S., H. Tian, Y. Qi, G. Pontius, J. Cornell and J. Uhlig, (1995a). “Spatially explicit models of 

land use change and their application to the tropics”. DOE Research Summary, No. 31. (Ed. By 

CDIAC, Oak Ridge National Lab). 

Hall, C. A. S., H. Tian, Y. Qi, G. Pontius, J. Cornell and J. Uhlig, (1995b). “Modeling spatial and temporal 

patterns of tropical land use change”. J. of Biogeography, 22, 753-757. 

Hall, Cleveland and Kaufmann (1986). Energy and Resource Quality: The Ecology of the Economic 

Process. Wiley-Interscience, New York. 

Hall, M.H.P, C.A.S. Hall and M.R. Taylor (2000) Geographical Modeling: the synthesis of GIS and 

simulation modeling. Chapter. 7, in C. A. S. Hall, (Ed.) Quantifying Sustainable Development: the 

Future of Tropical Economies. Academic Press, San Diego, CA. 

IBGE, (Instituto Brasileiro de Geografia e Estastíca) 

http://www.ibge.gov.br/espanhol/estatistica/economia/agropecuaria/censoagro/41/d41_t01.shtm 

Kerr, Suzi (2001). “Seeing the Forest and Saving the Trees: Tropical Land Use Change and Global 

Climate Policy”. Can Carbon Sinks be Operational? Resources for the Future Workshop 

Proceedings, April 30, 2001. RFF web site. http://www.rff.org/disc_papers/PDF_files/0126.pdf 

Moura-Costa, Pedro (2001). “Elements of a Certification System for Forestry-Based Greenhouse Gas 

Mitigation Projects”. Can Carbon Sinks be Operational? Resources for the Future Workshop 

Proceedings, April 30, 2001. Resources For the Future web site. 

http://www.rff.org/disc_papers/PDF_files/0126.pdf 

Odum, H. T. (1983). Systems Ecology. Wiley Interscience, New York. 

Pathfinder Project (1999). http://www.geog.umd.edu/tropical/method.html 

Pontius, R. G. Jr. (2000). “Quantification error versus location error in comparison of categorical maps”. 

Photogrammetric Engineering & Remote Sensing 66(8) pp. 1011-1016. 

Pontius Jr. R.G. and Schneider, L, in press. “Land-use change model validation by an ROC method”. 

Agriculture, Ecosystems & Environment. 

Pontius, R.G. Jr., L. Claessens, C. Hopkinson Jr., A. Marzouk, E.B. Rastetter, L.C. Schneider, J. Vallino 

(2000). “Scenarios of land-use change and nitrogen release in the Ipswich watershed, 

Massachusetts, USA”. 4th International Conference on Integrating GIS and Environmental Modeling 

(GIS/EM4): Problems, Prospects and Research Needs. Banff, Alberta, Canada, September 2 - 8, 

2000. http://www.colorado.edu/research/cires/banff/upload/6/ 

Pontius, R.G. Jr., J. Cornell, C. Hall (2001). “Modeling the spatial pattern of land-use change with 

GEOMOD: application and validation for Costa Rica”. Agriculture, Ecosystems & Environment 85(1- 

3) p. 191-203. 


A- 45


APPENDIX 

Calakmul Region 

_____________________________________________________________________________________________________ 

Table 1. Individual Driver Category Weights (% of class deforested at time of calibration) for drivers used in the 

north and the south 

_____________________________________________________________________________________________________ 

DRIVERS RETURNING HIGHEST KAPPA IN THE NORTH 

1 Distance from Archeological Sites 

2 Elevation 

3 Distance from Towns 

4 Distance from 1970’s Agriculture 

5 Distance from year-round streams 

REGION 1: Hopelchen 


1 0.00 -1.00 11.65 2.82 0.45 

2 0.00 -1.00 6.46 1.81 1.05 

3 0.13 -1.00 2.37 1.22 1.32 

4 0.40 -1.00 6.64 0.25 1.68 

5 0.35 0.00 4.97 0.49 1.52 

6 1.25 9.02 0.20 0.69 0.00 

7 1.44 3.69 0.48 0.69 0.00 

8 0.47 2.68 0.16 2.62 0.16 

9 2.27 0.68 0.11 5.95 0.18 

10 1.34 0.30 0.33 1.30 0.58 

11 4.39 0.19 0.52 16.10 3.72 

12 3.37 0.02 1.61 -1.00 5.32 

13 4.35 0.00 0.00 -1.00 5.54 

14 12.24 0.00 -1.00 -1.00 3.36 

15 0.00 0.00 -1.00 -1.00 1.27 

16 -1.00 -1.00 -1.00 -1.00 2.89 

17 -1.00 -1.00 -1.00 -1.00 6.99 

18 -1.00 -1.00 -1.00 -1.00 9.89 

19 -1.00 -1.00 -1.00 -1.00 17.45 

20 -1.00 -1.00 -1.00 -1.00 6.93 

21 -1.00 -1.00 -1.00 -1.00 0.00 

22 -1.00 -1.00 -1.00 -1.00 -1.00 

23 -1.00 -1.00 -1.00 -1.00 -1.00 

24 -1.00 -1.00 -1.00 -1.00 -1.00 


A- 46


25 -1.00 -1.00 -1.00 -1.00 -1.00 

26 -1.00 -1.00 -1.00 -1.00 -1.00 

27 -1.00 -1.00 -1.00 -1.00 -1.00 

28 -1.00 -1.00 -1.00 -1.00 -1.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 



1 1.79 39.13 48.30 35.83 24.38 

2 0.71 51.09 20.01 8.68 10.14 

3 3.69 10.24 5.93 5.80 5.90 

4 3.14 5.23 3.77 3.14 0.19 

5 2.66 5.80 3.30 0.65 0.11 

6 1.80 1.39 1.75 0.29 0.21 

7 4.00 0.41 0.10 0.35 0.83 

8 2.66 1.67 0.44 0.44 0.65 

9 4.88 0.58 0.69 0.04 1.10 

10 9.43 0.11 0.75 0.02 1.60 

11 11.95 0.01 0.01 1.92 4.54 

12 12.79 0.00 0.00 0.00 5.58 

13 14.23 0.00 0.00 0.00 8.70 

14 18.66 0.00 0.00 0.00 9.90 

15 17.63 0.00 0.00 -1.00 10.21 

16 18.77 0.00 0.00 -1.00 7.12 

17 24.26 0.00 0.00 -1.00 8.14 

18 47.12 -1.00 0.00 -1.00 8.02 

19 93.28 -1.00 0.00 -1.00 12.70 

20 -1.00 -1.00 -1.00 -1.00 18.94 

21 -1.00 -1.00 -1.00 -1.00 27.91 

22 -1.00 -1.00 -1.00 -1.00 29.15 

23 -1.00 -1.00 -1.00 -1.00 27.34 

24 -1.00 -1.00 -1.00 -1.00 38.58 

25 -1.00 -1.00 -1.00 -1.00 44.94 

26 -1.00 -1.00 -1.00 -1.00 22.72 

27 -1.00 -1.00 -1.00 -1.00 3.99 

28 -1.00 -1.00 -1.00 -1.00 0.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

REGION 3: Calakmul 


1 0.42 -1.00 2.20 0.33 0.12 

2 0.14 -1.00 0.06 0.29 0.22 


A- 47


3 0.09 0.00 0.09 0.35 0.10 

4 0.21 0.00 0.01 0.38 0.02 

5 0.02 0.00 0.08 0.21 0.00 

6 0.34 0.00 0.01 0.00 0.20 

7 0.17 0.00 0.00 0.03 0.37 

8 0.06 0.06 0.00 0.00 0.75 

9 0.27 0.35 0.00 0.00 0.41 

10 0.05 0.36 0.00 0.00 0.00 

11 0.00 0.18 0.04 0.00 -1.00 

12 0.00 0.12 0.06 0.00 -1.00 

13 -1.00 0.09 0.00 0.00 -1.00 

14 -1.00 0.20 0.00 0.00 -1.00 

15 -1.00 0.04 0.00 -1.00 -1.00 

16 -1.00 0.00 0.00 -1.00 -1.00 

17 -1.00 0.00 0.00 -1.00 -1.00 

18 -1.00 -1.00 0.00 -1.00 -1.00 

19 -1.00 -1.00 0.00 -1.00 -1.00 

20 -1.00 -1.00 -1.00 -1.00 -1.00 

21 -1.00 -1.00 -1.00 -1.00 -1.00 

22 -1.00 -1.00 -1.00 -1.00 -1.00 

23 -1.00 -1.00 -1.00 -1.00 -1.00 

24 -1.00 -1.00 -1.00 -1.00 -1.00 

25 -1.00 -1.00 -1.00 -1.00 -1.00 

26 -1.00 -1.00 -1.00 -1.00 -1.00 

27 -1.00 -1.00 -1.00 -1.00 -1.00 

28 -1.00 -1.00 -1.00 -1.00 -1.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

REGION 4: Escarcega 


1 -1.00 -1.00 -1.00 0.00 -1.00 

2 -1.00 -1.00 1.84 1.01 -1.00 

3 -1.00 -1.00 0.17 0.00 -1.00 

4 -1.00 0.68 0.00 0.00 -1.00 

5 0.00 0.15 0.00 -1.00 -1.00 

6 1.09 0.00 -1.00 -1.00 -1.00 

7 0.00 0.00 -1.00 -1.00 -1.00 

8 0.00 0.00 -1.00 -1.00 -1.00 

9 -1.00 -1.00 -1.00 -1.00 -1.00 

10 -1.00 -1.00 -1.00 -1.00 -1.00 

11 -1.00 -1.00 -1.00 -1.00 -1.00 


A- 48


12 -1.00 -1.00 -1.00 -1.00 -1.00 

13 -1.00 -1.00 -1.00 -1.00 -1.00 

14 -1.00 -1.00 -1.00 -1.00 -1.00 

15 -1.00 -1.00 -1.00 -1.00 -1.00 

16 -1.00 -1.00 -1.00 -1.00 -1.00 

17 -1.00 -1.00 -1.00 -1.00 -1.00 

18 -1.00 -1.00 -1.00 -1.00 0.00 

19 -1.00 -1.00 -1.00 -1.00 0.06 

20 -1.00 -1.00 -1.00 -1.00 0.81 

21 -1.00 -1.00 -1.00 -1.00 1.27 

22 -1.00 -1.00 -1.00 -1.00 0.10 

23 -1.00 -1.00 -1.00 -1.00 0.00 

24 -1.00 -1.00 -1.00 -1.00 0.00 

25 -1.00 -1.00 -1.00 -1.00 0.00 

26 -1.00 -1.00 -1.00 -1.00 0.00 

27 -1.00 -1.00 -1.00 -1.00 0.00 

28 -1.00 -1.00 -1.00 -1.00 -1.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

DRIVERS RETURNING HIGHEST KAPPA IN THE SOUTH 

Map 1 = EJIDOS 

Map 2 = Distance from Year-round STREAMS 

Map 3 = Distance from ROADS 

Map 4 = Distance from Arqueological Sites 

Map 5 = Distance from TOWNS 



1 0.93 0.13 4.88 -1.00 8.52 

2 -1.00 0.22 4.69 -1.00 2.92 

3 -1.00 0.57 0.07 -1.00 8.18 

4 -1.00 0.46 0.00 -1.00 0.63 

5 -1.00 0.45 0.00 -1.00 0.00 

6 -1.00 0.01 0.00 -1.00 0.00 

7 -1.00 1.49 0.58 -1.00 0.36 

8 -1.00 0.32 1.22 -1.00 0.00 

9 -1.00 16.31 0.34 0.19 0.00 

10 -1.00 22.69 0.00 1.13 0.00 

11 -1.00 13.92 0.00 15.25 0.00 

12 -1.00 22.72 0.00 24.88 0.00 


A- 49


13 -1.00 1.14 11.14 0.00 0.00 

14 -1.00 -1.00 0.00 0.00 -1.00 

15 -1.00 -1.00 0.00 0.00 -1.00 

16 -1.00 -1.00 0.00 0.00 -1.00 

17 -1.00 -1.00 7.99 0.00 -1.00 

18 0.00 -1.00 0.00 0.00 -1.00 

19 2.57 -1.00 0.00 0.26 -1.00 

20 0.68 -1.00 0.00 2.31 -1.00 

21 -1.00 -1.00 17.25 5.08 -1.00 

22 -1.00 -1.00 0.30 0.00 -1.00 

23 -1.00 -1.00 0.00 -1.00 -1.00 

24 -1.00 -1.00 0.00 -1.00 -1.00 

25 -1.00 -1.00 0.00 -1.00 -1.00 

26 -1.00 -1.00 -1.00 -1.00 -1.00 

27 -1.00 -1.00 -1.00 -1.00 -1.00 

28 -1.00 -1.00 -1.00 -1.00 -1.00 

29 -1.00 -1.00 -1.00 -1.00 -1.00 

30 -1.00 -1.00 -1.00 -1.00 -1.00 

31 -1.00 -1.00 -1.00 -1.00 -1.00 

32 -1.00 -1.00 -1.00 -1.00 -1.00 

33 0.00 -1.00 -1.00 -1.00 -1.00 

34 -1.00 -1.00 -1.00 -1.00 -1.00 

35 -1.00 -1.00 -1.00 -1.00 -1.00 

36 -1.00 -1.00 -1.00 -1.00 -1.00 

37 -1.00 -1.00 -1.00 -1.00 -1.00 

38 -1.00 -1.00 -1.00 -1.00 -1.00 

39 -1.00 -1.00 -1.00 -1.00 -1.00 

40 -1.00 -1.00 -1.00 -1.00 -1.00 

41 -1.00 -1.00 -1.00 -1.00 -1.00 

42 -1.00 -1.00 -1.00 -1.00 -1.00 

43 -1.00 -1.00 -1.00 -1.00 -1.00 

44 -1.00 -1.00 -1.00 -1.00 -1.00 

45 -1.00 -1.00 -1.00 -1.00 -1.00 

46 -1.00 -1.00 -1.00 -1.00 -1.00 

47 -1.00 -1.00 -1.00 -1.00 -1.00 

48 -1.00 -1.00 -1.00 -1.00 -1.00 

49 22.57 -1.00 -1.00 -1.00 -1.00 

50 0.00 -1.00 -1.00 -1.00 -1.00 

51 0.00 -1.00 -1.00 -1.00 -1.00 

52 -1.00 -1.00 -1.00 -1.00 -1.00 


A- 50


53 -1.00 -1.00 -1.00 -1.00 -1.00 

54 -1.00 -1.00 -1.00 -1.00 -1.00 

55 -1.00 -1.00 -1.00 -1.00 -1.00 

56 -1.00 -1.00 -1.00 -1.00 -1.00 

57 -1.00 -1.00 -1.00 -1.00 -1.00 

58 -1.00 -1.00 -1.00 -1.00 -1.00 

59 -1.00 -1.00 -1.00 -1.00 -1.00 

60 -1.00 -1.00 -1.00 -1.00 -1.00 

61 -1.00 -1.00 -1.00 -1.00 -1.00 

62 -1.00 -1.00 -1.00 -1.00 -1.00 

63 -1.00 -1.00 -1.00 -1.00 -1.00 

64 -1.00 -1.00 -1.00 -1.00 -1.00 

65 -1.00 -1.00 -1.00 -1.00 -1.00 

66 -1.00 -1.00 -1.00 -1.00 -1.00 

67 -1.00 -1.00 -1.00 -1.00 -1.00 

68 -1.00 -1.00 -1.00 -1.00 -1.00 

69 -1.00 -1.00 -1.00 -1.00 -1.00 

70 -1.00 -1.00 -1.00 -1.00 -1.00 

71 -1.00 -1.00 -1.00 -1.00 -1.00 

72 -1.00 -1.00 -1.00 -1.00 -1.00 

73 -1.00 -1.00 -1.00 -1.00 -1.00 

74 -1.00 -1.00 -1.00 -1.00 -1.00 

75 -1.00 -1.00 -1.00 -1.00 -1.00 

76 -1.00 -1.00 -1.00 -1.00 -1.00 

77 -1.00 -1.00 -1.00 -1.00 -1.00 

78 -1.00 -1.00 -1.00 -1.00 -1.00 

79 -1.00 -1.00 -1.00 -1.00 -1.00 

80 -1.00 -1.00 -1.00 -1.00 -1.00 

81 -1.00 -1.00 -1.00 -1.00 -1.00 

82 -1.00 -1.00 -1.00 -1.00 -1.00 

83 -1.00 -1.00 -1.00 -1.00 -1.00 

84 -1.00 -1.00 -1.00 -1.00 -1.00 

85 -1.00 -1.00 -1.00 -1.00 -1.00 

86 -1.00 -1.00 -1.00 -1.00 -1.00 

87 -1.00 -1.00 -1.00 -1.00 -1.00 

88 -1.00 -1.00 -1.00 -1.00 -1.00 

89 -1.00 -1.00 -1.00 -1.00 -1.00 

90 -1.00 -1.00 -1.00 -1.00 -1.00 

91 -1.00 -1.00 -1.00 -1.00 -1.00 

92 -1.00 -1.00 -1.00 -1.00 -1.00 


A- 51


93 -1.00 -1.00 -1.00 -1.00 -1.00 

94 -1.00 -1.00 -1.00 -1.00 -1.00 

95 -1.00 -1.00 -1.00 -1.00 -1.00 

96 -1.00 -1.00 -1.00 -1.00 -1.00 

97 -1.00 -1.00 -1.00 -1.00 -1.00 

98 -1.00 -1.00 -1.00 -1.00 -1.00 

99 -1.00 -1.00 -1.00 -1.00 -1.00 

100 -1.00 -1.00 -1.00 -1.00 -1.00 

101 -1.00 -1.00 -1.00 -1.00 -1.00 

102 -1.00 -1.00 -1.00 -1.00 -1.00 

103 -1.00 -1.00 -1.00 -1.00 -1.00 

104 -1.00 -1.00 -1.00 -1.00 -1.00 

105 -1.00 -1.00 -1.00 -1.00 -1.00 

106 -1.00 -1.00 -1.00 -1.00 -1.00 

107 -1.00 -1.00 -1.00 -1.00 -1.00 

108 -1.00 -1.00 -1.00 -1.00 -1.00 

109 -1.00 -1.00 -1.00 -1.00 -1.00 

110 -1.00 -1.00 -1.00 -1.00 -1.00 

111 -1.00 -1.00 -1.00 -1.00 -1.00 

112 -1.00 -1.00 -1.00 -1.00 -1.00 

113 -1.00 -1.00 -1.00 -1.00 -1.00 

114 -1.00 -1.00 -1.00 -1.00 -1.00 

115 -1.00 -1.00 -1.00 -1.00 -1.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

REGION 2: Calakmul 


1 0.63 0.25 0.67 2.13 2.97 

2 -1.00 0.39 0.32 0.38 0.78 

3 0.56 0.62 0.25 0.54 0.36 

4 -1.00 0.80 0.29 0.40 0.30 

5 -1.00 2.18 0.72 0.19 0.22 

6 -1.00 2.24 0.26 0.50 0.10 

7 -1.00 4.93 0.45 0.69 0.00 

8 -1.00 13.24 0.21 0.26 0.05 

9 -1.00 31.43 0.01 0.07 0.00 

10 -1.00 24.16 0.05 0.08 0.00 

11 13.67 1.18 0.35 1.11 0.00 

12 16.93 -1.00 0.15 1.23 0.02 

13 8.81 -1.00 1.63 1.26 0.01 

14 1.24 -1.00 0.61 0.18 0.00 


A- 52


15 -1.00 -1.00 2.08 0.41 0.00 

16 -1.00 -1.00 1.32 0.30 0.00 

17 -1.00 -1.00 1.19 0.09 -1.00 

18 0.02 -1.00 2.52 0.15 -1.00 

19 18.12 -1.00 0.19 1.24 -1.00 

20 -1.00 -1.00 0.47 2.26 -1.00 

21 -1.00 -1.00 2.58 1.22 -1.00 

22 1.99 -1.00 0.00 0.00 -1.00 

23 -1.00 -1.00 0.00 -1.00 -1.00 

24 -1.00 -1.00 0.00 -1.00 -1.00 

25 -1.00 -1.00 0.00 -1.00 -1.00 

26 -1.00 -1.00 -1.00 -1.00 -1.00 

27 -1.00 -1.00 -1.00 -1.00 -1.00 

28 0.71 -1.00 -1.00 -1.00 -1.00 

29 0.00 -1.00 -1.00 -1.00 -1.00 

30 0.38 -1.00 -1.00 -1.00 -1.00 

31 0.00 -1.00 -1.00 -1.00 -1.00 

32 -1.00 -1.00 -1.00 -1.00 -1.00 

33 0.71 -1.00 -1.00 -1.00 -1.00 

34 0.07 -1.00 -1.00 -1.00 -1.00 

35 0.00 -1.00 -1.00 -1.00 -1.00 

36 0.00 -1.00 -1.00 -1.00 -1.00 

37 0.00 -1.00 -1.00 -1.00 -1.00 

38 0.04 -1.00 -1.00 -1.00 -1.00 

39 0.00 -1.00 -1.00 -1.00 -1.00 

40 0.00 -1.00 -1.00 -1.00 -1.00 

41 0.00 -1.00 -1.00 -1.00 -1.00 

42 0.00 -1.00 -1.00 -1.00 -1.00 

43 0.02 -1.00 -1.00 -1.00 -1.00 

44 0.00 -1.00 -1.00 -1.00 -1.00 

45 0.00 -1.00 -1.00 -1.00 -1.00 

46 0.00 -1.00 -1.00 -1.00 -1.00 

47 0.08 -1.00 -1.00 -1.00 -1.00 

48 0.09 -1.00 -1.00 -1.00 -1.00 

49 20.49 -1.00 -1.00 -1.00 -1.00 

50 0.00 -1.00 -1.00 -1.00 -1.00 

51 0.00 -1.00 -1.00 -1.00 -1.00 

52 -1.00 -1.00 -1.00 -1.00 -1.00 

53 -1.00 -1.00 -1.00 -1.00 -1.00 

54 -1.00 -1.00 -1.00 -1.00 -1.00 


A- 53


55 -1.00 -1.00 -1.00 -1.00 -1.00 

56 -1.00 -1.00 -1.00 -1.00 -1.00 

57 -1.00 -1.00 -1.00 -1.00 -1.00 

58 -1.00 -1.00 -1.00 -1.00 -1.00 

59 0.00 -1.00 -1.00 -1.00 -1.00 

60 3.47 -1.00 -1.00 -1.00 -1.00 

61 14.44 -1.00 -1.00 -1.00 -1.00 

62 28.87 -1.00 -1.00 -1.00 -1.00 

63 4.11 -1.00 -1.00 -1.00 -1.00 

64 1.35 -1.00 -1.00 -1.00 -1.00 

65 1.94 -1.00 -1.00 -1.00 -1.00 

66 0.67 -1.00 -1.00 -1.00 -1.00 

67 0.03 -1.00 -1.00 -1.00 -1.00 

68 2.69 -1.00 -1.00 -1.00 -1.00 

69 4.49 -1.00 -1.00 -1.00 -1.00 

70 1.20 -1.00 -1.00 -1.00 -1.00 

71 0.11 -1.00 -1.00 -1.00 -1.00 

72 0.59 -1.00 -1.00 -1.00 -1.00 

73 1.82 -1.00 -1.00 -1.00 -1.00 

74 0.00 -1.00 -1.00 -1.00 -1.00 

75 0.35 -1.00 -1.00 -1.00 -1.00 

76 1.15 -1.00 -1.00 -1.00 -1.00 

77 -1.00 -1.00 -1.00 -1.00 -1.00 

78 -1.00 -1.00 -1.00 -1.00 -1.00 

79 -1.00 -1.00 -1.00 -1.00 -1.00 

80 0.00 -1.00 -1.00 -1.00 -1.00 

81 0.52 -1.00 -1.00 -1.00 -1.00 

82 0.00 -1.00 -1.00 -1.00 -1.00 

83 2.76 -1.00 -1.00 -1.00 -1.00 

84 1.45 -1.00 -1.00 -1.00 -1.00 

85 4.90 -1.00 -1.00 -1.00 -1.00 

86 1.17 -1.00 -1.00 -1.00 -1.00 

87 0.79 -1.00 -1.00 -1.00 -1.00 

88 0.20 -1.00 -1.00 -1.00 -1.00 

89 0.68 -1.00 -1.00 -1.00 -1.00 

90 0.85 -1.00 -1.00 -1.00 -1.00 

91 0.56 -1.00 -1.00 -1.00 -1.00 

92 0.65 -1.00 -1.00 -1.00 -1.00 

93 0.00 -1.00 -1.00 -1.00 -1.00 

94 0.00 -1.00 -1.00 -1.00 -1.00 


A- 54


95 0.20 -1.00 -1.00 -1.00 -1.00 

96 0.00 -1.00 -1.00 -1.00 -1.00 

97 0.04 -1.00 -1.00 -1.00 -1.00 

98 0.30 -1.00 -1.00 -1.00 -1.00 

99 0.23 -1.00 -1.00 -1.00 -1.00 

100 0.14 -1.00 -1.00 -1.00 -1.00 

101 0.59 -1.00 -1.00 -1.00 -1.00 

102 0.77 -1.00 -1.00 -1.00 -1.00 

103 0.74 -1.00 -1.00 -1.00 -1.00 

104 0.21 -1.00 -1.00 -1.00 -1.00 

105 -1.00 -1.00 -1.00 -1.00 -1.00 

106 -1.00 -1.00 -1.00 -1.00 -1.00 

107 36.20 -1.00 -1.00 -1.00 -1.00 

108 0.01 -1.00 -1.00 -1.00 -1.00 

109 0.00 -1.00 -1.00 -1.00 -1.00 

110 0.03 -1.00 -1.00 -1.00 -1.00 

111 0.13 -1.00 -1.00 -1.00 -1.00 

112 0.00 -1.00 -1.00 -1.00 -1.00 

113 0.08 -1.00 -1.00 -1.00 -1.00 

114 0.00 -1.00 -1.00 -1.00 -1.00 

115 0.26 -1.00 -1.00 -1.00 -1.00 

0 -1.00 -1.00 -1.00 -1.00 -1.00 

Table 2. “Success” of drivers by municipality 

Total Region Hopelchen Champoton Calakmul Escarcega 

DRIVERS Map Name #Classes %Correct Kappa %Correct Kappa %Correct Kappa %Correct Kappa %Correct Kappa 

NORTH 

1 Ag70n_a.rst 14 92.9321 0.1314 88.3934 0.0957 89.1697 0.1588 98.1093 0.0017 98.6332 0.0069 

2 Allwtn_a.rst 21 92.1627 0.0368 88.0718 0.0707 87.5456 0.0327 98.1104 0.0023 98.6578 0.0248 

3 Demn_a.rst 17 93.2479 0.1702 87.7421 0.0450 90.0365 0.2261 98.1153 0.0049 98.6223 -0.0010 

4 Ejidno_a.rst 107 92.2363 0.0459 87.3146 0.0117 87.8245 0.0543 98.2003 0.0498 98.6359 0.0089 

5 Hststn_a.rst 19 93.7404 0.2307 88.3283 0.0907 90.9781 0.2993 98.1115 0.0029 98.6469 0.0168 

6 Rdsn_a.rst 18 92.2503 0.0476 87.5072 0.0267 87.8918 0.0596 98.1067 0.0004 98.6851 0.0446 

7 Sect1n_a.rst 10 92.4209 0.0685 87.5555 0.0305 88.2604 0.0882 98.1046 -0.0007 98.6359 0.0089 

8 Strmpn_a.rst 28 92.8640 0.1230 89.6282 0.1919 88.6867 0.1213 98.1170 0.0058 98.6141 -0.0070 

9 Townsn_a.rst 19 93.2351 0.1686 89.2283 0.1608 89.6001 0.1922 98.1325 0.0140 98.6578 0.0248 

10 Watpn_a.rst 25 92.0514 0.0231 87.2045 0.0031 87.5455 0.0327 98.0910 -0.0079 98.6441 0.0148 


A- 55


11 Wetlpn_a.rst 2 91.8928 0.0036 87.1700 0.0004 87.1884 0.0049 98.1064 0.0002 98.6359 0.0089 

12 Wetlsn_a.rst 2 92.0056 0.0175 88.3638 0.0934 87.1248 0.0000 98.1059 0.0000 98.6359 0.0089 

13 Wetltn_a.rst 2 92.0498 0.0229 88.3638 0.0934 87.2242 0.0077 98.1046 -0.0007 98.6359 0.0089 

Combinations 

5, 3 19 93.9568 0.2573 87.6867 0.0407 91.5956 0.3472 98.1452 0.0207 98.6551 0.0228 

5, 3, 9 19 93.8638 0.2459 89.3918 0.1735 90.9194 0.2947 98.1667 0.0321 98.6933 0.0506 

5, 3, 9, 1 19 93.9740 0.2594 89.0746 0.1488 91.2494 0.3203 98.1646 0.0309 98.6933 0.0506 

5, 3, 9, 1, 8 28 94.1046 0.2755 89.3016 0.1665 91.4386 0.3350 98.2088 0.0543 98.6605 0.0268 

5, 3, 9, 1, 8, 7 28 94.0547 0.2693 89.3147 0.1675 91.3130 0.3253 98.2209 0.0607 98.6605 0.0268 

5, 3, 9, 1, 8, 7, 6, 4 107 94.0738 0.2717 89.5191 0.1834 91.3136 0.3253 98.2080 0.0539 98.6633 0.0288 

Total Region 0.0923 

0.0615 

0.0242 

0.0177 

0.0228 

0.0212 

0.0140 

0.0098 

0.0022 

-0.0004 

-0.0046 

0.0920 

0.0857 

0.0841 

0.0936 

0.0933 

Champoton Calakmul 

SOUTH Map Name #Classes %Correct Kappa %Correct Kappa %Correct Kappa 

1 Ejidso_a.rst 115 97.7901 0.0927 95.0882 0.1043 97.8321 2 Strmps_a.rst 13 97.7195 0.0637 95.2055 0.1257 97.7585 3 Rdss_a.rst 25 97.6239 0.0244 94.6816 0.0301 97.6695 4 Hststs_a.rst 22 97.6201 0.0229 95.4385 0.1682 97.6539 5 Townss_a.rst 16 97.6187 0.0223 94.5571 0.0074 97.6662 6 Sect1s_a.rst 10 97.6184 0.0222 94.7916 0.0502 97.6622 7 Demn_a.rst 19 97.6036 0.0161 94.9291 0.0753 97.6451 8 Ag70s_a.rst 25 97.5940 0.0121 94.9450 0.0782 97.6351 9 Allwts_a.rst 8 97.5800 0.0064 95.2055 0.1257 97.6168 10 Watps_a.rst 19 97.5729 0.0035 95.1331 0.1125 97.6108 11 Wetlss_a.rst 2 97.5548 -0.0039 94.6049 0.0161 97.6006 Combinations 

1, 3, 5, 8, 9 5 drivers of north 97.6988 0.0552 

4, 8, 6, 9 4 drivers 97.7960 0.0951 

4, 8, 6, 5 5 drivers 97.8041 0.0984 96.0477 0.2793 97.8313 4, 8, 6 3 drivers 97.7778 0.0876 95.2952 0.1420 97.8163 4, 8 2 drivers 97.7743 0.0862 95.3068 0.1441 97.8126 4, 8, 6, 5, 9 5 drivers of south 97.8082 0.1001 96.0651 0.2824 97.8353 4, 8, 6, 5, 9, 7 6 drivers 97.8074 0.0998 96.0651 0.2824 97.8344 Winrock International 

A- 56


Figure 1. Individual Deforestation Driver Risk Maps 1-11 


A- 57



A- 58


Meseta Purepecha Region 

_____________________________________________________________________________ 

________________________ 

Table 1. Individual Driver Category Weights (% of class deforested at time 

of calibration) for each sub-region of Meseta Purepecha 

________________________________________________________________________ 

URUAPAN 

CATEGORY Dist_aga Droad_a Aspect Slope Soils dpob2 elev_a ppmo tmaxmo 

1 59.46 66.38 90.32 77.43 14.79 73.88 98.7 0 -1 

2 45.29 55.16 39.92 58.39 45 63.93 92.55 48.09 -1 

3 38.03 45.44 37.38 33.08 70.17 48.45 50.28 74.04 -1 

4 33.58 38.45 40.14 17.35 39.17 39.34 31.81 34.72 0.64 

5 35.07 31.37 42.74 10.1 -1 36.74 37.71 37.44 18.22 

6 30.82 27.44 38.31 8.41 27.9 30.71 40.55 48.41 45.51 

7 32.54 23.33 41.88 7.28 42.07 29.24 47.4 0 47.48 

8 36.08 20.38 40.89 5.13 -1 28.5 45.09 -1 25.66 

9 34.57 18.09 44.56 0 -1 29.44 46.18 -1 83.13 

10 35.22 17.79 44.08 0 35.78 23.79 22.81 -1 100 

11 44.13 18.26 -1 -1 77.52 13.12 19.87 -1 -1 

12 43.53 19.82 -1 -1 93.97 12.56 4.87 -1 -1 

13 44.37 21.27 -1 -1 -1 9.31 0 -1 -1 

14 42.69 20.87 -1 -1 -1 12.81 0 -1 -1 

15 38.08 20.98 -1 -1 -1 5.14 -1 -1 -1 

16 36.56 21.05 -1 -1 -1 0 -1 -1 -1 

17 38.32 21.63 -1 -1 -1 0 -1 -1 -1 

18 35.63 22.07 -1 -1 -1 -1 -1 -1 -1 

19 34.38 23.68 -1 -1 -1 -1 -1 -1 -1 

20 30.88 21.84 -1 -1 -1 -1 -1 -1 -1 

21 26.43 20.37 -1 -1 -1 -1 -1 -1 -1 

22 20.83 20.97 -1 -1 -1 -1 -1 -1 -1 

23 24.57 25.21 -1 -1 -1 -1 -1 -1 -1 

24 28.91 27.11 -1 -1 -1 -1 -1 -1 -1 

25 24.94 18.3 -1 -1 -1 -1 -1 -1 -1 

26 8.11 2.06 -1 -1 -1 -1 -1 -1 -1 

27 -1 0 -1 -1 -1 -1 -1 -1 -1 

28 -1 0 -1 -1 -1 -1 -1 -1 -1 

29 -1 0 -1 -1 -1 -1 -1 -1 -1 

30 -1 -1 -1 -1 -1 -1 -1 -1 -1 

31 -1 -1 -1 -1 -1 -1 -1 -1 -1 

32 -1 -1 -1 -1 -1 -1 -1 -1 -1 

0 -1 -1 66.67 66.67 -1 -1 -1 -1 -1 

TANCITARO 


1 75.1 77.84 93.99 88.83 53.17 82.03 86.36 100 -1 

2 67.76 72.03 71.65 78.99 60.24 73.69 97.03 95.67 -1 

3 65.3 64.99 56.3 58.95 84.57 64.31 80.07 80.86 -1 


A- 59


4 62.49 59.09 46.94 38.56 97.06 57.83 79.23 58.04 13 

5 60.72 54.15 50.13 31.78 -1 53.13 58.57 61.8 32.38 

6 57.76 48.6 60.08 29.52 54.98 44.09 56.67 52.22 63.16 

7 57.09 42.52 64.03 25.88 58.02 38.98 66.59 2.67 57.83 

8 50.61 37.27 69.26 21.94 -1 35.95 61.81 -1 69.56 

9 38.56 33.06 74.17 32.26 -1 26.96 45.42 -1 95.71 

10 35.57 27.29 73.27 100 52.28 24.88 33.01 -1 -1 

11 46.37 24.47 -1 -1 96.64 38.19 26.19 -1 -1 

12 55.79 21.64 -1 -1 -1 31.79 15.91 -1 -1 

13 63.88 22.9 -1 -1 -1 40.09 0 -1 -1 

14 67.82 27.18 -1 -1 -1 61.67 -1 -1 -1 

15 72.73 30.87 -1 -1 -1 71.52 -1 -1 -1 

16 71.77 38.82 -1 -1 -1 78.48 -1 -1 -1 

17 89.56 50.24 -1 -1 -1 -1 -1 -1 -1 

18 98.53 72.54 -1 -1 -1 -1 -1 -1 -1 

19 100 79.07 -1 -1 -1 -1 -1 -1 -1 

20 -1 83.96 -1 -1 -1 -1 -1 -1 -1 

21 -1 80 -1 -1 -1 -1 -1 -1 -1 

22 -1 81.61 -1 -1 -1 -1 -1 -1 -1 

23 -1 80.21 -1 -1 -1 -1 -1 -1 -1 

24 -1 85.06 -1 -1 -1 -1 -1 -1 -1 

25 -1 84.54 -1 -1 -1 -1 -1 -1 -1 

26 -1 83.75 -1 -1 -1 -1 -1 -1 -1 

27 -1 73.47 -1 -1 -1 -1 -1 -1 -1 

28 -1 64.52 -1 -1 -1 -1 -1 -1 -1 

29 -1 72.73 -1 -1 -1 -1 -1 -1 -1 

30 -1 62.5 -1 -1 -1 -1 -1 -1 -1 

31 -1 -1 -1 -1 -1 -1 -1 -1 -1 

32 -1 -1 -1 -1 -1 -1 -1 -1 -1 

0 -1 -1 84.21 84.21 -1 -1 -1 -1 -1 

PATZCUARO 


1 87.35 75.4 93.36 88.34 49.17 86.16 32.93 -1 -1 

2 75.87 68.3 54.94 72.82 58.33 78.95 -1 27.37 -1 

3 64.44 61.29 51.89 40.1 42.45 68.05 -1 55.3 -1 

4 52.69 54.22 48.28 15.32 73.46 55.62 93.27 55.77 11.13 

5 44.26 47.19 51.73 8.68 97.99 41.69 50.17 44.37 41.46 

6 41.96 42.22 51.83 3.94 18.18 31.88 60.31 -1 51.6 

7 40 36.24 52.99 0.39 57.24 29.5 61.91 -1 74.46 

8 35.33 31.45 54.32 0 100 26.25 72.47 -1 75.12 

9 36.51 26.34 54.12 0 66.23 23.91 48.4 -1 -1 

10 33.08 21.74 52.35 0 -1 21.81 40.93 -1 -1 

11 32.48 17.22 -1 -1 63.17 26.69 41.05 -1 -1 

12 32.16 12.69 -1 -1 93.25 30.4 13.53 -1 -1 

13 36.2 8.43 -1 -1 -1 28.7 0 -1 -1 

14 51.36 6.69 -1 -1 -1 15.8 0 -1 -1 

15 54.26 6.4 -1 -1 -1 13.69 -1 -1 -1 


A- 60


16 50.23 6.27 -1 -1 -1 19.28 -1 -1 -1 

17 51.26 7.15 -1 -1 -1 9.41 -1 -1 -1 

18 53.6 9.58 -1 -1 -1 0 -1 -1 -1 

19 35.97 15.82 -1 -1 -1 0 -1 -1 -1 

20 24.79 18.18 -1 -1 -1 -1 -1 -1 -1 

21 20.51 19.36 -1 -1 -1 -1 -1 -1 -1 

22 9.07 17.93 -1 -1 -1 -1 -1 -1 -1 

23 5.99 18.13 -1 -1 -1 -1 -1 -1 -1 

24 4.33 19.49 -1 -1 -1 -1 -1 -1 -1 

25 29.09 10.13 -1 -1 -1 -1 -1 -1 -1 

26 94.44 0 -1 -1 -1 -1 -1 -1 -1 

27 100 0 -1 -1 -1 -1 -1 -1 -1 

28 -1 0 -1 -1 -1 -1 -1 -1 -1 

29 -1 0 -1 -1 -1 -1 -1 -1 -1 

30 -1 0 -1 -1 -1 -1 -1 -1 -1 

31 -1 0 -1 -1 -1 -1 -1 -1 -1 

32 -1 0 -1 -1 -1 -1 -1 -1 -1 

0 -1 -1 32.93 32.93 -1 -1 -1 -1 -1 

MESETA 


1 85.11 81.18 95.62 90.46 27.63 90.89 66.27 67.83 -1 

2 66.79 71.08 47.37 74.97 44.88 81.63 98.46 73.62 -1 

3 52.79 61.64 53.98 41.92 86.42 71.6 96.45 60.62 0 

4 49.5 53.97 55.08 18.23 72.22 64.11 93.13 34.77 13.05 

5 48.93 48.24 53.43 13.32 -1 54.9 46.91 56.51 34.22 

6 51.29 42.39 51.65 7.1 28.9 49.97 79.43 41.18 53.59 

7 54.66 36.63 53.19 4.13 52.43 45.67 69.9 6.87 59.23 

8 58.65 31.46 50.86 2.29 -1 41.24 62.17 -1 90.5 

9 57.36 28.25 47.23 0 -1 34.97 52.34 -1 93.16 

10 55.09 25.47 47.5 0 17.89 29.86 32.69 -1 -1 

11 53.6 22.09 -1 -1 97.64 29.2 34.98 -1 -1 

12 50.82 18.75 -1 -1 100 29.53 20.56 -1 -1 

13 50.17 15.19 -1 -1 -1 29.91 0.93 -1 -1 

14 53.06 12.56 -1 -1 -1 31.85 0 -1 -1 

15 52.47 11.52 -1 -1 -1 43.92 0 -1 -1 

16 49.44 10.14 -1 -1 -1 37.1 -1 -1 -1 

17 45.12 8.06 -1 -1 -1 20.88 -1 -1 -1 

18 40.7 6.74 -1 -1 -1 22.62 -1 -1 -1 

19 28.28 5.04 -1 -1 -1 21.01 -1 -1 -1 

20 24.82 4.13 -1 -1 -1 6.08 -1 -1 -1 

21 27.65 4.62 -1 -1 -1 0 -1 -1 -1 

22 27.71 6.32 -1 -1 -1 0 -1 -1 -1 

23 24.57 4.59 -1 -1 -1 -1 -1 -1 -1 

24 21.86 0 -1 -1 -1 -1 -1 -1 -1 

25 24.83 0 -1 -1 -1 -1 -1 -1 -1 

26 26.54 0 -1 -1 -1 -1 -1 -1 -1 

27 43.19 0 -1 -1 -1 -1 -1 -1 -1 

28 53.08 0 -1 -1 -1 -1 -1 -1 -1 


A- 61


29 49.7 0 -1 -1 -1 -1 -1 -1 -1 

30 34.66 0 -1 -1 -1 -1 -1 -1 -1 

31 0.57 0 -1 -1 -1 -1 -1 -1 -1 

32 0 0 -1 -1 -1 -1 -1 -1 -1 

0 -1 -1 66.27 66.27 -1 -1 -1 -1 -1 


A- 62


Table 2. “success” of drivers by region. 


Rank Driver % Correct Kappa 

REGION URUAPAN TANCITA PATZCUAR MESETA 

% 

% 

% 

Correct Kappa Correct Kappa Correct Kappa % Correct Kappa RANK 

1 All drivers 94.9237 0.1505 93.6216 0.0979 98.1396 0.1525 93.3538 0.2883 95.2261 0.0310 1 

2 tmaxmo_a 94.6354 0.1023 93.5398 0.0863 97.9381 0.0607 92.7581 0.2245 94.9551 -0.024 2 

3 dpob2_a 94.5743 0.0921 93.0989 0.024 97.8402 0.0161 92.43 0.1894 95.3433 0.0548 3 

4 elev_a 94.3865 0.0606 93.4233 0.0698 98.004 0.0907 91.8228 0.1244 94.9073 -0.0337 4 

5 Dist_aga 94.3736 0.0585 93.1655 0.0334 97.8007 -0.0019 92.1221 0.1564 94.9514 -0.0248 5 

6 Soils_a 94.3706 0.0580 93.3249 0.0559 97.8214 0.0075 91.6041 0.1010 95.1623 0.0181 6 

7 Droad_a 94.3513 0.0548 93.0157 0.0122 97.8440 0.0178 91.6228 0.1030 95.2936 0.0447 7 

8 Slope_a 94.2697 0.0411 92.9589 0.0041 97.8289 0.0109 91.4933 0.0891 95.1933 0.0243 8 

9 ppmo_a 

93.9661 -0.0097 

92.5749 

10 Aspect_a 93.9449 -0.0133 92.7135 

- 

0.0502 98.0417 0.1079 90.7206 0.0064 94.9926 -0.0164 9 

- 

0.0306 97.7969 -0.0036 90.5451 -0.0124 95.0751 0.0004 10 

Combinations Neighborhood=0 


Neighborhood=0 % Correct Kappa 

% 

Correct Kappa 

% 

Correct Kappa 

% 

Correct Kappa % Correct Kappa 

2,3,4,5,6 0.0122 

0.0247 

0.0386 

0.0472 

-0.0061 

-0.0177 

94.7996 0.1298 93.5453 0.0871 97.9683 0.0744 93.1682 0.2684 95.1333 2,3 94.7522 0.1218 93.5079 0.0818 97.9777 0.0787 92.8991 0.2396 95.1952 4,6,7 94.7243 0.1172 93.4746 0.0771 97.876 0.0324 92.7854 0.2275 95.2636 3,7,4 94.7047 0.1139 93.207 0.0392 97.9306 0.0573 92.8717 0.2367 95.3058 2,3,4 94.7004 0.1132 93.4566 0.0745 98.1208 0.1439 92.8516 0.2345 95.0433 2,3,4,5 94.6775 0.1093 93.5134 0.0826 98.0869 0.1285 92.807 0.2298 94.9861 Neighborhood=1 



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Rank Driver % Correct Kappa 

% 

Correct Kappa 

% 

Correct Kappa 

% 

Correct Kappa % Correct Kappa 

1 Slope_a 0.2109 

0.1943 

0.208 

0.204 

0.1722 

0.1488 

0.1859 

0.1429 

0.2046 

0.2213 

0.193 

0.2031 

0.2067 

0.2067 

0.1778 

0.2044 

0.2027 

95.6059 0.2647 94.646 0.2428 98.1603 0.1619 93.8732 0.3439 96.1122 2 Dist_aga 95.5465 0.2548 94.6017 0.2365 98.0944 0.1319 93.8372 0.3401 96.0307 3 Droad_a 95.5329 0.2525 94.4603 0.2165 98.1434 0.1542 93.784 0.3344 96.0982 4 Soils_a 95.5286 0.2518 94.6599 0.2447 98.0925 0.131 93.6272 0.3176 96.0785 5 9 drivers 95.5239 0.251 94.4783 0.219 98.23 0.1936 93.9308 0.3501 95.9219 6 elev_a 95.4981 0.2466 94.5532 0.2296 98.2243 0.1911 93.9221 0.3492 95.8065 7 dpob2_a 95.4884 0.245 94.4284 0.212 98.1076 0.1379 93.8185 0.3381 95.9894 8 tmaxmo_a 95.4781 0.2433 94.5448 0.2285 98.2262 0.1919 93.8876 0.3455 95.7775 9 Disd76a** 95.4742 0.2426 94.452 0.2153 98.0812 0.1259 93.6113 0.3159 96.0813 10 Aspect_a 95.4682 0.2416 94.4228 0.2112 98.0455 0.1096 93.5164 0.3057 96.1638 11 ppmo_a 95.4622 0.2406 94.1678 0.1751 98.213 0.1859 93.8416 0.3406 96.0241 Combinations Neighborhood=1 

1,2 95.6265 0.2681 94.7209 0.2534 98.1961 0.1782 93.9164 0.3486 96.0738 1,2,3,4 95.5866 0.2615 94.5629 0.231 98.1697 0.1662 93.9005 0.3469 96.0916 1,2,3 95.5784 0.2601 94.5476 0.2288 98.1566 0.1602 93.8905 0.3458 96.0916 1,2,3,4,6 95.5638 0.2576 94.5629 0.231 98.2526 0.2039 93.9567 0.3529 95.9491 1,3,7,9 95.5412 0.2539 94.409 0.2092 98.1886 0.1748 93.866 0.3432 96.0804 2,7,3 95.5319 0.2523 94.4464 0.2145 98.1302 0.1482 93.8444 0.3409 96.0719 3,7,6 95.4848 0.2444 94.4062 0.2088 98.1641 0.1636 93.8689 0.3435 95.9331 0.1745 


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Figure 1 A-Individual driver risk maps. 


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Modeling deforestation baselines using GEOMOD - Instituto ...

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