GIS Methodologies for Developing Conservation Strategies Part 6 potx

Using a GIS to Determine Critical Areas 113 mentioned earlier. The relative weights P R k (i,j) are also normalized to 1, with the result that, on average (weighted by the areas covered by each threat), P R k (i,j) is 1. Hence, multiplying P R k (i,j) by P A k will produce the threat A k (i,j) for each cell, that will average over the entire area to a value equal to P A k . The methodology for the normalization is described below. Normali zing the Paramet ers Given initial values for weights P(i,j) for a given threat k, the normalized values P R (i,j), or relative weight, is given by: P R (i,j)ס P(i,j) <P> Equation (3) where <P> is the weighted average of the P(i,j) within the map. We will now explain how to compute <P> for different spatial distributions. Factors Covering a Well-Defined Area This is the simplest case that applies to slope classes and aquifers. Once a weight P l is assigned to each class l within a threat k, a scale factor <P> is calculated from the area occupied by each class l relative to the total area of interest. Given the set of class weight P l , each having an area S l , we obtain: S T ס ∑ l S l Equation (4) and <P>ס ∑ l P l (i,j) S l S T Equation (5) where P 1 (i,j) is the weight of each class l at cell (i,j). Within IDRISI, the areas S l are computed with area. Factors That Vary Continuously in Space This is the case for population density and threat from distance to roads. The problem can be illustrated by a simple example. Let us consider an infestation by insects at a given point of the ACCVC. At a given time, the density of insects will decrease with the distance from the infestation nucleus. Insects could be found at any place in the area but are scarce far from the nucleus. If we want to use equation 5, we have to determine the area of influence, S T , of the insect infestation. If we consider all the ACCVC (the area of interest) as the area affected by insects, the scaling factor <P> as in equation 5 will be very small since the insects are concentrated near the infestation nucleus (i.e., S l is small). On the other hand, if we set a threshold 114 Leclerc and Rodriguez to define S T , saying that if the density of insects is lower than x percent it is considered negligible, then the value that S T will take will depend on the choice of x. Therefore, there is no well-defined area to weight a distribution that varies slowly in space, and the task of normalizing this distribution is by no means simple. There is, however, a way to use equation 3 with such distribution. For a continuous distribution, equation 5 is represented by a volume integral: <P>ס ͐ AOI P(x,y) dS S T Equation (6) where AOI denotes the area of interest of surface S T . Such an integral can be evaluated numerically if the distribution is discretized. In GIS, discretization is done by reclassifying the continuous map in a large number of discrete classes. This is already done if the map is represented by integer numbers. For these new classes, one can calculate the average value of the threat (obtaining the P l ), and their areas (S l ), and compute <P> with equation 5. With IDRISI this is done with the modules reclass (to obtain the discrete map from the continuous map), extract (to extract the average value of the continuous map within each discrete class), and area (to obtain the area of each discrete class). Then one can compute <P> with equation 5. Details of the Threats Roads and Trails In Latin America, building a new public road results in the colonization of the area within a short time along the road. The natural tendency is to develop agricultural and cattle production and wood extraction simply because it is easier to bring the products to market. It also results in an increase in the standard of living in the region and increased demand for products. We digitized roads from 1:50,000 scale maps and made the assumption that the pressure of the human activity along them is decreasing exponentially with the distance to the road. All types of roads (from highway to gravel) have been given the same weight. The choice of the exponential comes from the hypothesis that the probability of penetrating a given distance in the forest (and therefore to deforest at that point) is a constant. Consequently, the probability P(d ם ⌬d) of going from a point at d to a point at (d ם ⌬d) within the forest is proportional to the probability P(d) of being at d (i.e., having logged up to that point). P(dם⌬d)סP(d) ( 1מ ⌬d d 0 ) Equation (7) where d 0 is a constant called the characteristic distance. Therefore: P(d)סexp מ d d 0 Equation (8) Using a GIS to Determine Critical Areas 115 Hence the probability of entering the forest up to a distance d 0 is P(d 0 ) ס 1/e ס 0.37. This function (figure 9.3) applied to roads is shown in the map in figure 9.4. We estimated that a characterestic distance of 1 km was a reasonable figure for the area. Forest Management Plans The forest management plans in the ACCVC ap- proved by the DGF after 1989 have been mapped at a 1:50,000 scale, which provides an overview of where the logging activity was concentrated between 1986 and 1992. Some of the plans have already been executed within the Braulio Carrillo protected area, as a consequence of the private ownership of these lands. In these areas the loggers (principally locals) have contracts with the owners of the land covering a period of from one to five years. The management plans have to be approved by DGF. This is a long and tedious process which involves several revisions, but approval is eventually conceded. To simulate the pressure that the logging activity can bring to an area, we selected a buffer zone of 1 km around the actual management plans and gave it a weight of 1. The management plans supervised or approved by FUNDECOR have been given a weight of 0, which implies that these plans do not present any F IG. 9.3 Graph of the threat due to the proximity to a road in function of the dis- tance of the latter, following equation 8. The arrow shows the value of the function for the characteristic distance d 0 (1000 m). 116 Leclerc and Rodriguez risk to the environment (figure 9.5). Adjacent to these areas, however, FUNDE- COR has a responsibility because the logging activities, although well managed, imply road construction which opens the way to uncontrolled logging in the buffer zone. Population Density The forest and other natural resources that are close to population centers are under constant pressure. More families will need more land for construction and agriculture, and will represent a larger threat to the forest. In addition, water pollution is likely to increase closer to the villages. Locating population centers and estimating the population density will help focus efforts where human activity is greatest. Since 1945 Costa Rica has put tremendous effort and money toward improv- ing education, and as a consequence it has a lower rate of illiteracy than the United States. As soon as there are more than six children in an area, a school is built and a teacher assigned. Numbers of schools allowed us to generate a better estimate of population density than from the villages that appear on the outdated maps. The schools, digitized as points from the 1:50,000 scale maps of the Ministry of Education, were assigned a weight equal to the number of registered students. Then, by applying successive passes of a 3 ן 3 average filter, we generated a Gaussian distribution centered on the school (which represents student density) and converted this to population density by knowing the num- ber of family members and students per family. Here, however, we are only interested in the normalized distribution. F IG. 9.4 Map of the normalized threat due to proximity to roads (1000 m characteris- tic distance, exponential model). Using a GIS to Determine Critical Areas 117 In the remote areas of the ACCVC the children will walk a maximum of 2.5 km to go to school (those that have to walk a longer distance will eventually have a school built closer to their homes). Knowing this fact, we can estimate the extent of the population distribution. The average filter that IDRISI provides (filter) computes, for each cell (i,j), the average of the values of its nearest neighbors. Given E(i,j), the registration for a school located at cell (i,j), the value after 1 pass of the filter will be E(i,j)ס 1 9 ∑ kסiםl kסiמl ∑ lסjםl lסjמl E(k,l) Equation (9) Each time the filtering operation is done on the map resulting from the preceding filter, the population distribution becomes more Gaussian, is flattened, and extends radially. On a raster map with a square pixel of 100 m, one hundred successive passes of the average filter produces a distribution with a width at half height R E/E0ס50% of about 1,000 m and a maximum radius of approximately 2.5 km. This distribution represents the density of students per hectare (since the pixel is one hectare). The normalized map resulting from this operation is shown in figure 9.6. Table 9.2 shows how the distribution changes with the number of consecutive passes of the average filter. In this table, E/E 0 represents the height of the center of the distribution (for example, the height of the distribution after one hundred passes is 0.0024 of the original height E 0 , which is the number of F IG. 9.5 Map of the normalized threat due to forest management plans (1 km buffer zone) 118 Leclerc and Rodriguez registered students). R max is the value where the distribution is close to zero, although the absolute zero is reached at a distance equal to the number of passes times the size of the pixel (i.e., for one hundred passes, 100 ן 100 m ס 10 km; see figure 9.7). IDA Land Distribution As an important criterion to determine a potential threat to natural resources, FUNDECOR considered the proximity occasioned by the IDA’s land repatriation plan. In fact, IDA has given extensive areas of forest free to families in order to promote agriculture and development. A way to add F IG. 9.6 Map of the normalized population density (one hundred passes ofa3x3 average filter). The map was produced starting from a point coverage of the schools, where the value of the school is equal to the number of registered students. T ABLE 9.2 Distribution Changes Produced by Passes of a 3ן3 Average Filter Number of Passes (f) R E/E0ϭ50% R max E/E 0 10 times 400 m 1,000 m 0.0218 50 times 750 m 2,000 m 0.0047 100 times 1,000 m 2,500 m 0.0024 150 times 1,300 m 3,500 m 0.0018 note: Effect of consecutive passes of the filter in the IDRISI program (filter)on the width of the distribution at 50 percent of maximum height, its maximum width (1 percent of maximum height), and the height of the distribution. Using a GIS to Determine Critical Areas 119 value to the land, however, is to cut down the forest. To simulate the fact that new IDA colonies can be located in the neighborhood of existing colonies, we considered a simple 1 km buffer. The IDA colonies and the buffer zone have been given a weight of 1 (figure 9.8). Slopes The criterion of reduced slopes as a threat to the natural resources is based on the fact that steep slopes are a natural barrier for logging and for expansion of the population. Flat areas, to the contrary, are prone to be invaded rapidly. The threat for slope classes has been computed using the inverse of the average slope in a given slope class, and has been normalized using the area of each class (equation 5). The calculated weights for slopes are 5–15 percent slope ס 3.838; 15–30 percent slope ס 1.706; 30–45 percent slope ס 1.023; 45–60 percent slope ס 0.731; 60–75 percent slope ס 0.569; 75 percent slope ס 0.465. Note that there is no area of less than 5 percent slope in the ACCVC. The resulting map is shown in figure 9.9. Conflicts Between Threats The parameters used by the model are globally as independent as possible, but there may be areas where two parameters are redundant. For example, where population is concentrated there is sometimes a F IG. 9.7 Graph depicting the effect of the number of passes of an average filter on a point distribution having an initial value of 1. The resulting distribution is very close to Gaussian and hence becomes smaller and broader with increased filtering. 120 Leclerc and Rodriguez F IG. 9.8 Map of the normalized threat due to IDA colonies F IG. 9.9 Map of the normalized threat due to reduced terrain slopes Using a GIS to Determine Critical Areas 121 greater density of roads and the region is usually flatter, and the simple sum of the weights resulting from these parameters will overestimate the threat. To overcome this situation, we combine population and roads into a single threat: P(population ם roads) Equation (10) ס MAX[P(population,P(roads)] This operation is done after the normalization of the respective parameters. It is this combined threat that is used as a specific threat P R k in equation 2. Results and Discussion Map of Critical Areas The critical areas map is the result of combining the distinct layers representing different threats, as in equation 1 (for the total threat maps, see figures 9.10 and 9.11), and of overlaying the total threat map with the map showing the natural resources of interest (normalized after prioritization). Figure 9.12 shows the F IG. 9.10 Map of the total threat to forest which results in combining the preceding normalized threat maps with the weights appearing in the text (under the section “Prioritizing the Threats”), according to equation 1. Darker areas have already been deforested or are more prone to deforestation. 122 Leclerc and Rodriguez F IG. 9.11 Map of the total threat to aquifers which results from combining the pre- ceding normalized threat maps with weights appearing in the text (under the section “Prioritizing the Threats”), according to equation 1. Darker areas are more prone to contribute to water contamination. F IG. 9.12 Map of critical areas (1986 forest cover mask of the total threat map of fig- ure 9.10) [...]... area deforested was determined for every level of threat (figure 9.14) For low levels of threats, deforestation was erratic, probably due to errors in F IG 9.13 Forest cover changes for the period 19 86 1992, based on digital classification of Landsat TM images and on the interpretation of aerial photos Due to persistent cloud coverage in 1992, three 1992 scenes of different dates were combined with information... in Costa Rica) (1993), the following wildlife zones are identified for this area of study: tropical dry forest, wet premontane forest, and tropical wet forest The average annual rainfall is 1 ,62 4 mm Materials and Methods Fieldwork Location and Installment of Plots For the vegetation sampling, secondary and tertiary paths commonly used for horses were followed These paths are well distributed throughout... potential habitat for the white-tailed deer (Odocoileus virginianus) in the study area (1994) TABLE 10.1 Area for Each Category of Habitat Quality Potential for Odocoileus virginianus Present on the Study Area (Bagaces 1994) Habitat Quality Area (ha) Percent High Medium (food) Medium (cover) Medium (water) Low (cover, food) Low (cover, water) Inappropriate 21,512.25 854.50 9, 363 .00 868 .00 11 ,63 3 .62 38.25 15,810.44... 1.4 15 .6 1.4 19.4 0.1 26. 3 Total 60 ,080. 06 100.0 with the other half in private farms and government farms of Costa Rica’s Instituto de Desarrollo Agrario (IDA, or Institute of Agrarian Reform) Approximately 25 percent (15,810.4 ha) of the total area does not provide minimal conditions for deer (inappropriate habitat) The rest of the 21,891.7 ha have limits in some of the habitat requirements for the... analysis can help to identify the factors that do not contribute to deforestation For example, for high threat levels, the average deforestation rate seems to become constant or decrease This phenomenon could be explained in some areas by the proximity of the forested land to national parks, where the owners prefer to maintain the forest cover in the hope that the government will purchase the land to... respect to their average priority levels, and preference for support is given to high-priority areas even if their commercial value is not as high 1 26 Leclerc and Rodriguez F IG 9. 16 Map of the critical areas for forest, reclassified in three risk levels Low, medium, and high risks correspond to approximately 0.04 percent, 0.07 percent, and 0.11 percent deforestation rates Acknowledgments Thanks are due to... threat (0.4–0 .6) 4—Great threat (0 .6 0.8) 5—Extreme threat (Ͼ0.8) Land Use Change and Model Validation We used Landsat TM satellite imagery and aerial photographs for cloudy areas to estimate land use changes in the ACCVC for the period 19 86 1992 (figure 9.13) To validate the model, the critical areas map (real values) from figure 9.12 was reclassified in twenty levels in intervals of 0.05 For different...Using a GIS to Determine Critical Areas 123 critical areas map for forest, in this case a simple mask of the total threat map with the 19 86 forest cover map (see folowing section) These maps contain quantitative real values that we can now reclassify to obtain a qualitative map that is easier to interpret visually For example, we earlier defined five levels of criticality:... Rodriguez F IG 9.14 Graph of the deforestation percentage by 0.05 threat range, as a function of the threat, in the buffer zone and excluding the protected areas The linear behavior for threats between 0.1 and 0.7 confirms the validity of our model For lower threats, erratic behavior is probably due to errors in the registration/classification of the forest cover maps For very high threats, the model again... Washington, D.C.: Division of Ecological Services, Department of the Interior Ward, L and B Weigle 1993 To save a species: GIS for manatee research and management GIS World 6: 34–37 Welch, R., M Remillard, and R Slack 1988 Remote sensing and geographic information system techniques for aquatic resource evaluation Photogrammetric Engineering and Remote Sensing 54: 177–85 Williamson, S and D Hirth 1985 . weights for slopes are 5–15 percent slope ס 3.838; 15–30 percent slope ס 1.7 06; 30–45 percent slope ס 1.023; 45 60 percent slope ס 0.731; 60 –75 percent slope ס 0. 569 ; 75 percent slope ס 0. 465 . Note. area deforested was deter- mined for every level of threat (figure 9.14). For low levels of threats, deforestation was erratic, probably due to errors in F IG. 9.13 Forest cover changes for the. contamination. F IG. 9.12 Map of critical areas (19 86 forest cover mask of the total threat map of fig- ure 9.10) Using a GIS to Determine Critical Areas 123 critical areas map for forest, in this case a simple mask