Analysis and estimation of deforestation using satellite imagery and GIS Assessment of deforestation by comparison of the classification maps One of the methods for change detection using satellite images is to compare the results of classification of the images. Two other methods are to calculate the division or subtraction of the two images. The main problem of these methods is that they can only define where some changes are happened. The advantage of the classified-map comparison method in to the other methods is that not only the location but also the nature and type of the changes will be determined. In other words we will define what landuse has been changed to what other. In this method, first, the images of different times are classified according to the purpose of change detection. Afterward, by overlaying the two classified images with a proper overlay condition, we can determine the location and amount of any changes we are interested in. Because our goal was to determine the deforestation, the only two classes that we considered are the forest and non-forest. The two images are classified using the Maximum-Likelihood method. By overlaying the results of classifications, the map of the occurred changes are resulted, as is shown in Figure 1. From this map, it can be realized how much of the forest have been damaged and where this has happened. In addition, the pattern and spatial distribution of the phenomenon is properly illustrated. Furthermore, it can be seen where the forest and non-forest classes have been stable and where new forest has been growing. Creation of the logistic regression model A regression model is a statistical model in which a relation between a phenomenon (a dependent variable) and some of its factors (some independent variables) will be defined based on some observations. These observations are in fact a set of values measured or observed for the dependent and independent variables. Having the model specified and calibrated, the unknown value of the phenomenon can be calculated and predicted on the basis of known values of its factors. Logistic regression models, a special type of regression models, are used when we want to study the probability of membership in two contradictory classes, such as a forest area being either stable or destroyed. It should be noted that logistic regression can be used to determine the probability of any of the two possibilities (classes) identically. A logistic regression model is usually of the type: Here, p is the dependent variable and shows the probability of one of the two conditions. Dependent variables of x1, x2 and x3 represent the factors defining the phenomenon and b1, b2 and b3 are their coefficients: a is the additive coefficient. 
In this research, we selected a sampling set of about 5% of the pixels, from the two classes of stable forest and destroyed forests, i.e. forests that have remained forest and forests that have been changed or destroyed. The number of sample pixels is 5106 pixels in total. In these sample pixels, the parameters of elevation, slope, aspect and distance from villages are considered as independent variables and the stability of the forest as the dependent variable. As mentioned, we could use the forest destruction (deforestation) as the dependent variable and get exactly the same results. Independent variables are extracted from the relevant generated maps. The dependent variable, i.e. the forest stability, is represented by the two values of 0 and 1 for the sample pixels. 0 represents the deforested areas and 1 represents the stable forests. By introducing the sample data to the specified logistic regression model, in the first stage, the variable of distance from population centers entered to the model, improving the X2 parameter to the value 601.641. This parameter (called square of chi) is a measure for the goodness of the model; a low value for X2 means the model is suitable to the data. The effectiveness of the model in prediction of the phenomenon can be summarized in a simple table (Table 1). From the total 5106 sample pixels, 1256 pixel were changed (deforested) and 3850 pixels were unchanged forest. In every stage of the regression, all pixels are evaluated by the model and a value is predicted for each pixel. The number of both correctly and wrongly predicted pixels for each stage is shown in Table 1. In other words, in this table, the groups of deforested and unchanged forest pixels are compared with what is predicted for them by the model. From the 2nd and 3rd column of the table is clear that 12.18% of the changed (deforested) pixels and 92.47% of unchanged pixels are predicted correctly by the model. This means a total prediction-accuracy of 72.72%. |