Analysis and estimation of deforestation using satellite imagery and GIS
In the second stage, the elevation variable entered the model and changed the X2 parameter to the value 272.826. Having the two parameters of elevation and distance from villages together in the model, 18.55% of the changed (deforested) pixels and 93.82% of unchanged pixels were predicted correctly by the model. This stage showed a total accuracy of 75.30% in the prediction of pixels (Table 1).
In the third stage, the aspect variable entered the model and caused a significant improvement in the X2 parameter, changing it into 92.681. The three parameters of elevation, distance from villages and aspect (aspect of slope) being incorporated in the model, 31.93% of the changed pixels and 93.90% of unchanged pixels were predicted correctly by the model. This resulted in a total accuracy of 78.65% in the prediction of pixels, as can be extracted from the last two columns of Table 1.
Table 1 Prediction accuracy of the model in different stages of introducing variables
| |
Predicted as changed (1st stage) |
Predicted as unchanged (1st stage) |
Predicted as changed (2nd stage) |
Predicted as unchanged (2nd stage) |
Predicted as changed (3rd stage) |
Predicted as unchanged (3rd stage) |
| Changed Pixels |
153 |
1103 |
233 |
1023 |
401 |
855 |
| Unchanged pixels |
290 |
3560 |
238 |
3612 |
235 |
3615 |
After the third stage, no other independent variable could enter the model. This means that the only remained variable, i.e. slope, could not cause any significant improvement to the performance of the model and its suitability with the data. This can be either because of the irrelevance of this variable to the phenomenon, or because of its high correlation with other variables incorporated in the model.
Table 2 shows the results of the calibration of the model. The model represents the phenomenon of forest-stability (as opposed to deforestation) on the basis of three factors of distance from population centers, elevation and aspect. The coefficients of these factors in the resulted model are presented in Table 2.
Table 2 Coefficients of variables in the resulted regression model
| Factor (variable) name |
Coefficient in the logistic regression model |
| Distance from population centers |
0.0010 |
| Elevation |
0.0068 |
| Aspect |
0.0027 |
| Constant value |
- 9.8675 |
Conclusions and future work
The following remarks and recommendations can be concluded from this study:
- In analysis and comparison of images of different times, the error (accuracy) of geo-referencing for the images should be smaller than a pixel dimension. Otherwise, the difference in the geometry and location of any feature in the two images will result in the acceptance of an unrealistic change.
- In this research, we first needed to classify the satellite images. The changes introduced to the pixel-values, by interpolation and during geometric correction, have an undesirable effect on the result of classification. To moderate this effect, the new values of pixels can be generated using the nearest neighbor method.
- As was expected before the research, by moving away from the population centers the stability of forests increase. This is because, in the vicinity of villages, the forests are cut down mainly with the intention of using the land for agriculture and grazing and using the wood as fuel.
- In areas with higher elevation forest is more stable. This is partly because in high areas the environment in general is cleaner and more intact. The higher the area, the less suitable it is for agriculture and the more difficult it is to go.
- The slope aspect has an important role in deforestation in this area. The inclines and hills toward south get more sunlight and therefore are more suitable for agriculture. On the other hand, the east and north directions are enjoying the humidity coming from the Caspian Sea.
- From the beginning, we were aware of the possible dependency among the introduced factors, but the types of dependencies were not clear. It was proved that the slope has a high correlation with other factors, mainly with distance from villages and elevation. Therefore, the importance of comprehensive studies about factors affecting a phenomenon before modeling it has been proved.
- There are many other factors that might be relevant to deforestation and are not covered in this study. Examples of such factors are soil type, distance from the roads and population of the villages.
- In studies related to landuse change between years, effort should be made to use the images of the same season and even the same month. When images are from the same month or season, the changes detected from them are more realistic and reliable.
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