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Analysis and estimation of deforestation using satellite imagery and GIS
Preprocessing and analysis of the satellite images
Usually three types of errors occur when a satellite image is generated by the satellite sensor. The first is the sensor error. The second is the error created by the atmospheric parameters, which affect the amount of radiation received by the sensor. The third one is the geometric errors related to the curvature of the Earth surface, the Earth rotation, elevation differences, location and situation of the satellite etc. Therefore, These errors should be considered and managed before using the data:
- Sensor errors
The two images used were already corrected by their providers. Therefore, there was no need for any processing in this regard.
- Radiometric correction
The Earth atmosphere scatters the shorter wavelengths in a selective manner and this reduces the contrast of the image. The numerical value of each pixel in the image is not a realistic representation of the amount of radiation from the ground surface. These values are changed either by atmospheric absorption or by scattering throughout the atmosphere. In general, atmospheric errors are discussed in three parts: the Haze, Sunangle and Skylight errors. Atmospheric corrections are required in the following situations:
- When we want to compare the images related to different times: When using methods such as image subtraction and image division for change detection, the effect of atmosphere on the two images related to different times are quite different.
- When the ratio of two bands of an image is needed to be calculated, because the atmosphere has different effects on different wavelengths.
- When we want to study spectral characteristics of different phenomena.
If we wanted to use the division or subtraction of images for determining the changes in forest landuse, then we would have to correct for the haze, sunangle and skylight errors. In our approach we compare the results of the landuse classification maps extracted from the two images. The classification of landuse can be done better and more accurate with the raw (unprocessed) images. Therefore, there was not any need for the above corrections in our images.
- Geometric corrections
The process and analysis of multi-temporal data can be done only when they are geo-referenced similarly, or in another words, when they are geo-referenced to each other. Our images had to be geo-referenced to each other with an accuracy of one pixel. Otherwise, the error coming from different coordinates for similar objects in the two images can be wrongly accepted as a landuse change. In other words, with an inaccurate geo-referencing, a pixel might refer to different objects in the two images and be considered as a landuse or land cover change, which is not realistic.
To prevent such a problem, in comparison of multi-temporal images, the best solution is to geo-reference one of the images using the available topographic maps and then geo-referencing the other images according to the first one, i.e. using image-to-image registration.
In photo/image registration (geo-referencing), the most important task is the proper selection of control points, especially when there is a long time period between the map and the image. Usually, man-made features such as buildings and road intersections are a better choice for control points than the natural ones. The reasons are that they have sharper boundaries and more contrast with their surrounding. Besides, they are geometrically more stable than features such as river/stream junctions. The general rules are that we should try to select more stable features that have longer change periods and the more control points we select the more accurate our registration will be.
We simply used the first order polynomial equations for geo-referencing of the images, which remove the errors related to the rotation and scaling of the image. These are:
X = a0 + a1x + a2y
Y = b0 + b1x + b2y
where x and y are the coordinates of a point in the first coordinate system and X and Y are its new coordinates in the new coordinate system.
In this study, the ETM+ image of the year 2001 was first geo-referenced using the information in its header approximately. Then, it was geo-referenced accurately using the available 1:25000 digital maps and the digitized features of the 1:50000 maps of the area. The control points were selected using different color composites with band-combinations of 754, 432 and 543. Afterward, the TM image of 1987 was geo-referenced using the already registered TM image.
For geo-referencing the 2001 image 18 control points were used initially. Every control point with an RMSE or residual error bigger than a pixel size was removed from the calculation and the process of registration was repeated with the rest of the control points. Finally, 10 points with the average error of 16.47 meters remained and were used for registration. For image-to-image registration of the 1987 image 20 control points were initially used. Finally, 6 points were removed and the image was geo-referenced using the remained 14 points with the RMSE of 18.92 meters.
In view of the fact that our images are used for landuse classification, any change to the numeric value of the pixels will introduce some errors and has an undesirable effect on our classification. Therefore, to minimize this effect during geometric correction, the new values of pixels were generated using the nearest neighbor method.
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