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Forestry
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Detecting Tropical Deforestation Using Satellite Radar Data:
A Case Study From Central Sumatra, Indonesia
Radar interpretation can be separated into two approaches: (1) visual and (2) digital. Visual interpretation is still the simplest and the most powerful approach to classify the radar imagery in which always difficult to be interpreted by the presents of the speckle noise. However, classification needs the knowledge about the study area obtained by field data checking. The image elements used for visual interpretation are tone, texture, pattern, location and association. These last element was the most important element. Some tone or texture differences are detectable from the backscatter differences. However, without the knowledge about the area, those detectable and delineable areas are almost impossible to the known.
An alternate process of classification is digital image processing by supervised classification. This classification mainly involve the computer and again also the prior knowledge of the area. Actually the visual interpretation of radar imagery is not pure manual step of image classification, because, before the radar images are interpreted visually, some processing by computer was also done, (image subsetting, georeferencing, and filtering), in the same way as for the digital image classification approach by supervised classification.
Table 2. Comparison between two data sets (DS1 and DS2)
| First Data Sets (DS1)(ers1:red, ers2: green, ers3:blue) |
Second Data Sets (DS2)(jers:red, ers2:green, res3:blue) |
| Recognize 6 classes |
Recognize 7 classes |
| Can separate old secondary forest and young secondary forest |
Can not separate the forest into log over forest and secondary forest |
| Can not recognize oil palm, because mixed with the forest itself |
Can see oil palm separate from forest, but can not distinguish it from rubber |
| Can recognize the wet area |
Can recognize the wet area |
| Clear cut can be seen but bit difficult to detect |
Clear cut easy to detect |
| Rice is more easy to detect |
Rice can be seen, but not so clear because mixed with water and wet area |
| Agriculture mixed with the tree (rubber and forest itself) |
Agriculture mixed with the tree (rubber) |
| Can not separate the forest stand from another perennial tree like rubber |
Can not separate the forest stand from another perennial tree like rubber |
Table 3. The comparison of the result of both classification approaches
| Some remarks |
Visual Interpretation |
Digital Image processing |
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Single JERS |
Single ERS |
Data sets with JERS and ERS |
Data sets with ERS only |
| Number of classes |
11 |
8 |
7 |
6 |
| Separate log over and secondary forest |
yes |
yes |
no |
no |
| Separate old and secondary forest |
yes |
no |
yes |
no |
| Separate rubber from forest |
yes |
no |
yes |
no |
| Detect clear cut |
yes |
yes |
yes |
Yes but not so clear |
| Separate oil palm from forest |
Yes, but not so clear |
yes |
yes |
No |
Reference
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De Gier, A., 1995, Your 7,000 Square Meters. Inaugural address, International Institute for Aerospace Survey and Earth Sciences (ITC), Enschede, The Netherlands.
- Hoekman, D.H., 1990, Radar Remote Sensing Data For Application in Forestry, Ph d. Thesis, Wageningen Agriculture University, The Netherlands.
- Hoffer, R.M., Maxwell, S., Ochis, H., 1995, Use of Radar For Forestry Applications, Colorado State University, fort Collins, Colorado.
- Hussin, Y.A. and Shaker, S.R., 1995, Tropical Rain Forest Land Use Detection Analysis Using Remotely Sensed Data and GIS: A Case Study From Sumatra, Indonesia, Annual conference and Exposition Proceedings, Volume, 1, 1995, Nashville, Tennessee.
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