Tropical Forest Cover Typees Differntiation using Satellite
Optical and Radar Data:A Case Study From Jambi, Indonesia
Result and Discussions
Results of Supervised Classification of Landsat TM image
The supervised classification of Landsat TM data resulted into nine classes: water, swamp, agriculture, logged-over forest, secondary forest rubber, oil palm, grass/along and cleas cut areas. It was not possible to have more than nine classes because of the overlap between the clusters in the feature space. It was especially difficult to separate settlements from the forest/agriculture or old secondary forest from the young secondary forest. This was due to the fact that settlements in the study area are always surrounded by the home-sated gardens which include different types of trees that exist in the forest area too. This results in mixed spected reflectance characteristics. Thus it was decided to combine some of these classes.
Settlements were not classified on TM image due to the fact that the people in the study area grow trees/fruit tree, agriculture crops in and around their houses as homestead gardens which it spectrally confusing with other classes. It was also difficult to classify rice separately as there was overlap between rice and other agriculture fields or bare land farmers in the study area normally plant rice starting in May and harvesting in September. Since the Landsat TM image was acquired in September( 15 September), rice could take on the features of other agriculture fields or bare soil
Results of Supervised Classification of SPOT image
Supervised classification of SPOT image resulted in six classes: water, agriculture, logged-over forest, secondary forest, and rubber and clear-cut areas. The presence of clouds and haze in the image affecting the spectral reflectance value of the cover types. Classifying further classes resulted in overlap between the clusters and misclassification of the true classes
Results of Supervised Classification of JERS-SAR image
The supervised classification of JERS-1 SAR image comes up with seven land cover classes. These classes are: water, agriculture, logged-over forest, secondary forest, rubber, clear cut areas and settlements, Since JERS-1image has single band (L-band 23.5 cm) HH polarization, with incidence angle 35 degrees, therefore it was not
Possible to have more than seven classes because overlap was occurring between the clusters in the feature space for further classification. Oil palm was not classified as this was always misclassified as rubber or agriculture fields were also were classified as clear cut areas because these fields were either barren or having very young crops which acted as specular reflector. Similarly some misclassification also occurred among water, agriculture and clear-cut areas. Forest classification was better comparing to ERS-1.
However settlement were significantly clearer on JERS-1 image as compared to optical images because of the radar corner reflection phenomena. The appearance of settlements on radar image is due to geometric configuration of urban features. The most favorable geometric configuration for an object to become a corner reflector are often man made structures. The side of a building combined with reflection from a ground is an example of such a structure. When two such surfaces are at right angles and open to the radar, a dihedral corner reflector is formed. A wave scattered from a dihedral corner reflector will undergo a reflection at each surface and will return in the direction from which it came. Therefore settlements in the radar image have high backscatter values.
Results of the supervised classification of ERS -1 SAR image
Like JERS-1 image ERS-1 image have also single band (C-band 5.6cm) VV polarization with 22 degrees incidence angle but supervised classification of ERS-1 image resulted in to five classes only as shown in these are: water, forest, agriculture, clear-cut areas settlements. In ERS-1 rubber and secondary forest were having overlap with logged-cover forest and therefore these three classes were merged in to a single class. One possible reason for is the short wave length (C-band=5.6cm) of ERS-1 which does not interest with forest cover types. Oil palm as in JERS-1 was again not classified in this image. Similarly some misclassification and overlap was also noticed among water, agriculture and clear cut areas. However settlements were classified clearly with high accuracy because they acted as corner reflector as mentioned earlier for JERS-1.
Visual interpretation in general, ERS-1 image was able to recognize three more classes that was not able to recognize during digital classification. These are: secondary forest, oil palm and rice. JERS-1 image was also able to recognize four more classes i.e. rice and oil palm and one subclass of agriculture and secondary forest each. The ability to recognize forest, rubber and oil palm plantation using JERS -1 were better than ERS-1. This was due to L-band(23.cm wavelength) of JERS-1 which penetrate through the vegetation canopies better than the C-band(5.6cm) of ERS-1. Similarly interpretation of TM image was able to recognize seven (7) more classes than the digital classification. These are: settlements, rice and roads one more subclass of logged-over forest, secondary forest, rubber, oil palm and agriculture each. However, the appearances of settlements on radar image (especially in ERS-1 image) were significantly clearer than the TM image because of the radar image reflection phenomena. As said earlier that appearance of geometric configuration for an object to become a corner reflector are often man made structure. The side of building combined with reflection from a ground is an example of each a structure. When two such surfaces are at right angles and open to the radar, a dihedral corner reflector is formed. A wave scattered
from a dihedral corner reflector will undergo a reflection at each surface and will return in the direction from which it came. Therefore settlements in the image appear very bright. The river, rice fields and clear-cut areas appeared darker on the radar image. This is due to specular reflection from these classes. This means that radar energy reflects away form the antenna (e.g. mirror like reflection). Thus little radar energy returned to the antenna
