Land Use/Land Cover Change Detection in the Chiang Mai Area using Landsat TM
5. Results and Discussion
5.1 Results obtained from two-date classification for change detection Land use/land cover in the 1988 and 1991 image scenes was classified into 15 categories equally. The accuracy assesment was conducted for both classified data (Congalton, 1991) to obtain an overall accuracy level of 82% and 85% for the 1988 and 1991 respectively. The land use/land cover statistics derived by a direct comparison between the two classified image data are represented in Table 1. It was found that the use of some possible combinations for classifying the 1988 image did not provide a satisfactory result as indicated by difference between the classified image and the ground data. For example, the 234 band combination did not provide accurate information of built-up areas. A paddy field to the south-west of the Chiang Mai international airport was mis-classified as belonging to a high density built-up area. Nor did it provide good enhancement of water bodies. For this reason, the overall TM bands (except the thermal band) was chosen for extracting information on land use from the 1988 scene so that, regardless o date handling problem, all information contained in these original bands would be assessed. Another reason is that this data has a good quality in terms of stability and is less subject to noise (Jupp, 1994-personal communication).
Classification of the 1991 Landsat TM scene using the combination of bands 1-5 and 7 to give correspondence to the 1988 image yielded poor results. In particular, some shadows in the mountains were misclassified as belonging to water bodies.
The change image constructed by performing change matrix yielded 225 possible land cover changes in the study area (see Table 2). In Table 2, the number of unchanged pixels are represented by values along the diagonal of the matrix, while the number of changed pixels are represented by values off the diagonal. It was found that 350398 pixels (31536 ha) had not changed, while the number of changed pixels totaled 888074 (79927 ha). Only 14 major changes were considered meaningful in the comparison to the ground truth information, so that they were maintained in the change map as shown in Figure 2.
The biggest change was found from dipterocarp forest to low density built-up areas, which accounts for about 10422 hectares. In a descending order, the changing from dry paddy field to low density built-up areas accounts for about 7537 ha; change from dipterocarp forest to vacant land use accounts for 6996 ha; change form dipterocarp forest to dry paddy field accounts for about 5205 ha, and change from dipterocarp forest to mixed field crops accounts for about 4127 ha (see Table 3)
5.2 Change results obtained from the ratio image
The result of applying this technique shows that changes have occurred between 1988 and 1991 Based on the false color composite image created by combination of bands 3 from two dates, and its ratio (Figure 3), green and blue colors obviously highlight areas of change, especially from dry paddy field to development land (mostly construction sites) and from dipterocarp forest to vacant land use. The prominent change areas are the green valley resort in Mae Rim district, the Mae Kwang dam in Doi Saket district and the housing estate near Hang Dong district, which clearly appears green.
Areas of no change appear deep red, for example, hill evergreen forest at Doi Suthep (Suthep Mountain). cropland in Sansai district and soya beans cultivation in Hang Dong district.
A false color composite image created by combinations of bands 4 from two dates and its ratio does not give a satisfactory result compared with the first combination, therefore it is not acceptable.
6. Conclusions and Recommendations
The application of any change detection technique may be unsuccessful if user do not have enough knowledge about its characteristics in relation to the conditions over the area of study. Generally, the use of more than one technique is preferred by many researchers, because they can compare the results derived, and finally select the best ones for their project.
The technique of change detection based on two-date classification has been found to be less restricted in terms of the algorithms used when compared with other change detection techniques. For example, the use of image differencing requires that the geometry of an image must be known as accurately as possible (about a quarter to half a pixel). otherwise substantial errors in change detection will occur.
Close phenological correspondence between the images is also required, in order to reduce errors in change detection. However, change detection based on two-data classification requires good results from a classification.
The most important factors that should be taken into account when performing change detectin, as recommended by Jensen (1981), have involved the familiarity with the study area, the quality of the data set, and the characteristics of change detection algorithms.
It may be concluded that the use of Landsat TM for mapping land use/land cover change in the Chiang Mai area provided a satisfactory result if the appropriate techniques were used in data analysis. However, the research work on land use/land cover should be conducted on a regular interval, so that the information can be updated through time.
This research is further investigating the use of GIS data and sequential air photos of the test area as another means of studying land use/land cover change. The large scale of the air photographs allows comparatively accurate interpretation of the relevant land cover boundaries, and these can be recorded in the digital spatial data base through use of a digital Elevation Model (DEM) (Salamanca Software Pty Ltd. 1992). Thus it can be demonstrated that older as well as more modern elements of the national information infrastructure can be used to augment and to test the change detection results.
7. Acknowledgments
I wish to acknowledge the Thailand Remote Sensing Center for providing the Lansat data for this research project. Thanks are given to resource persons, from both Thailand and Australia e.g. Prof. Vanpen Surarerks (Dept. of Geography. Chiang Mai University. Dr. David Jupp (CSTRO, Division of Water Resources, Canberra, Australia) for their useful supervisors. A/Prof Jin Peterson and A/Prof. Paul Bishop for their assistance and encouragements throughout my research period.
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