Integration of Remote Sensing and GIS for
Land Use/Land Cover Mapping in Nil Wala Basin
4.3 Other Data:
From available latest photography (1994) the Landuse/Landcover patterns were interpreted and compiled in to 1:50,000 scale (Figure 3.0). There data convert to digital form. The existing landuse layer (1:50,000 Topographic map & 1:100,000 Landuse map) in a GIS was also extracted in order to find out the temporal changes. Figure 4.0 shows the 1983 Landuse/Landcover infromation.
Figure 3.0
Figure 4.0
4.4 Generation of Multidate Landuse/Landcover Information
From GIS Landuse layer (1:50,000) and 1994 Landcover eight different categories were chosen to identify major changes. The multi level data layer concept [3] was not applicable for this study to produce multi data Lanuse/Landcover layer because of the scale difference of the available data. Hence 1:100,000 information was not used for comparison.
Only five categories were chosen in classified Lansat TM (1992) image for comparison. It was not possible to identify the other categories due to their high spectral correlation.
5.0 Results of the Multitemperal Comparison
In multitemperal assessment the 1993 Landuse data compared with 1992 TM image and 1994 Landuse/landcover. The results of the part of study area pertaining to Landcover changess are summarised in Table 2.0. The Landuse/Landcover classes and their changes are briefly discussed below.
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Built-up land, Forest, water categories are not shown the major change when compare with 1994 landcover information.
- Paddy, homestlead and coconut were very much affected.
- Marginal decrease of the paddy cultivation observed
- A new landuse category also identified in the study area and it was abandoned paddy Vast stretch of this Landcover presented along the Nilwala River basin (see figure 2.0).
- Previous paddy cultivation now improved as homesteads and other plantation (cinnamon)
The classified image also shows the changes (Table 2.0) very similar to the results from aerial phto interpretation. There was a difficult when differentiating paddy, homestead and coconut because of identical ground reflectance.
Table 2.0 Results of the multi temporal comparsion.
6.0 Conclusion:
The results of this study clearly indicated that the landuse pattern of the study area have been changed. Although detailed information can be provided only by aerial holographs the study revealed the great potential of multispectral satellite data for broad level mapping and monitoring changes in Landuse/Landcover over a period of time. Socioeconomic development as well as topographic factors may be reasons for these changes. However, due to time constraints we couldn't analysis reasons for these changes and further studies will be continued. It was not possible to compare change with multi-temporal satellite data because of the lack of multi temporal satellite images. Further improvement of the study temporal satellite data also required.
It is not possible to give the entire result as they are being analyzed and will be submitted at the conference.
References
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EASI/PACE raster image processing Software, Canada.
- Rames S. Hooda and Dennis G.Dye. Identification and Mapping of Irrigated Vegetation using NDVI-Climatological Modeling, 16th Asian Conference on Remote Sensing 1995.
- Paul Suharto and Mostafa Radwan. Digital Mapping and Topographic Database aspects in the development of spatial information systems in Indonesia.
- Alden P. Colvocoresses. Image Mapping with the Tehmatic Mapper, Photogrametic Engineering and Remote Sensing September 1986.
