Integration of Remote Sensing and GIS for
Land Use/Land Cover Mapping in Nil Wala Basin
W.T.G. Mendis & A. Wadigamangawa
Survey department, Sri Lanka
Development of Remote Sensing and Geographic Information System (GIS) technologies have lead to the betterment of mapping and interpretation techniques as a means of understanding and effectively managing the present resources for sustainable. Analysis of mechanisms of land use pattern changes plays an important role in not only forecasting changes but also formulating local development policies.
The Nilwala Ganga Rehabilitation Project Program of Southern Province is being implemented by the Governmented by the Government. The Landuse/Landcover is one of the most important layes inany GIS for Agricultural Resources Management.
The main objective of this study is to find out changes of Land use/Land cover patterns due to implementation of the Nilwala Ganga Flood Protection Scheme.Both air borne and space borne remote sensing offer efficient and timely data for monitoring spatial changes of patterns over a period of time. A comparative study was also carried out in order to find the level of information obtainable from two remote sensing techniques. The result from this study showed that the Integration of Remote Sensing and GIS appears to have a potential for providing necessary information for updating Landuse maps.
Remote Sensing is a powerful technique for Surveying, Mapping and Monitoring earth resources. This technology combined with GIS which excels in storage, manipulation and analysis for Geographic information and Socio-economics data provide a wider application. Land resource and environmental decision makers require quantitative information on the spatial distribution of land use types and their conditions as well as temporal changes. Remotely sensed satellite data in conjunction with available other data sources have been used to find such land uses.
The Nilwala basin project in the southern province was originated in 1986 to over come seasonal flooding However the presentation of floods had reduced fertility of the irritable land. As a result of this there have been changes in land use pattern. That means ecology as well as socio-economic balances of the region have been changed. Hence proper land use planing is a major requirement in order to prevent these situation.
The major objective of this study was to find out changes of land use/land cover pattern due to implementation of the Nilwala Ganga Flood Protection Scheme (NGFPS). Then to analysis reasons for these changes and to propose a method for identifying major changes using satellite imagery in conjuction with exiting land use information in GIS.
3.0 Study Area
The study area covers Nilwala River Basin in the Southern Province of Sri Lanka. Catchment area of 971.0 km2. Fallingng within the latitude and longitude between 5o 55′ & 6o 13′ and 80o 25 80o 38′. ( as shown in Diagram 1.0)
4.0 Data used and Methodology
4.1 Data Used:
The data used are given in table 1.0
||30 m Resolution
Figure 1.0 (Proposed Methodology)
4.2 Satellite Data:
PCI Software  that is an integrated image processing & Geographic Information System was used for digital analysis of analysis of Landsat Thematic Mapper ( TM) Data. The aim of the digital processing was to generate a classified image.
The atmospheric corrections performed prior to the image classification. Then the image was geo-referenced using 1:50,000 Topographic map of the study area. Considering the most useful combination of spectral bands for Landuse/landcover mapping, our first choice was only bands B3,B5 and B7. If NDVI (Normalised Difference Vegetation Index, i.e. B4-B3/B4-B3) is correlated with climatic variables that are known to influence plant growth . Hence, there is a possibility to distinguish irrigated vegetation and natural vegetation. Keeping that in mind, NDVI was calculated for TM data since it is an effective indicator of the amount of green vegetation presented in an observed Landscape.
The selection of training areas were based on spectral signature and spectral seperatebility among classes. Training statistics of each class (variance -covariance matrix) were generated. Finally, a classified image was generated by supervised classification based on Maximum Likelihood classifier. The Landuse boundaries of the classified image were then convert to vector in order to compose 1992 data of land cover (Figure 2.0)