Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite

Transcription

1 Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite R.Manonmani, G.Mary Divya Suganya Institute of Remote Sensing, Anna University, Chennai ABSTRACT Information on landuse/landcover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land. In the study, Remote Sensing and geographic information system (GIS) were used in order to study landuse/landcover changes. Land use change may influence many natural phenomena and ecological processes, including runoff, soil erosion and sedimentation and soil conditions. The Urban areas are changing due to various human activities, natural conditions and development activities. According to the user requirements, updating of landuse mapping is required to various departments. The aims of this study are to detect land use changes between 1990 to 2005 using satellite images of Land Sat 7 ETM+ (1990) and IRS LISS III (2005) and digital topographic maps have been used. The objectives of the study is to see the landuse/landcover changes in Urban areas and identifying hotspots of land cover changes using multi temporal satellite data and also studying relationship between human pressure on landuse/landcover and its impacts in the vital Urban habitats. Landuse changes have been detected by image processing method in EDRAS imagine. Finally to predict the changes in Urban habitants and landuse/landcover changes occurred. Monitoring of landuse/landcover changes which would help to plan development activities such as major schemes and for used requirements. Change detection has shown that the built up area increased between 1990 and 2005 by 15.83% from ha to ha. Also, the area with irrigated land farms have been decreased to ha (2.48%) and the scrub land decreased to 5.19%. Keywords: Landuse/landcover, Change detection 1. Introduction Landuse is influenced by economic, cultural, political, and historical and land tenure factors at multiple scales. Land use referred to as man s activities and the various uses which are carried on land. Landcover is referred to as natural vegetation, water bodies, rock/soil, artificial cover and others resulting due to land transformation. Since both landuse/landcover are closely related and are not mutually exclusive they are interchange able as the former is inferred based on the land cover and on there contextual evidence. A serious problem for modeling urban landuse change has been the lack of spatially detailed data. GIS and remote sensing have the potential to support such models, by providing data and analytical tools for the study of urban environments. Urban land cover types and their areal distributions are fundamental data required for a wide range of studies in the physical and social science, as well as by municipalities for land planning purposes (Stefanov, 2001). The technologies of Geographical Information Systems (GIS) and Remote Sensing have been combined to detect and control urban encroachment in a way which 60

2 is easier and faster then the traditional methods of surveying the urban environment (Da Costa, 1999). In this study, in years period, land use changes in Villivakkam were examined. Changes in residential areas and wasteland and water bodies were exposed. Urban land use characteristics were combined with some plans aiming at urban development and thus it is proposed for future land use decisions to local authorities. Information on landuse/landcover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land for agriculture studies, economic production etc. Today, with the growing population pressure, low man land ratio and increasing land degradation, the need for optimum utilization of land assumes much greater relevance. 2. Study Area The Villivakkam block (study area) of Thiruvallur district is located in between N to N Latitudes and E to E Longitudes, falling in SOI Toposheets 66C4 with an aerial extent of ha and found in the west of Chennai. Villivakkam is one of the most rapidly populated area and landuse changes regions of Thiruvallur district. From 1990 to 2005, resident population is nearly doubled. 3. Data Sources Data in this study are used of digital topographic maps dated 1976 and with an 1:50000 scale. Landsat TM and IRS P6 LISS III satellite data were used to generate land use map for 1990 and Methodology The studies of monitoring urban residential growth of Villivakkam block were first started with a topographic map prepared in 1976 and comparing this topographic map with satellite image. For past 15 years, constructional activities increased depending on population growth and due to opening of more industry, with the construction of airport and establishment of the university, an 61

3 increasing residential construction was determined through satellite image. The flow chart depicting the study area are illustrated below Figure 1. SOI Toposheet Base Layer Landsat Satellite Visually interpreted using On Screen Key interpretation elements Land Use Map 1990 Change Detection Map IRS P6 Satellite Land Use Map 2005 Accuracy assessment 4.1 Data pre processing Figure1. Flow chart depicting the change detection method Pre processing has involved scanning and digitization of Survey of India Toposheets at 1:50000 scale to serve as the base map. Scanned maps don t usually contain information as to where the area represented on the map fits on the surface of the earth, for these images have to register coordinates. To establish the relationship between an image (row, column) coordinate system and a map (x, y) coordinate system we need to align or georeference the raster data (image). Processing has involved application of various GIS function and advanced digital image processing technique including contrast manipulation, edge enchancement, and image registered. The images were geometrically rectified and registered to the same projection namely, Transverse Mercator WGS 1984 to lay them over each other 4.2 Image classification The initial LandSat (1990) and final (2005) IRS IC LISS III imageries were subjected to a classification zones. Visual image interpretation was utilized to classify the images to different landuse categories. In order to classify the rectified images, five classes were delineated in the images namely, agriculture, fallow land, scrub land, industry and built up. The overall testing accuracy for the classification of Landsat (1990) was 82.14%, while it was 86.46% for IRS IC LISS III image (2005). The land use map prepared for the year 1990 and 2005 are shown in figure 2 and 3 respectively. 4.3 Change detection Change detection analysis encompasses a broad range of methods used to identify, describe and quantity differences between images of the some scene at different times or under different conditions many of the tools can be used independently or in combination or in combination as 62

4 part of a change detection analysis. Change detection menu after a straight forward approach to measuring changes between a pair of images that represent a pair of images that represent on initial stage and final stage. The change detection statistics for classification images average used for the compute Thiruvallur District Villivakkam Block difference map for Land Use Map for the year 2005 using LISSIII Satellite Data Land Use Map images. ± Legend Perenial Tank River / Stream Industry Settlement Scrub Plantation Salt affected Slight Fallow Agriculture Dry Tank Km Figure 2. Map showing the Landuse categories for the year 2005 Thiruvallur District Villivakkam Block Land Use Map for the year 1990 using Landsat Satellite Data Land Use Map ± Legend Perenial Tank Dry Tank Agriculture River / Stream Settlement Fallow Scrub Salt affected slight Plantation Km 63

5 Figure 3. Map showing the Landuse categories for the year 2005 Thiruvallur District Villivakkam Block Change Detection map for the year Change Detection Map ± Legend Decrease in Area Increase in Area Unchanged Km Figure 4. Change Detection Map showing the Landuse categories for the year Results and Discussion The land use categories such as built up land, agriculture, water body, wasteland and others have been identified and mapped from the Landsat TM and IRS LISS III of 1990 and The change detection map is presented in figure 4. About 36.99% of the areas are occupied by builtup land during 1990 and about 52.82% areas are occupied by built up during People utilize the land for agricultural purposes. Under utilization, mis management could be observed in the field. As a result the yield is not optimum.the area occupied by the agriculture is about 16.59% (1990) and 14.11% (2005). This is due to shifting of agricultural land to built up. From the rainfall data collected for a period of 10 years, it sis understood that the mean annual rainfall is above the normal rainfall. Owing to the increase in human population the plantation category have been decreased from 0.67% to 0.61%. Decrease of about 2272 ha of the wasteland during 2005 when compared to Some small scale Industries has also been found during 2005 which would help the human employment. Table 1 shows the change in landuse pattern. 64

6 Landuse / Land cover 1990 (Ha) Area in % 2005(Ha) Area in % Difference types Built up Agriculture land Fallow land Wasteland Industry nil nil nil Perennial waterbodies Dry water bodies River/stream Plantation Total Geographic Area Conclusions Table 1. Area under different land use / land cover categories during The present study shows that satellite remote sensing based land cover mapping is very effective. The high resolution satellite data such as LISS III data and Landsat TM are good source to provide information accurately. Under utilization of potential land, increased population, and land conversion are the major driving forces for the change in land use during the past 15 years. The overall accuracy of the present land cover study is 85%. Based on the analysis of changes in land use / land cover some of the remedial measures are suggested, which are essential for optimum and sustainable utilization of land resources and prevention of further undesirable and deteriorated changes in land use. Crop rotation could help to improve the land potential and to avoid poor yield. Base on the soil suitability fruit trees could be planted to improve the economy of the people. 8. References 1. Mas, J.F., 1999, Monitoring land cover changes: a comparison of change detection techniques, International Journal of Remote Sensing, 20(1), National Remote Sensing Agency / Project report, 2006, National land use and land cover mapping using Multi Temporal AWiFS data 65