REMOTE SENSING AND GIS IN LAND USE PLANNING Sathees kumar P 1, Nisha Radhakrishnan 2 1 1 Ph.D Research Scholar, Department of Civil Engineering, National Institute of Technology, Tiruchirappalli- 620015, E-mail: geosat08@gmail.com 2 2 Assistant Professor, Department of Civil Engineering, National Institute of Technology, Tiruchirappalli- 620015, E-mail: nisha@nitt.edu Abstract: About the Author: The different land use categories and their spatial and temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998-2006), from the analysis of topographical map, IRS 1D and IRS P6 for the year 1973, 1998, 2002 and 2006 using ArcGIS and ERDAS Imagine 9.1. In this study, based on the results of classified images, the agricultural land coverage area was reduced 7.8% from the year 1998 to 2006, While the area under settlement increased 14.7% from the year 1998 to 2006. Mr. Sathees Kumar P, Research Scholar I am presently pursuing Ph.D in the area of Land Use Planning under the guidance of Dr. Nisha Radhakrishnan in NITT. I did my master degree in Geoinformatics, College Of Engineering, Guindy Anna University, Chennai. Keywords: land use land cover, change detection. E mail ID: geosat08@gmail.com & 403109003@nitt.edu Contact No: +91 9486125352 Page 1 of 7
Introduction Planning and development of urban areas with infrastructure, utilities, and services has its legitimate importance. It requires high level of competence, comparable tools and techniques. Urban planning and management basically involves the most effective and efficient use of land. Land is a limited resource. The growing pressure of population coupled with an increasing variety of demands being made on land resources have brought extra pressure on the available resources all over the country. Land use is a key concept in the town planning profession. A major objective of planning analysis is to determine how much space and what kind of facilities a community will need for activities, in order to perform its functions. An inventory of land uses will show the kind and amount of space used by the urban system. It will permit comparisons among different towns, and would give a basis for deriving space and facility requirements from economic and population projections. Land use land cover classification is a time consuming and expensive processes. Remote sensing offers a quick and effective approach to the classification and mapping of land use land cover changes over space and time. Information on changes in land resource classes, direction, area and pattern of land use land cover classes form a basis for future planning (Palaniyandi and Nagarathinam, 1997). In Tiruchirappalli, where up-to-date information is frequently missing in data banks at Municipal and State level, the information obtained from these work could eventually fill this gap. Objectives: The objectives of the study (i) (ii) (iii) (iv) (v) (vi) To study the LULC information for the study area. To define classes based on LULC for India. To find suitable method for LULC Classification for the study area. To find spatial and temporal changes in the study area by using ERDAS IMAGINE9.1. To study the trend analysis based on multi-dated satellite images. To provide recommendations to guide policy and decision making towards effective ways to cope with the adverse implications of these changes. Page 2 of 7
Study Area: The study area is Tiruchirappalli town, Tamilnadu (Fig.1), one of the famous historical and cultural cities in India. The spatial extend of the study area is between 10 44 46 N to 10 44 46 N Latitude and 78 39 11 78 44 13 E Longitude. The study covers an area of approximately 14360 ha. The average annual rainfall is 821.4 mm. The mean maximum and minimum temperature range from 41.1 0 C and 18.6 0 C, respectively. The area has experienced remarkable land cover changes due to urban expansion, population pressure and various economic activities. Fig: 1 Location of Study Area (Ref- tnmaps.tn.nic.in) Materials and Methods In this study, IRS data of 1998 to 2006, SOI Toposheet 58 J/9, J/10, J/13, J/14 (1:50,000) were selected and used to to find the spatial and temporal changes in the study area (Table 1). As the digital data did not have any real earth coordinates, data were geometrically corrected using ground control points viz. road road intersection, road rail intersection, canal road intersection, etc. were taken from the toposheet using ERDAS IMAGINE 9.1 image processing package. Page 3 of 7
Table 1 Satellite data used in the study Sl.No Data product Imagery Date Resolution (m) Path/Row 1 IRS-1D PAN 19/10/1998 5 (101-066) 2 IRS-1D LISS III 05/06/2002 23.5 (101-066) 3 IRS-P6 LISS III 22/02/2006 23.5 (102-112) 4 IRS-P6 LISS IV 13/06/2006 5.8 (101-066) Different land use/land cover classes like agriculture, settlement with vegetation, fallow land, plantation, sand, and river etc. were then identified using visual interpretation keys such as colour, tone, texture, pattern, size and shape.description of these land cover classes are presented in Table 2. IRS images were compared supervised classification technique (David and Verbyla, 1995). In the supervised classification technique, three images with different dates are independently classified. Accurate classifications are imperative to insure precise change-detection results. A Supervised classification method was carried out using training areas and test data for accuracy assessment. Maximum Likelihood Algorithm was employed to detect the land cover types in ERDAS Imagine9.1. Table 2 Land Use & Land Cover Classification Categories (Palaniyandi and Nagarathinam, 1997). Category No Level I Level II 01 Built-up Land Urban/Rural Settlement 02 Agricultural Land 2.1 Crop Land 2.2 Fallow/Harvested Land 2.3 Plantation 03 Forest 3.1 Dense Forest 3.2 Degraded / Open forest 3.3 Forest Blank 3.4 Forest Plantation 04 Wasteland 4.1 Salt affected Land 4.2 Gullied / Ravinous 4.3 Land with / without scrub 4.4 Sandy Area 4.5 Barren Rocky / Stony waste / Sheet Rock 05 Water bodies 5.1 River / Stream 5.2 Lake / Reservoir /Tank / canal 5.3 Tank with scrub / Plantation Page 4 of 7
Methodology: Define classes on Land use Land cover rules in India (Ref- Anji Reddy) Data Acquisition GPS Field survey Image processing registration and sub setting Multi-Temporal Signature Generation Multi-Temporal Supervised classification derived from Signature Post-classification processing Accuracy Assessment Land use & land cover change detection Analysing Land use & land cover changes using multitemporal images Fig: 2 Methodology Flowchart Page 5 of 7
Results and Discussion: This paper aim in investigating land use/land cover changes occurring in Tiruchirappalli city between 1998 and 2006 using remote sensing and GIS. The main change observed for the time period of 1998-2006 was that the area of agriculture decreased approximately by 1071 ha, and settlement area increased approximately by 2025 ha. The uncultivated area increased approximately by 1319 ha (Table.2).The settlement area is increasing because of the population growth in the city. The area of the water bodies are differing based on the seasonal variation of the data. Because of the settlement growth in the study area the waste lands are decreasing constantly. Land use land cover Categories Table 2 Land Use & Land Cover distribution 1998 2002 2006 Area(Ha) Area (%) Area(Ha) Area (%) Area(Ha) Area (%) Agricultural 2521.3310 18.328 1641.9498 11.936 1449.4180 10.536 Water 501.5081 3.646 136.2746 0.990 232.5825 1.691 Uncultivated Land 3563.7800 25.906 3986.1530 28.976 4883.3130 35.498 Waste Land 3832.4730 27.859 3169.9470 23.043 1828.4380 13.291 Settlement 3337.5950 24.261 4822.3640 35.055 5362.9380 38.984 Fig: 3 Comparative LULC Pattern of Tiruchirappalli City Page 6 of 7
Conclusion The results of the study suggest that the analysis of sequential satellite data offers means of extraction of information on land use land cover. In fact, for shorter intervals satellite data are very helpful for the detection of land use land cover changes, due to repetitive coverage at very short intervals. In this study analysis has been done for period of 8 years. In future it is planned to develop land use and land cover changes with higher precision qualitatively and quantitatively to give better trend analysis. The results of the study suggest that the References: 1. Anji Reddy, M., Remote Sensing and Geographical Information Systems, BS Publications, 2001. 2. David., and Verbyla, L., Satellite Remote Sensing of Natural Resources, Lewis Publishers, 1995. 3. Palaniyandi, M., and Nagarathinam, V., (1997). Land Use/Land Cover Mapping and Change Detection Using Space Borne Data. Journal of the Indian Society of Remote Sensing, Vol.25, No.I. 4. http://tnmaps.tn.nic.in/district Page 7 of 7