URBA PATTER AALYSIS -MAJOR CITIES I IDIA. Sowmyashree. M.V 1,3, T.V. Ramachandra 1,2,3. 1 Centre for Ecological Science. 2 Centre for Sustainable Technology. 3 Centre for infrastructure, Sustainable Transportation and Urban Planning. Indian Institute of science, Bangalore- 560012. Abstract Urban pattern is the way how different functions and elements of the settlement are distributed and mixed together spatially. This study has been done for major cities of India Mumbai, Kolkata, Coimbatore, Hyderabad, Chennai, Bhopal, Ahmedabad, Pune to understand the spatial and temporal pattern of the urban sprawl in using satellite images. Landsat MSS, TM and ETM+ datasets are used in five years interval. Based on these thirty one landscape metrics were computed. Results indicate that the built up area increased rapidly in the period 1995 to 2010 and urban pattern was more dispersed and fragmented because of less planning strategies. Keywords: Urban pattern, spatial metrics. Introduction The extent of urbanization or the sprawl is one such phenomenon that drives the change in urban patterns. Remote sensing methods have been widely applied in mapping land surface features in urban areas(e.g. Haack et al., 1997; Jensen & Cowen, 1999). The rapid development of remote sensing technology, together with the spatial metrics provides a better interpretation and verification of how urban patterns evolve and change over time. Recently there has been an increasing interest in applying spatial metrics techniques in urban environments because these help bring out the spatial component in urban structure (both intra- and inter-city) and in the dynamics of change and growth processes(alberti & Waddell, 2000; Barnsley & Barr, 1997; Herold, Clarke, & Scepan, 2002). Landscape metrics helps to quantify spatial patterns in heterogeneous landscapes. Pattern quantification include describing how urban has changed through time. This leads to make the future predictions regarding urban pattern change, determining whether patterns on two or more landscapes differ from one another. Analyzing urban pattern helps in better urban planning and effective utilization of natural resource and infrastructure facilities. This paper explored how spatial metrics analysis revealed the urban pattern and structure of ten cities of India. Study Area 22 nd -24 th December 2010 Page 1
India is a country in South Asia. It is the seventh-largest country by geographical area, the second-most populous country with over 1.18 billion people. India has the latitude of 22 0 00' and longitude of 77 0 00' W which denote its geographical alignment. The specific latitude of India suggests its position in orthern Hemisphere. The terra firma of India is bounded by the Bay of Bengal, Arabian Sea, Pakistan, Bangladesh, Myanmar, epal, Bhutan and China.Our study was done for major cities of India such as Indian cities: Coastal cities are Mumbai and Chennai. Inland cities are ew Delhi, Bangalore, Hyderabad, Ahmedabad, Coimbatore, Bhopal, Kolkata and Pune (Fig (a). Fig (a) Selected major cities of India. Data Collection Datasets are mainly downloaded from USGS (http://glovis.usgs.gov.), GLCF (http://www.landcover.org/) and Earth explorer websites (http://edcsns17.cr.usgs.gov/earthexplorer/). Methodology Landsat MSS, TM and ETM+ datasets are used in five years series for the respective region (1970, 1980, 1990 and 2000). Georectification was done for the MSS Datasets of all cities. The boundary of the region was selected, and the region is enlarged to the buffer boundary of distance 10 kilometer. This boundary is used to crop the images in IDRISI/GRASS. Supervised classification using maximum likelihood classifier algorithm was carried out with the help of collected training data for all cities. Four land use classes were selected built-up, vegetation, water bodies 22 nd -24 th December 2010 Page 2
and others. Fig (b) shows 2000 s classified images of all ten cities. Accuracy assessment was done by generating the error matrix for each of the images. Table 1 shows the Overall accuracy assessment result for all classified images. Spatial metrics were used to quantify the shapes and urban pattern of the cities in the classified images which was calculated in FRAGSTATS/GRASS. Some metrics were calculated manually. Results and Discussion 1970s 1980s 1990s 2000s Delhi 89 99 97 88 Mumbai 73 98 99 99 Bangalore 93 79 88 99 Hyderabad 81 84 99 76 Kolkata 99 88 93 93 Chennai - 96 89 99 Ahmedabad 77 74 70 68 Coimbatore 97 95 96 92 22 nd -24 th December 2010 Page 3
Bhopal 98 98 96 99 Pune 91 91 91 98 Built-up Vegetation Waterbodies Others. Fig (b): Images of the 10 cities (2000 s)- (Top- left to right) Delhi, Bangalore, Hyderabad, Kolkata, Mumbai (Bottom- left to right) Ahmedabad, Bhopal, Coimbatore, Pune and Chennai. Table 1: Overall accuracy assessment results Spatial metrics Spatial metrics were used to quantify the shapes and urban pattern of the cities. The set of metrics were computed to analyze whether the urban pattern was aggregated, compact or dispersed. Table 2 shows the description of the spatial metrics. Indicator Abbreviation Formula Description umber of patches P P=ni P equals the number of patches of the corresponding patch type. Class Area CA CA equals the sum of the areas (m 2 ) of all patches of the corresponding patch type, divided by 10,000 (to convert to hectares). Mean Shape Index MSI M (Mean) equals the sum, across all patches of the corresponding patch 22 nd -24 th December 2010 Page 4
Pij shape = min P ij type, of the corresponding patch metric values, divided by the number of patches of the same type. M is given in the same units as the corresponding patch metric. Area weighted AWMPFD Where s i and p i are the area and mean patch fractal dimension AWMPFD i= 2ln0.25 p /ln S i i i= 1 i = i= s i= 1 perimeter of patch i, and is the total number of patches Clumpiness Index Clumpy Clumpiness ranges from -1 to 1 where Clumpy = -1 when the focal patch type is maximally disaggregated, Clumpy = 0 when the focal patch is distributed randomly and approaches 1, when patch type is maximally aggregated. Compactness Index CI Where s and p are the area and pi / pi 2 λ s i / λ / p perimeter of the largest patch. i i CI = = 2 2 ormalized LSI LSI is the normalised version of the Landscape Shape landscape index and provides a simple Index measure of class aggregation or clumpiness. Mean Patch Size MPS MPS = A patch (10000) The range in MPS is limited by the grain and extent of the image and the minimum patch size in the same manner as patch area. 22 nd -24 th December 2010 Page 5
Area- weighted Euclidean earest eighbor Distance Distribution E_AM E = hij E_AM equals the distance (m) to the nearest neighboring patch of the same type, based on shortest edge-toedge diatance and where hij= distance from patch ij to nearest neighboring patch of the same type based on patch edge-to-edge distance. Landscape Shape LSI LSI equals the total length of edge in Index the landscape, given in number of cell surfaces, divided by the minimum total length of edge possible, also given in number of cell surfaces, which is achieved when the landscape consists of a single patch. Table 2: Description of the Spatial metrics. In our study, the P was found maximum in the year 1999 in all respective cities, which showed cities were more fragmented and P almost decreased in 2000, reflecting compactness of the cities. The line graph of P for 10 cities fig (a). Class Area (CA) Study shows the increase in the built-up area in all the cities from 1970 to 2000 as in fig (b). Mean Shape Index (MSI) showed decreasing trend from 1970 to 1990 and increased in 2000 for cities like Delhi, Bangalore, Chennai indicates built-up patch getting more irregular shape. The line graph is in fig (d). AWMPFD approaches 1 for shapes with very simple perimeters, such as circle or square, and approaches 2 for shapes with highly convoluted, plane-filling perimeter. The study showed uniformity for all the region as in fig (c). Clumpiness Index in the range of 0 to 1 and approaching towards 1 indicating built-up patch getting maximally aggregated as in fig (e). The compactness index (CI) measures not only the individual patch shape but also the fragmentation of the overall urban landscape (Li and Yeh, 2004). The more regular the patch shape and the smaller the patch number, the bigger the CI value. LSI is the normalized version of the landscape index and provides a simple measure of class aggregation or clumpiness. The fig (g) shows the LSI. It also represents cities like Delhi, Mumbai, Bangalore are getting compact in time. Mean Patch Size shows the small variation in all the regions with respect to time fig (h). Landscape Shape Index (LSI) equals the sum of the landscape boundary and all edge segments within the landscape boundary, divided by the square root of the total landscape area. LSI = 1 when the landscape consists of single square patch and LSI increases without limit as landscape shape becomes more irregular as in fig (i). 22 nd -24 th December 2010 Page 6
umber of patches fig(a) Class Area fig(b) AWMPFD fig (c) Mean Shape Index fig (d) Clumpiness Index (Clumpy) fig(e) Compactness Index (CI) fig(f) 22 nd -24 th December 2010 Page 7
LSI fig (g) MPS fig (h) E_AM fig (i) LSI fig (j) The line graph representation of spatial metrics for 10 cities Conclusion The combined application of remote sensing and spatial metrics supports the analysis of urban pattern and growth and land use change in a variety of different ways. The built-up area increased rapidly in the period 1995 to 2010. Based on the analysis of the metrics, it is reasonable to argue that there has been a relative change in the fragmentation and spatial patterns or structures of the built up features (Yikalo H. Araya). This indicates the increase in the class area and number of patches in each of the cities. Urban pattern was more dispersed and fragmented because of less planning strategies. 22 nd -24 th December 2010 Page 8
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