scholarly journals MANAGING URBAN SPRAWL USING REMOTE SENSING AND GIS

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 59-66
Author(s):  
K. S. Krishnaveni ◽  
P. P. Anilkumar
Keyword(s):  
Remote Sensing ◽  
Urban Sprawl ◽  
Vegetation Index ◽  
City Planning ◽  
Integrated Approach ◽  
Developed Countries ◽  
Remote Sensing And Gis ◽  
Shannon’S Entropy ◽  
Shannon's Entropy ◽  
Gis Techniques

Abstract. Indian cities, like several other developing cities around the world, are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanization may result in urban sprawl, which is characterized by low-density impervious surfaces, often clumsy, extends along the fringes of metropolitan areas with unbelievable pace, disperse, auto-dependent with environmentally and socially impacting characteristics. The ill-effects of urban sprawl in developing countries scenario is a bit complicated compared to that of developed countries because of uncontrolled population growth and haphazard urbanization. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in understanding the urban sprawl phenomenon in a better way compared to time- consuming conventional methods. An overview of the enormous potential of remote sensing and GIS techniques in mapping and monitoring the Spatio-temporal patterns urban sprawl is dealt with here. The spatial pattern and dynamics of the urban sprawl of Kozhikode Metropolitan Area (KMA, Kerala, India) during the period from 1991 to 2018 using the integrated approach of remote sensing and GIS are attempted here. Index derived Built-up Index (IDBI) which is a thematic index-based index (combination of Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index (SAVI)) is used for the rapid and automated extraction of built-up features from the time series satellite imageries. The extracted built-up areas of each year are then used for Shannon’s entropy calculations, which is a method for the quantification of urban sprawl. The results of IDBI and Shannon’s entropy analysis highlight the fact that there occurs an alarming increase in the built-up areal extent from 1991 to 2018. The urban planning authorities can make use of these techniques of built-up area extraction and urban sprawl analysis for effective city planning and sprawl control.

scholarly journals Urban Sprawl Patterns and Processes in Delhi from 1977 to 2014 Based on Remote Sensing and Spatial Metrics Approaches

2016 ◽  
Vol 20 (14) ◽  
pp. 1-29 ◽  
Author(s):  
Madhavi Jain ◽  
A. P. Dimri ◽  
D. Niyogi
Keyword(s):  
Remote Sensing ◽  
Urban Sprawl ◽  
Shannon’S Entropy ◽  
Weighted Mean ◽  
Spatial Metrics ◽  
Shannon's Entropy ◽  
Urban Patterns ◽  
Patterns And Processes ◽  
The City ◽  
Compactness Index

Abstract Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson’s chi statistics and Shannon’s entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts—Central and New Delhi. Relative entropy, which scales Shannon’s entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density.

scholarly journals Assessing urban sprawl by remote sensing and GIS techniques

2021 ◽  
Vol 7 (3) ◽  
pp. 144-156
Author(s):  
Mohamed El-Sayed Embaby
Keyword(s):  
Remote Sensing ◽  
Urban Sprawl ◽  
Bare Soil ◽  
Remote Sensing And Gis ◽  
Sustainable Urban Development ◽  
Land Loss ◽  
Gis Techniques ◽  
Agriculture Land ◽  
Assessment Approach ◽  
The Government

In the context of mega sustainable urban development projects, this research was commenced with the impartial of assessing urban sprawl (i.e., land use versus land cover variations) during 2000-2016 by remote sensing and GIS techniques, where GCR “Greater Cairo Region” was taken as a case study. Primarily, literature in the field of remote sensing and GIS technologies were assembled and scrutinized. In addition, data regarding build zones, agricultural areas and bare soil regions so as water were assembled from different resources (i.e., Ministry of Agriculture). Remote sensing and GIS techniques were implemented. Results were obtained and analyzed. Finally, conclusions were deduced and recommendations were suggested. The research flagged-out that the GCR designated a drastic agriculture land loss of 10%. In addition, the research confirmed that built-up areas increased by 10% during 2000-2016. On the other hand, the results indicated that Giza and Qalyubia Governorates recorded the largest agriculture land loss by 8.72 and 7.88%, respectively, which is equivalent to 47 and 77 km2, respectively. The research prioritized the importance of defining the most affected zones within Giza and Qalyubia Governorates by correlating GIS data versus governmental files, where a difference of 50% was designated. The research portrayed the priority of modifying the government assessment approach in order to control urban sprawl.

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