Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia

2019 ◽  
Vol 6 (1) ◽  
pp. 383-395 ◽  
Author(s):  
Fajar Yulianto ◽  
Hana Listi Fitriana ◽  
Kusumaning Ayu Diah Sukowati
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
River Basin ◽  
Shannon’S Entropy ◽  
West Java ◽  
Shannon's Entropy

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 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 Analysis using GIS Application of Bhagur City

2021 ◽  
Vol 9 (9) ◽  
pp. 861-868
Author(s):  
Keyur Rai
Keyword(s):  
Remote Sensing ◽  
Urban Sprawl ◽  
Urban Growth ◽  
Growth Pattern ◽  
Agricultural Land ◽  
Urban Housing ◽  
Shannon’S Entropy ◽  
Shannon's Entropy ◽  
Landcover Change ◽  
The City

Abstract: The word “Urban Sprawl” means growth is more than the normal and the criteria that makes it different from urban growth is this excessive nature. Cities grow continuously and planned growth is achieved when there is a right balance between urban growth and urbanization. But when growth is above normal its pressure on the region and the city will face major new challenges. Urban sprawl is unrestricted growth in many urban housing areas, business development and roads in large parts of the world, without worrying about urban planning. Urban Sprawl are of three types i.e., linear growth, cluster growth and leapfrog growth. This paper inspect the use of Remote Sensing and GIS in mapping of urban sprawl (1990-2021) and landuse/ landcover change detection to detect changes that has been taken place between these periods in Bhagur city. The paper helps to study the software such as ArcGIS, used to classify between built up and agricultural land using temporal signatures obtained from satellite images. To numerically understand the growth pattern Shannon’s entropy is used. Shannon’s entropy is used as an index to quantify the degree of dispersion or concentration of built-up areas. Entropy approach shows concentration growth pattern in Bhagur city. Keywords: Urban Sprawl, GIS, Remote sensing, Land use/ Land cover, Shannon’s entropy.

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