Relative Shannon’s Entropy Approach for Quantifying Urban Growth Using Remote Sensing and GIS: A Case Study of Cuttack City, Odisha, India

Patra, P. K.; Behera, Duryadhan; Goswami, Shreerup

doi:10.1007/s12524-022-01493-z

Cited by 18 publications

(17 citation statements)

References 31 publications

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“…al. et al et al et al Dinesh Kumar , 2007;Jasrotia , 2007;Sreedhar , 2009;Chowdhury , 2009;Mohammed-Aslam and Balasubramanian, 2010;Agarwal , 2013;Abdalla, 2012;Adhikary 2021;Patra , 2021;Mahanta , 2020). The present investigation is an attempt to explore the possibility and prospect of groundwater in the Kantli River Basin.…”

Section: Introductionmentioning

confidence: 84%

Delineation of the Groundwater Potential Zone in Kantli River Basin, Jhunjhunu District, Rajasthan: A Geospatial Approach

Chatterjee¹,

Behera²,

Patra³

et al. 2023

JGSR

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Remote sensing and GIS are advanced techniques and tools that have been used for various researches including groundwater geology. The Landsat and the IRS satellite datasets have been utilized to extract information on the hydrogeomorphic and groundwater related features of a semi-arid Precambrian hard rock terrain in the Kantli River Basin of Jhunjhunu District, Rajasthan, India. It is the upper part of the Shekhawati River. The study area is covering about 2313.2013 km2 in the Jhunjhunu District, Rajasthan. In the present study, the groundwater potential zone is identified by the GIS overlay techniques using the spatial analyst tool in ArcGIS 10.2. The developed methodology is demonstrated in the Kantli River Basin of Rajasthan, western India. Originally, nine thematic layers, viz. topographic elevation, land slope, geomorphology, geology, soil, pre and post-monsoon groundwater depths, annual net recharge, annual rainfall and proximity to surface water bodies were considered in this study. Therefore, five groundwater potential zones were identified and distinguished in the study area, viz. 'very good', 'good', 'moderate', 'poor' and 'very poor' based on groundwater potential index values, which will enable the local bodies for the future planning and management of the groundwater resource. Severe groundwater contamination has been found occasionally in the study area. Every year during the summer, the region is facing a lot of problems with portable groundwater. As the study area is semi-desert, the influences of salinity have been increasing day by day in the groundwater. Keywords: Groundwater, Remote Sensing, Kantli River, Satellite Images, GIS, Potential zone

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Section: Introductionmentioning

confidence: 84%

Delineation of the Groundwater Potential Zone in Kantli River Basin, Jhunjhunu District, Rajasthan: A Geospatial Approach

Chatterjee¹,

Behera²,

Patra³

et al. 2023

JGSR

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show abstract

“…This model is a preferable option for comparing two or more periods of urban expansion because it represents a specific land‐use class's geographical concentration or dispersion (Rahman et al, 2011). The property of Shannon's entropy model is not reflected by the number of divisions or the way by which the study area is classified (Patra et al, 2022; Taiwo, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Various urban growth analysis models are available. The Shannon entropy model is one of the most commonly used models for evaluating urban growth and sprawl in remote sensing and GIS (Chettry & Surawar, 2020; Nero, 2017; Patra et al, 2022). The entropy model is helpful to understand the growing trend of any city.…”

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model

2023

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Silchar City and its surrounding areas have witnessed massive population growth in the last few decades due to local and migrated peoples, which led to the rapid transformation of its land use/land cover (LU/LC) pattern. The spatio‐temporal very high‐resolution satellite data (VHRS) and on‐screen visual interpretation techniques were used for detailed large‐scale LU/LC analysis. The LU/LC classification of the City has been prepared for 2005, 2011, and 2018 with five primary and a further 17 sub‐classes. In the present study use was made of the Census of India reports from 1991 to 2011, and the geometrical increase method was used to evaluate the ward‐level population, population density, and land requirements for residential purposes; these have been predicted from 2021 to 2051 with every 10 years interval. The ity's wards have been classified into five zones, and the Shannon entropy model has been applied to analyze the degree of randomness and trends of urban built‐up growth. The obtained results indicate that Silchar City is compact near the center, which follows the infill built‐up growth trend. However, as the distance increased from its center, the scattering with an infill growth pattern was observed. The accuracy assessment techniques are used to validate the results of the classification. The results of this study can provide detailed information for land‐use planners, researchers, policy‐decision makers, and municipal authorities.

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“…To identify the urban sprawl pattern of the study region, normalized Shannon's entropy method is implemented making use of the urban maps of 2002, 2013 and 2020 [36]. The study region is divided into five distance-based zones (0 -5 km, 5 -25 km, 25 -50 km, 50 -75 km, and 75 -100 km) based on the administrative center.…”

Section: Urban Growth Analysis Through Shannon's Entropy Analysismentioning

confidence: 99%

Spatio-Temporal Land Cover Analysis and the Impact of Land Cover Variability Indices on Land Surface Temperature in Greater Accra, Ghana Using Multi-Temporal Landsat Data

Devendran¹,

Banon²

2022

JGIS

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Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region. Shannon's entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.

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Relative Shannon’s Entropy Approach for Quantifying Urban Growth Using Remote Sensing and GIS: A Case Study of Cuttack City, Odisha, India

Cited by 18 publications

References 31 publications

Delineation of the Groundwater Potential Zone in Kantli River Basin, Jhunjhunu District, Rajasthan: A Geospatial Approach

Delineation of the Groundwater Potential Zone in Kantli River Basin, Jhunjhunu District, Rajasthan: A Geospatial Approach

Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model

Spatio-Temporal Land Cover Analysis and the Impact of Land Cover Variability Indices on Land Surface Temperature in Greater Accra, Ghana Using Multi-Temporal Landsat Data

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