Assessment of urban sprawl using landscape metrics and Shannon’s entropy model approach in town level of Barrackpore sub-divisional region, India

Das, Sandipta; Angadi, Dasharatha P.

doi:10.1007/s40808-020-00990-9

Cited by 55 publications

(15 citation statements)

References 94 publications

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“…Notable exceptions are Effat and El Shobaky's (2015) entropy map-ping of city-district sprawl, and Rahman's (2016aRahman's ( , 2016b municipal level, and Das and Angadi (2020) sub-regional level. Das and Angadi (2020) use the spatial landscape metrics approach and apply Shannon's entropy model as a sprawl indicator to remotely sensed data at the aggregate scale (towns). Specifically, urban growth was examined by the zoning of each town.…”

Section: A Brief Review Of Methodsmentioning

confidence: 99%

“…Liu et al (2018) used a simplified model to identify the expanding area reflecting urban sprawl patterns as they computed local spatial entropy based on the estimated 2015 population density with a 1 km x 1 km spatial resolution and applied K-means clustering. Our approach is similar to studies of urban sprawl at municipal level with land use entropy, like Das and Angadi's (2020), but different from existing studies that do not examine the morphology of urban sprawl at a finer-scale of the block group within the city-region or explore the relation of sprawl to blight. Above all else, we stress an entropy analysis of land use pattern at an even a finer-scale of the city-region that is commensurate with neighborhoods that uniquely define the city.…”

Section: A Brief Review Of Methodsmentioning

confidence: 99%

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Mapping the morphology of sprawl and blight: A note on entropy

2021

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The urban expansion from the city center to the suburb and beyond is indicated by Shannon entropy, a robust and versatile measure of sprawl. However, the metropolitan regionwide entropy masks the morphology of land cover and land use consequential to urban expansion within the city-region. To surmount the limitation, we focus on the block-group, which is a US census defined socio-spatial unit that identifies the metropolitan region’s development pattern structurally, forming tracts that comprise neighborhoods. The concentration and dispersion of land use and land cover by block-group reveals a North American metropolitan region’s commonly known but rarely measured spatial structure of its urban and suburban sprawl. We use parcel data from county assessor of property (GIS) and land cover pixel data from the National Land Cover Data (NLCD) to compute block-group land-use and land-cover entropy. The change in block group entropy over a decade indicates whether the city- region’s land use and land cover transition to a concentrated or dispersed pattern. Furthermore, we test a hypothesis that blight correlates with sprawl. Blight and sprawl are among the key factors that plague the metropolitan region. We determine the correlations with household income as well as (block group) distance from the city center. It turns out, blight is among the universally held distance-decay phenomena. The share of the block group’s blighted properties decays (nonlinearly) with distance from the city center. Highlights for public administration, management and planning: • The metropolitan region’s outward growth is highlighted by mapping the changing morphology of the block group within the city-region. • The block group entropy is computed with land use (parcel) and land cover (pixel) data. • The block group entropy change indicates the pattern of the land use and land cover transition with concentration or dispersion. • We test the hypothesis that blight correlates with sprawl with statistical models. • The block group’s blighted properties decrease (nonlinearly) with distance from the city center.

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Section: A Brief Review Of Methodsmentioning

confidence: 99%

Section: A Brief Review Of Methodsmentioning

confidence: 99%

Mapping the morphology of sprawl and blight: A note on entropy

2021

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“…The Maximun Likelihood Classification (MLC) technique was used for this purpose. The MLC is a reliable and probability-based classication technique that has been widely applied to multi-temporal classification (Das and Angadi, 2020). The three reflectance bands most useful in distinguishing vegetation ecosystems (band 3: red, 0.63-0.69 µm; band 4: near infrared, 0.76-0.90 µm and band 5: mid infrared, 1.55-1.75 µm) were used to process the images.…”

Section: Pre-processing Of Data and Classificationmentioning

confidence: 99%

Mapping spatio-temporal changes in mangroves cover and projection in 2050 of their future state in Benin

et al. 2022

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Mangroves are precious ecosystems that provide vital socio-economic, environmental and cultural benefits to humanity. However, they are declining alarmingly due to human activities and natural hazards. Assessment of their spatio-temporal dynamics is essential to monitor these ecosystems and guide their management to ensure sustainability. We assessed the spatio-temporal dynamics of mangroves and predicted their future trends using remote sensing techniques and Markovian chain analysis. Landsat images TM/ETM+ (for 1988, 2001 and 2019) were obtained, processed, classified and analyzed using remote sensing techniques and GIS. The changes observed during these periods (1988–2001, 2001-2019 and 1988–2019) were used to predict future trends up to 2050, using Markovian chain analysis. The results showed that the mangrove area studied, which occupied 5205.24 ha in 1988, declined by 62.07% between 1988 and 2001 but increased by 18.84% from 2001 to 2019. This increase is attributed to strengthened mangrove restoration efforts. The mangroves had mainly been converted into grassland (52.35% in 1988-2001 and 7.31% in 2001-2019) and other vegetation types (17.57% in 1988-2001 and 27.05% in 2001-2019). Their decline was most severe in the municipalities of Abomey-Calavi and Ouidah, which therefore require greater conservation efforts. Our projection based on Markovian chain analysis suggests that these mangroves will continue to decline, but slowly. This study provides essential information to guide future mangrove conservation action in the study area.

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“…Suitability analysis refers to the analysis of land for a specific development activity, and the regional division of an appropriate grade is proposed through the comprehensive consideration and evaluation analysis of multiple influencing factors [4][5]. With the emphasis on systematization and scientificity in urban land planning, the organic combination of multiple disciplines shows a broad development prospect [6][7]. Using the Water Evaluation And Planning System (WEAP) and MATLAB can help us to try and select the best measures to mitigate Water stress at the watershed level [8].…”

Section: Introductionmentioning

confidence: 99%