Assessment of ecological environment quality in Kolkata urban agglomeration, India

The global ecosystem has been significantly disrupted on various spatiotemporal scales over the last three decades due to human activities. Geospatial technology can quickly, effectively, and quantitatively to evaluate the spatiotemporal change of eco-environmental quality (EEQ). The present study i...

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Bibliographic Details
Published in:Urban ecosystems Vol. 25; no. 4; pp. 1137 - 1154
Main Authors: Maity, Sukamal, Das, Subhasis, Pattanayak, Jhumarani Maity, Bera, Biswajit, Shit, Pravat Kumar
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2022
Springer Nature B.V
Subjects:
Agglomeration
autocorrelation
Biomedical and Life Sciences
Clustering
Decision making
Ecology
Ecosystems
Environment
Environmental assessment
Environmental conditions
environmental factors
Environmental Management
Environmental quality
Evaluation
heat
Human influences
humans
Imagery
India
Indicators
issues and policy
Landsat
Life Sciences
Nature Conservation
Policy making
principal component analysis
Principal components analysis
Remote sensing
Satellite imagery
Spatial analysis
Urban areas
Urban development
Urban Ecology
Urban growth
Urban sprawl
urbanization
Values
India
Kolkata India
Kolkata
Ecological index
Spatial autocorrelation
Remote sensing
Principal component analysis
ISSN:1083-8155, 1573-1642
Online Access:Get full text
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Summary:The global ecosystem has been significantly disrupted on various spatiotemporal scales over the last three decades due to human activities. Geospatial technology can quickly, effectively, and quantitatively to evaluate the spatiotemporal change of eco-environmental quality (EEQ). The present study is focused on novel approach of Remote Sensing based Ecological Index (RSEI), using Landsat Imagery data to assess environmental conditions and changes pattern. Four ecological indicators were prepared in the year 1990, 2000, 2010 and 2020 of Kolkata urban agglomeration (KUA) to evaluate the ecological environmental condition. The principal component analysis (PCA) and spatial autocorrelation analysis can relate all indicators with each other’s and RSEI. Out study indicated, greenness and wetness have a positive effect on EEQ of the province, but both dryness and heat have a negative effect. However, it should be noted that greenness has a greater impact on the eco-environment than the other three indicators. Based on the RSEI values, we have categorized the environmental standards of the study area into four groups—very good (0.81−1.00), good (0.61−0.80), acceptable (0.41−0.60), poor (0.21−0.40), and very poor (0.00−0.20), where high values ​​indicate that environmental quality is stable and healthy for living organisms and low values ​​indicate relatively unstable and threatening conditions of the environment. The status of RSEI showed that 9.02%, 12.29%, 12.79% and 37.23% of an area was under poor to very poor condition in the year of 1990, 2000, 2010 and 2020 respectively. Good to very good condition of RSEI values was increased from 19.12% to 34.074% during 1990 to 2010, but declined of RSEI value 9.47% during 2010 to 2020 due to urban expansion. Here, Moran's I values fund that 0.265, 0.543, 0.396 and 0.367 in the year 1990, 2000, 2010 and 2020 respectively. The result of Moran’s I values indicate that clustering nature. The present study can helpful for the decision making of ecological management guided by planners and policy makers.
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ISSN:1083-8155
1573-1642
DOI:10.1007/s11252-022-01220-z