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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Veröffentlicht in:	Urban ecosystems Jg. 25; H. 4; S. 1137 - 1154
Hauptverfasser:	Maity, Sukamal, Das, Subhasis, Pattanayak, Jhumarani Maity, Bera, Biswajit, Shit, Pravat Kumar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2022 Springer Nature B.V
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Bera, Biswajit Shit, Pravat Kumar Das, Subhasis Maity, Sukamal Pattanayak, Jhumarani Maity
Author_xml	– sequence: 1 givenname: Sukamal surname: Maity fullname: Maity, Sukamal organization: Department of Geography, Narajole Raj College – sequence: 2 givenname: Subhasis surname: Das fullname: Das, Subhasis organization: Department of Geography, Narajole Raj College, PG Department of Geography, Raja N.L.Khan Womens College (Autonomous) – sequence: 3 givenname: Jhumarani Maity surname: Pattanayak fullname: Pattanayak, Jhumarani Maity organization: Department of Geography – sequence: 4 givenname: Biswajit surname: Bera fullname: Bera, Biswajit organization: Department of Geography, Sidho Kanho Birsha University – sequence: 5 givenname: Pravat Kumar orcidid: 0000-0001-5834-0495 surname: Shit fullname: Shit, Pravat Kumar email: pravatgeo2007@gmail.com organization: PG Department of Geography, Raja N.L.Khan Womens College (Autonomous)
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