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Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China
Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environment...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2024-07, Vol.16 (13), p.2380 |
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description | Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection. |
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The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs16132380</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>China ; Climate change ; Coefficient of variation ; Datasets ; ecological environment quality ; Environmental monitoring ; Environmental protection ; Environmental quality ; Google Earth Engine (GEE) ; Heat ; human activity ; Humidity ; Hunan Province ; Industrial development ; Landsat ; Landsat satellites ; Mountain regions ; Mountainous areas ; Mountains ; Precipitation ; Remote sensing ; Spatiotemporal data ; Urban areas ; Urban sprawl</subject><ispartof>Remote sensing (Basel, Switzerland), 2024-07, Vol.16 (13), p.2380</ispartof><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.</description><subject>China</subject><subject>Climate change</subject><subject>Coefficient of variation</subject><subject>Datasets</subject><subject>ecological environment quality</subject><subject>Environmental monitoring</subject><subject>Environmental protection</subject><subject>Environmental quality</subject><subject>Google Earth Engine (GEE)</subject><subject>Heat</subject><subject>human activity</subject><subject>Humidity</subject><subject>Hunan Province</subject><subject>Industrial development</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Mountain regions</subject><subject>Mountainous areas</subject><subject>Mountains</subject><subject>Precipitation</subject><subject>Remote sensing</subject><subject>Spatiotemporal data</subject><subject>Urban areas</subject><subject>Urban sprawl</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkd1q3DAQhU1oISHJTZ9A0LvSTfRjy1bvgtl2A4Gm2fRajOVxqsUruZK8sO_Qh46yG5rMzRwOh28OTFF8YvRKCEWvQ2SSCS4aelKccVrzRckV__BOnxaXMW5oHiGYouVZ8W-58-OcrHcEXE_WE2SdcDv5ACN5wDh5F5H4gSyNH_2TNdleup0N3m3RJfJrhtGmPUmerOYtOHJjkt3ZZDEeiO1ot5DwG2khc9Zp7vcvtNXscvY--J11Br-S9o91cFF8HGCMePm6z4vf35eP7Wpx9_PHbXtztzCclWnBJQ6llNjjIGhdcdE1XVexXtZIKQrVm7qqlDB1qWQ3NMBlBbzpWFUZ1XGlxHlxe-T2HjZ6Crlh2GsPVh8MH540hGTNiNqAYYJKA2jKkg60GaCraK86ptD0tM6sz0fWFPzfGWPSGz8Hl-vrXE5xKWXT5NSXY8oEH2PA4f9VRvXL8_Tb88QzL9mNSw</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Hui, Jiawei</creator><creator>Cheng, Yongsheng</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>DOA</scope></search><sort><creationdate>20240701</creationdate><title>Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China</title><author>Hui, Jiawei ; 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The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs16132380</doi><oa>free_for_read</oa></addata></record> |
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subjects | China Climate change Coefficient of variation Datasets ecological environment quality Environmental monitoring Environmental protection Environmental quality Google Earth Engine (GEE) Heat human activity Humidity Hunan Province Industrial development Landsat Landsat satellites Mountain regions Mountainous areas Mountains Precipitation Remote sensing Spatiotemporal data Urban areas Urban sprawl |
title | Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China |
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