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Spatio-Temporal Separating Analysis of NDVI Evolution and Driving Factors: A Case Study in Nanchang, China
Investigating vegetation coverage and quantifying environmental changes offer critical insights for ecological protection, resource management, and policymaking. This study explores the spatial and temporal separation of evolutionary characteristics and driving factors of the NDVI in Nanchang City f...
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Published in: | Sustainability 2024-12, Vol.16 (23), p.10494 |
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description | Investigating vegetation coverage and quantifying environmental changes offer critical insights for ecological protection, resource management, and policymaking. This study explores the spatial and temporal separation of evolutionary characteristics and driving factors of the NDVI in Nanchang City from 2000 to 2022, using methods such as the Hurst Exponent, the ReliefF feature selection algorithm, and geographical detectors. The results show the following observations: (1) Temporal analysis: the average NDVI in Nanchang City was 0.453, showing an overall upward trend, although the rate of increase gradually slowed over time. (2) Spatial analysis: vegetation in Nanchang City exhibited a pattern of sustained reverse development, with notable trends of “improvement around rivers and lakes” and “large-scale degradation of urban land”. (3) Feature selection: among the three algorithms tested, ReliefF proved most effective in analyzing temporal drivers of NDVI changes. Human factors were identified as the dominant drivers of NDVI variation, while meteorological factors were less significant. (4) Geographical driver analysis: The geographical detectors revealed that population density, nighttime lights, and land cover types were the primary drivers of vegetation change. Regions with a negative correlation between NDVI and human factors are mainly centered in the central area of Nanchang City and Jinxian County, whereas positive correlations were observed around rivers and lakes. This study delves into the changing patterns of vegetation cover in Nanchang City, offering scientific insights to guide the protection and management of the regional ecological environment, thereby promoting sustainable development. |
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This study explores the spatial and temporal separation of evolutionary characteristics and driving factors of the NDVI in Nanchang City from 2000 to 2022, using methods such as the Hurst Exponent, the ReliefF feature selection algorithm, and geographical detectors. The results show the following observations: (1) Temporal analysis: the average NDVI in Nanchang City was 0.453, showing an overall upward trend, although the rate of increase gradually slowed over time. (2) Spatial analysis: vegetation in Nanchang City exhibited a pattern of sustained reverse development, with notable trends of “improvement around rivers and lakes” and “large-scale degradation of urban land”. (3) Feature selection: among the three algorithms tested, ReliefF proved most effective in analyzing temporal drivers of NDVI changes. Human factors were identified as the dominant drivers of NDVI variation, while meteorological factors were less significant. (4) Geographical driver analysis: The geographical detectors revealed that population density, nighttime lights, and land cover types were the primary drivers of vegetation change. Regions with a negative correlation between NDVI and human factors are mainly centered in the central area of Nanchang City and Jinxian County, whereas positive correlations were observed around rivers and lakes. This study delves into the changing patterns of vegetation cover in Nanchang City, offering scientific insights to guide the protection and management of the regional ecological environment, thereby promoting sustainable development.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su162310494</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Climate change ; Comparative analysis ; Computer centers ; Correlation analysis ; Distribution ; Environmental aspects ; Feature selection ; GDP ; Geography ; Gross Domestic Product ; Machine learning ; Measurement ; Optical properties ; Plant communities ; Precipitation ; Rain ; Rivers ; Sensors ; Time series ; Topography ; Trends ; Vegetation</subject><ispartof>Sustainability, 2024-12, Vol.16 (23), p.10494</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><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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(4) Geographical driver analysis: The geographical detectors revealed that population density, nighttime lights, and land cover types were the primary drivers of vegetation change. Regions with a negative correlation between NDVI and human factors are mainly centered in the central area of Nanchang City and Jinxian County, whereas positive correlations were observed around rivers and lakes. This study delves into the changing patterns of vegetation cover in Nanchang City, offering scientific insights to guide the protection and management of the regional ecological environment, thereby promoting sustainable development.</description><subject>Algorithms</subject><subject>Climate change</subject><subject>Comparative analysis</subject><subject>Computer centers</subject><subject>Correlation analysis</subject><subject>Distribution</subject><subject>Environmental aspects</subject><subject>Feature selection</subject><subject>GDP</subject><subject>Geography</subject><subject>Gross Domestic Product</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>Optical properties</subject><subject>Plant communities</subject><subject>Precipitation</subject><subject>Rain</subject><subject>Rivers</subject><subject>Sensors</subject><subject>Time series</subject><subject>Topography</subject><subject>Trends</subject><subject>Vegetation</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNpVkU9LxDAQxYsoKOrJLxDwJFpNmrRpvC27_lkQBVe9lmk6XbN0k5q04n57I-tBZw4zPH5v4DFJcsLoJeeKXoWRFRlnVCixkxxkVLKU0Zzu_tn3k-MQVjQW50yx4iBZLXoYjEtfcN07Dx1ZYA8-SnZJJha6TTCBuJY8zt7m5ObTdWOkLQHbkJk3nz_YLejB-XBNJmQKAcliGJsNMZY8gtXvYJcXZPpuLBwley10AY9_52HyenvzMr1PH57u5tPJQ6qzXA1pXgqUoq4BUcuclbrJoMmKWmiqS1lwaJUsKMSgusASZamLXNa6AKzrGoXmh8np9m7v3ceIYahWbvQxS6g4E4JJJRSN1OWWWkKHlbGtGzzo2A2ujXYWWxP1SZlRylSuZDSc_TNEZsCvYQljCNV88fyfPd-y2rsQPLZV780a_KZitPp5VvXnWfwbky2FQw</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Li, Jiatong</creator><creator>Wu, Hua</creator><creator>Zhu, Jiaqi</creator><creator>Xu, Yue</creator><creator>Guo, Qiyun</creator><creator>Li, Huishan</creator><creator>Xie, Xue</creator><creator>Liu, Sihang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0009-0000-0198-2251</orcidid></search><sort><creationdate>20241201</creationdate><title>Spatio-Temporal Separating Analysis of NDVI Evolution and Driving Factors: A Case Study in Nanchang, China</title><author>Li, Jiatong ; Wu, Hua ; Zhu, Jiaqi ; Xu, Yue ; Guo, Qiyun ; Li, Huishan ; Xie, Xue ; Liu, Sihang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c259t-584e74bbaeec7518cd2ad26b4c0c8763af9760a310c6e8e78c657bc6aebbbe4c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Climate change</topic><topic>Comparative analysis</topic><topic>Computer centers</topic><topic>Correlation analysis</topic><topic>Distribution</topic><topic>Environmental aspects</topic><topic>Feature selection</topic><topic>GDP</topic><topic>Geography</topic><topic>Gross Domestic Product</topic><topic>Machine learning</topic><topic>Measurement</topic><topic>Optical properties</topic><topic>Plant communities</topic><topic>Precipitation</topic><topic>Rain</topic><topic>Rivers</topic><topic>Sensors</topic><topic>Time series</topic><topic>Topography</topic><topic>Trends</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Jiatong</creatorcontrib><creatorcontrib>Wu, Hua</creatorcontrib><creatorcontrib>Zhu, Jiaqi</creatorcontrib><creatorcontrib>Xu, Yue</creatorcontrib><creatorcontrib>Guo, Qiyun</creatorcontrib><creatorcontrib>Li, Huishan</creatorcontrib><creatorcontrib>Xie, Xue</creatorcontrib><creatorcontrib>Liu, Sihang</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Jiatong</au><au>Wu, Hua</au><au>Zhu, Jiaqi</au><au>Xu, Yue</au><au>Guo, Qiyun</au><au>Li, Huishan</au><au>Xie, Xue</au><au>Liu, Sihang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatio-Temporal Separating Analysis of NDVI Evolution and Driving Factors: A Case Study in Nanchang, China</atitle><jtitle>Sustainability</jtitle><date>2024-12-01</date><risdate>2024</risdate><volume>16</volume><issue>23</issue><spage>10494</spage><pages>10494-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Investigating vegetation coverage and quantifying environmental changes offer critical insights for ecological protection, resource management, and policymaking. 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subjects | Algorithms Climate change Comparative analysis Computer centers Correlation analysis Distribution Environmental aspects Feature selection GDP Geography Gross Domestic Product Machine learning Measurement Optical properties Plant communities Precipitation Rain Rivers Sensors Time series Topography Trends Vegetation |
title | Spatio-Temporal Separating Analysis of NDVI Evolution and Driving Factors: A Case Study in Nanchang, China |
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