Spatio-temporal changes and driving mechanisms of vegetation in Yunnan Province based on MODIS-KNDVI in recent 20 years DOI

Shao Xin,

Xue Ding,

Jinliang Wang

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Abstract Vegetation cover serves as a pivotal indicator for evaluating key ecosystem attributes, signifi-cantly elucidating the intricate dynamics between global climate shifts and equilibrium. The employment of remote sensing extensive, high-fidelity vegetation surveillance is critical in appraising regional environmental transformations devising targeted conservation strategies. Implementing Kernel Normalized Difference Index (KNVDI) enhances precision change detection. Leveraging Google Earth Engine (GEE) data analysis, this investigation harnesses MODIS imagery spanning 2000 to 2020 construct KNVDI meticulous observation altera-tions Yunnan Province, China. Employing GIS methodologies, including Theil-Sen trend Mann-Kendall tests, centroid shift models, study intricately examines temporal spatial evolution over two decades. Incorporating Hurst index projections future trends utilizing an optimized geographic detector model, it probes into underlying drivers modifications region. Findings indicate:(1) pronounced increase from 2020, with growth rate 0.028 per decade average value 0.3304, showcasing west-high, east-low distribution. (2)Areas vege-tation substantially outweigh those decrease, predominantly located northeast southwest, contrasted sporadic reductions central northwest near significant inland lakes. H suggests reversal compared past. (3)Precipitation aridity emerge primary influencers on KNVDI, significantly affecting dynamics, their interactions demonstrating en-hanced nonlinear influence, particularly precipitation aridity/elevation. These insights offer valuable implications sustainable development strategic planning Province.

Язык: Английский

The current situation and trend of land ecological security evaluation from the perspective of global change DOI Creative Commons

Lijiao Li,

Meichen Fu,

Youxiang Zhu

и другие.

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112608 - 112608

Опубликована: Сен. 13, 2024

Язык: Английский

Процитировано

7
Research on Rural Environments’ Effects on Well-Being: The Huizhou Area in China DOI Creative Commons

Xingmeng Ma,

Xin Su, Yanlong Guo

и другие.

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(6), С. 189 - 189

Опубликована: Июнь 6, 2024

The Huizhou region is an important area of traditional Chinese culture, and currently, the state village’s surroundings in this still not perfect. In study, seven districts (counties) were selected for research. Rural Habitat Environment (RHES) Indicator Program based on concept Socio-Economic-Natural Complex Ecosystems (SENCE) constructs 18 metrics three dimensions. Trends influencing factors analyzed using entropy weight TOPSIS a Grey Relational Analysis (GRA) years 2013–2022, spatial temporal evolution was measured Geographic Information Systems (GISs). findings show that composite index grew from 2013 (0.3197) to 2022 (0.6806). Second, Tunxi District belongs high index–high economy category. Shexian, Xiuning, Qimen counties belong index–low Huangshan low Yixian County Third, all upward trend, has best RHES condition. Shexian ranks relatively comprehensive index.

Язык: Английский

Процитировано

1
Spatial and temporal evolution of forestry ecological security level in China DOI

Lu Wu,

Wei Fu,

Yuexiang Hu

и другие.

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Авг. 7, 2024

Язык: Английский

Процитировано

1
Spatio-temporal changes and driving mechanisms of vegetation in Yunnan Province based on MODIS-KNDVI in recent 20 years DOI

Shao Xin,

Xue Ding,

Jinliang Wang

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Abstract Vegetation cover serves as a pivotal indicator for evaluating key ecosystem attributes, signifi-cantly elucidating the intricate dynamics between global climate shifts and equilibrium. The employment of remote sensing extensive, high-fidelity vegetation surveillance is critical in appraising regional environmental transformations devising targeted conservation strategies. Implementing Kernel Normalized Difference Index (KNVDI) enhances precision change detection. Leveraging Google Earth Engine (GEE) data analysis, this investigation harnesses MODIS imagery spanning 2000 to 2020 construct KNVDI meticulous observation altera-tions Yunnan Province, China. Employing GIS methodologies, including Theil-Sen trend Mann-Kendall tests, centroid shift models, study intricately examines temporal spatial evolution over two decades. Incorporating Hurst index projections future trends utilizing an optimized geographic detector model, it probes into underlying drivers modifications region. Findings indicate:(1) pronounced increase from 2020, with growth rate 0.028 per decade average value 0.3304, showcasing west-high, east-low distribution. (2)Areas vege-tation substantially outweigh those decrease, predominantly located northeast southwest, contrasted sporadic reductions central northwest near significant inland lakes. H suggests reversal compared past. (3)Precipitation aridity emerge primary influencers on KNVDI, significantly affecting dynamics, their interactions demonstrating en-hanced nonlinear influence, particularly precipitation aridity/elevation. These insights offer valuable implications sustainable development strategic planning Province.

Язык: Английский

Процитировано

0