Assessing the spatiotemporal characteristics and driving factors of habitat quality in sustainable development demonstration zones: a case study of Guilin City, China DOI Creative Commons
Jian Zhong Xu,

Deqin Fan,

Fangzhen Wang

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 24, 2025

Guilin City, located in a typical karst landform area China, is one of the sustainable development demonstration zones. Evaluating habitat quality City and exploring its driving factors are helpful formulating effective measures for development. Based on Integrated Valuation Ecosystem Servicesand Tradeoffs (InVEST) model combined methods such as spatial autocorrelation analysis, Geographical detector Geographically weighted regression (GWR) model, this study evaluated from 2001 to 2022. The also analyzed spatiotemporal characteristics their possible factors. results indicate that: (1) average was 0.59, with 47.98% classified having good or excellent quality; however, has shown downward trend over past 22 years. (2) Moran’s I values were all greater than 0.8, indicating significant positive correlation clustering. Among these, low–low aggregation regions largest, whereas high–high showed most decrease. (3) Elevation factor affecting differentiation Guilin. interactions between various stronger those any single factor, exhibiting dual-factor enhancement effect. This highlights complexity comprehensive impact multiple changes provides scientific basis policy recommendations ecological protection within national agenda’s innovative

Language: Английский

Effectiveness and driving mechanisms of ecological conservation and restoration in Sichuan Province, China DOI Creative Commons
Wei Li, Xi Chen,

Jianghua Zheng

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113238 - 113238

Published: Feb. 21, 2025

Language: Английский

Citations

0
Spatial and Temporal Variations of Habitat Quality and Influencing Factors in Urban Agglomerations on the North Slope of Tianshan Mountains, China DOI Creative Commons
Ran Wang,

Honglin Zhuang,

Mengzhen Cheng

et al.

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 539 - 539

Published: March 5, 2025

The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for development western China, has experienced rapid regional economic and high population concentration since twenty-first century. Accompanied by increase in human activities NSTM, it significantly altered land use structure, leading to varying levels habitat disturbance degradation. In this paper, based on cover (LULC) NSTM from 2000 2020. InVEST model was employed assess quality, revealing notable spatial temporal variations. A geoprobe further explore drivers spatially distributed pattern quality research region. results show that (1) 2020, largely characterized grassland, unused land, cropland terms use, with expansion construction land; (2) overall study area is poor, clear distribution south low north, predominance grades, trend decreasing then increasing shown direction; (3) under influence urbanization region, degradation degree shows distinct radial middle at edges, “increase-decrease-increase” over time; (4) geodetector altitude type have greatest indicating region primarily influenced use.

Language: Английский

Citations

0
Spatio‐Temporal Change of Habitat Quality in Northeast China: Driving Factors Exploration Based on Land Use and Land Cover Change DOI Open Access
Chuanbao Wu, Yuanyuan Cui, J.L. Zhen

et al.

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: March 30, 2025

ABSTRACT Ecological environment plays an indispensable role in sustaining and developing human society natural ecosystems, while it continually suffers from degradation caused by activities. Land Use Cover (LULC), which serves as a proxy of the intensity intervention, has been regarded equally important factor affecting habitat quality climate change. Despite exploring close relationship between LULC changes quality, current research remains largely theoretical does not delve into management measures following degradation. Consequently, its practical implications for ecological conservation are limited. In this study, taking Northeast China, prominent contradiction protection, study area, InVEST model was introduced to assess based on data 2000 2020. Then, Geographically Weighted Regression (GWR) employed analyze explanatory variables change terms The results indicated that China 2020 mainly occurred cultivated land, artificial grassland, forestland. Habitat demonstrated progressive decline yet remained at intermediate level exhibited significant spatio‐temporal heterogeneity whole. Furthermore, regression there correlation Finally, classified three functional zones K‐Means clustering analysis: coordinated development zone, key each with own characteristics priorities. findings can provide scientific reference rational use land zoning China.

Language: Английский

Citations

0
Assessing the spatiotemporal characteristics and driving factors of habitat quality in sustainable development demonstration zones: a case study of Guilin City, China DOI Creative Commons
Jian Zhong Xu,

Deqin Fan,

Fangzhen Wang

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 24, 2025

Guilin City, located in a typical karst landform area China, is one of the sustainable development demonstration zones. Evaluating habitat quality City and exploring its driving factors are helpful formulating effective measures for development. Based on Integrated Valuation Ecosystem Servicesand Tradeoffs (InVEST) model combined methods such as spatial autocorrelation analysis, Geographical detector Geographically weighted regression (GWR) model, this study evaluated from 2001 to 2022. The also analyzed spatiotemporal characteristics their possible factors. results indicate that: (1) average was 0.59, with 47.98% classified having good or excellent quality; however, has shown downward trend over past 22 years. (2) Moran’s I values were all greater than 0.8, indicating significant positive correlation clustering. Among these, low–low aggregation regions largest, whereas high–high showed most decrease. (3) Elevation factor affecting differentiation Guilin. interactions between various stronger those any single factor, exhibiting dual-factor enhancement effect. This highlights complexity comprehensive impact multiple changes provides scientific basis policy recommendations ecological protection within national agenda’s innovative

Language: Английский

Citations

0