Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments DOI Creative Commons
Mingke Zhang, Hao Zhang, Wei Deng

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(19), P. 3648 - 3648

Published: Sept. 29, 2024

China’s arid regions are particularly vulnerable to the adverse effects of climate change and human activities, which pose threats habitat quality. Consequently, evaluations these vital for devising ecological strategies initiating regional remediation efforts. However, environmental variations in areas can cause quality fluctuations, complicates precise assessments. This study introduces a refined methodology that integrates remote sensing data field survey biomass modify estimates obtained from InVEST model Altai region over three decades. A comparative analysis unmodified, normalized difference vegetation index (NDVI)-modified biomass-modified was conducted. The results revealed an improvement correlation between observations, with significant increase R2 value 0.129 0.603. unmodified exhibits subtle mountainous areas, slight decline plains. modified shows increasing trend areas. finding contrasts reductions mountains typically reported by other studies. approach accurately expresses across different types, declines forested improvements shrubland grassland regions. is suitable accommodates urban agricultural ecosystems affected offering empirical biodiversity management.

Language: Английский

Trade-offs and synergies of ecosystem services in karst multi-mountainous cities DOI Creative Commons

Qin Li,

Yu Bao, Zhitai Wang

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 159, P. 111637 - 111637

Published: Jan. 28, 2024

Clarifying spatiotemporal changes in ecosystem services (ESs) and understanding the trade-offs synergies among different are crucial for effective ES management regional sustainable development. In karst multi-mountainous cities (KMCs), unique landscape pattern, fragile ecological environment, intense human disturbance contribute to accelerated rocky desertification degradation of ESs. Studying provides scientific guidance formulating policies, enhancing value, mitigating KMCs. Therefore, this study analyzes four ESs — habitat quality (HQ), carbon storage (CS), water production (WP), soil retention (SR) using remote sensing images from 2008, 2013, 2018 a typical KMC, aiming reveal investigate driving factors. The key findings as follows: 1) High HQ CS values were scattered patches throughout area, mainly within Urban Remnant Mountain (URM) woodland, but fragmented by construction land. SR WP initially increase then decreased, especially bareland. WP-HQ WP-SR, exhibited tradeoffs, while WP-CS CS-HQ demonstrated synergies. No significant trade-off or synergy relationships observed between HQ-SR CS-SR. 2) URMs, biological patch KMCs' built-up highest degree trade-off/synergy, accounting 32%. When considering only degree, URMs accounted 37%. To improve overall advantages KMCs, greater design consideration preservation essential. 3) Both environmental socio-economic factors influence synergies, with playing dominant role. Future green space planning should consider road layout, land use results can guide policies urban development other

Language: Английский

Citations

15
Temporal and spatial responses of landscape patterns to habitat quality changes in the Poyang Lake region, China DOI
Xinmin Zhang, Wenqiang Wan, Houbao Fan

et al.

Journal for Nature Conservation, Journal Year: 2023, Volume and Issue: 77, P. 126546 - 126546

Published: Dec. 16, 2023

Language: Английский

Citations

11
Spatio-Temporal Differentiation and Driving Factors of Land Use and Habitat Quality in Lu’an City, China DOI Creative Commons
Guandong Wang, Qingjian Zhao,

Jia Wei-guo

et al.

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 789 - 789

Published: June 3, 2024

The spatio-temporal evolution of land use/land cover (LULC) and habitat quality (HQ) is vital to maintaining ecological balance realizing regional sustainable development. Using the InVEST CA-Markov model, with Kendall coefficient as sensitivity value, LULC HQ in Lu’an City from 2000 2030 are simulated evaluated. Then, Spearman used analyze correlation between driving factors. Finally, influence policy factors on discussed. results show following: (1) 2030, mainly cropland (about 40%) forest 30%) which transferred construction land; (2) kappa 0.9097 (>0.75), indicating that prediction valid; (3) shows DEM (0.706), SLOPE (0.600), TRI (0.681), HFI (−0.687) strongly correlated HQ, while FVC (0.356) GDP (−0.368) weakly HQ; (4) main reasons for decrease increase area, vulnerability artificial forests threat factors, their low biodiversity. This study outlines exploratory research two perspectives effects provide suggestions development City.

Language: Английский

Citations

4
Spatiotemporal evolution and driving forces of landscape structure and habitat quality in river corridors with ceased flow: A case study of the Yongding River corridor in Beijing, China DOI

Xintong Du,

Yan Fang, Haiyue Zhao

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 123861 - 123861

Published: Jan. 8, 2025

Language: Английский

Citations

0
Changes in landscape disturbance intensity of sloping land in mountainous areas and their relationship with ecosystem services DOI Creative Commons
Huiqing Han, Xin Yu,

Yuanju Jian

et al.

Nature Conservation, Journal Year: 2025, Volume and Issue: 58, P. 129 - 152

Published: March 7, 2025

The stability of landscapes on sloping land forms the foundation for ecological protection and sustainable development in mountainous regions. However, with intensification human activities, particularly complex areas southwest China, landscape patterns have been severely disrupted. This study examines spatiotemporal changes disturbance intensity Guiyang their impact ecosystem services. findings show that, over past 20 years, overall has generally decreased, between 2000 2010. increased certain gradient zones, such as slopes 20–25 degrees. Meanwhile, services declined, especially water yield crop production, while carbon stock slightly increased. reveals a significant spatial correlation services, varying relationships across different It emphasises profound activities steeper slopes. contribution this research lies providing scientific basis management service conservation areas, highlighting importance mitigating strengthening restoration.

Language: Английский

Citations

0
Spatio-temporal evolution of habitat quality and its influencing factors in karst areas based on the InVEST model DOI Creative Commons

Chao Ma,

Hua Yang, Yan Zhi

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0314161 - e0314161

Published: March 13, 2025

The Critical Karst Zone provides rich natural resources and is an important habitat for the survival development of world’s human population. Meanwhile, urbanization processes have disrupted structure function ecosystems, endangering biodiversity habitats. However, existing studies few frequently explored combined effects environment activities on changes in quality. This article uses InVEST model GeoDetector method to analyze landscape patterns, spatiotemporal evolution quality, their driving factors karst areas. results show that: (i) From 1990 2020, forest, cropland, grassland fluctuated sharply, while building waters area showed exponential upward trend. overall fragmentation spatial heterogeneity are enhanced. (ii) quality index decreased from 0.7751 0.74085, showing downward shows a distribution pattern “high surrounding areas low central areas”, autocorrelation analysis that county-level units significant agglomeration effects. (iii) type enhancement dual factor or non-linear, which land use intensity population density main spatio-temporal In summary, adopting stringent ecological protection restoration initiatives aimed at minimizing activity safeguarding integrity regions imperative. Such measures contribute scientific underpinning decision-making regarding optimization regional composition enhance planning strategies.

Language: Английский

Citations

0
Road network expansion and landscape dynamics in the Chaohu Lake wetland: A 20-year analysis DOI Creative Commons
Xiang Gao,

Yue Qian,

Yifan Fang

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113443 - 113443

Published: April 1, 2025

Language: Английский

Citations

0
Determining rainwater dissolved organic carbon to reveal atmospheric carbon deposition in China's karst city: Variations, origins, and deposition flux DOI

Qing Ma,

Jie Zeng,

Qixin Wu

et al.

Atmospheric Research, Journal Year: 2024, Volume and Issue: 305, P. 107439 - 107439

Published: April 25, 2024

Language: Английский

Citations

3
Use of interpretable machine learning for understanding ecosystem service trade-offs and their driving mechanisms in karst peak-cluster depression basin, China DOI Creative Commons

Yichao Tian,

Qiang Zhang, Tao Jin

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112474 - 112474

Published: Aug. 9, 2024

Language: Английский

Citations

2
Use of Interpretable Machine Learning for Understanding Ecosystem Service Trade-Offs and Their Driving Mechanisms in the Karst Peak-Cluster Depression, China DOI

Yichao Tian,

Qiang Zhang, Tao Jin

et al.

Published: Jan. 1, 2024

The peak cluster depression is one of China's most ecologically fragile areas, with extensive karst development. It also notable for its successful ecological restoration projects against rocky desertification. Understanding the trade-offs and synergies among ecosystem services in this region exploring their driving mechanisms are crucial addressing many issues related to ecosystems devising management strategies that enhance human well-being. However, existing models assessing often fail consider unique geological context depressions, making it challenging apply general international area. Additionally, current research cannot adequately describe nonlinear processes, determining thresholds factors remains difficult. To address these challenges, study focused on basin Southwest China evaluated service functions 2000 2020 by adapting carbon fixation soil erosion basin. Using interpretable machine learning XGBoost-SHAP model, we quantified characteristics threshold effects synergies. Our findings include following: (1) Carbon storage increased from 753.99 tCO2∙km-2∙a-1 756.70 2020; however, decreased 16.56 t∙ha-2∙a-1 15.12 t∙ha-2∙a-1. (2) At watershed scale, exhibited both synergistic relationships, 63.3% area showing a trade-off 36.7% relationship. Trade-offs were prevalent upper lower reaches, while middle reaches demonstrated relationships. (3) NDVI emerged as primary driver changes trade-offs, NDVI, precipitation, temperature, evapotranspiration, elevation, lithology significant explanatory factors. These impact manner exhibit pronounced effects. (4) Climate contributed 31.65% geomorphic 14.81%, 5.72%, activities 5.39%. (5) Local SHAP values indicated substantial differences contributions drivers at different scales trade-offs. methodology implemented offers practical approach sustainable differentiated integrating assessment methods.

Language: Английский

Citations

0