Social–Ecological Factors and Ecosystem Service Trade-Offs/Synergies in Vegetation Change Zones of Qilian Mountain National Park During 2000–2020 DOI Creative Commons
Xiaoyuan Yang, Zhonghua Zhang, Huakun Zhou

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1402 - 1402

Опубликована: Апрель 15, 2025

An ecological restoration assessment aims to evaluate whether projects (ERPs) have achieved predefined objectives, such as improving fractional vegetation cover (FVC) and enhancing ecosystem services (ESs), well optimize strategies based on outcomes. Despite recent advancements, current studies still fall short of fully capturing the trade-offs among ESs identifying underlying drivers different trends. To address these challenges, we applied Theil–Sen method delineate change zones in Qilian Mountain National Park (QLMNP) between 2000 2020, employed bivariate Moran’s I statistics analyze synergies four within zones, including carbon sequestration (CS), soil conservation (SC), water (WC), biodiversity maintenance (BIO), utilized a spatial random forest (SRF) model explore main socio-ecological driving factors trends their distribution. Our results revealed significant recovery QLMNP particularly regions with initially low FVC. Positive CS, SC, BIO highlighted success efforts, primarily driven by land conversion forests increased precipitation. However, 8.82% exhibited stagnation or degradation due rising temperatures overgrazing, leading declines SC BIO. Notably, introduced ESs, especially high FVC areas, where strong trade-off emerged WC. These findings highlight need for refining balance resource allocation. Finally, integrated trends, ES relationships, propose grid-based zonal governance plans QLMNP, prioritizing WC enhancement critical components future planning. This study serves foundation optimizing maintaining while offering actionable insights fine-grained evaluation sustainable development planning other regions.

Язык: Английский

Spatiotemporal Dynamics of Ecosystem Services and Their Trade-Offs and Synergies in Response to Natural and Social Factors: Evidence from Yibin, Upper Yangtze River DOI Creative Commons
Chunhong Tian,

Liheng Pang,

Quanzhi Yuan

и другие.

Land, Год журнала: 2024, Номер 13(7), С. 1009 - 1009

Опубликована: Июль 7, 2024

During the rapid urbanization phase, trade-off between ecosystem services is most severe and also effective stage to implement ecological management. Exploring natural—social driving mechanisms for trade-offs contributes coordinated development of social economy nature. Taking typical mountainous city (Yibin) that currently in phase ecologically fragile as an example, utilizing a combination difference comparison, trade-off–synergy index (TSI), optimal-parameter-based geographical detector model (OPGD), multi-scale geographically weighted regression (MGWR), we spatially assess nature intensity ES relationships explore its social–natural mechanisms. Our findings reveal following: (1) Varied geospatial patterns four ESs—habitat quality (HQ), carbon storage (CS), soil conservation (SC), water yield (WY)—with greatest fluctuations WY. (2) Significant changes over time, showing predominant positive synergies WY-HQ, WY-SC, HQ-CS, negative HQ SC, WY-CS SC-CS. (3) Distinct, time-varying factors different relationships: climate topography WY, vegetation CS, economic HQ, SC. Rapid has diminished role natural factors. (4) The coefficients local various factors, based on which targeted recommendations can be proposed. For instance, establishment interconnected small wetlands green spaces urban areas enhancement multiple ESs. purpose this study provide scientific insights into optimizations key services’ are undergoing urbanization.

Язык: Английский

Процитировано

7
Construction of ecological conservation pattern based on ecosystem services of Three River Headwaters, Western China DOI Creative Commons
Jian Xue,

Zongxing Li,

Feng Qi

и другие.

Global Ecology and Conservation, Год журнала: 2023, Номер 44, С. e02491 - e02491

Опубликована: Апрель 26, 2023

The Three River Headwaters (TRH) is located in the hinterland of Qinghai-Tibet Plateau, which birthplace Yellow River, Yangtze and Lancang has an extremely important ecological status. In this paper, we use InVEST model USLE equation to assess ecosystem services (ESs) TRH as well identify security pattern propose conservation pattern. results show that ESs had overall increasing trend from 1990 2020, with a cumulative increase 4% area Class III hotspots. Based on construction source minimum resistance surface TRH, total 262 corridors length 22437 km are identified. To improve protection Priority Protection Area (PPA), Consolidation Enhancement (CEA) Coordination Optimization (COA) divided. Next, originality PPA's complex fragile types should be protected, disturbance by human activities strictly prohibited. CEA accelerate establishment new mechanism cross-linkage among various departments deal difficult problems protection. COA combine unique resources develop green tourism leisure sightseeing pastoralism drive regional economic development.

Язык: Английский

Процитировано

15
Windbreak and sand fixation service flow simulation in the terminal lake basin of inland rivers in arid regions: A case study of the Aral Sea basin DOI

Yonglong Han,

Xiaofei Ma, Wei Yan

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 945, С. 174047 - 174047

Опубликована: Июнь 15, 2024

Язык: Английский

Процитировано

6
Multifunctional evaluation and multiscenario regulation of non-grain farmlands from the grain security perspective: Evidence from the Wuhan Metropolitan Area, China DOI
Dan Huang,

Yanchi Lu,

Yaolin Liu

и другие.

Land Use Policy, Год журнала: 2024, Номер 146, С. 107322 - 107322

Опубликована: Авг. 30, 2024

Язык: Английский

Процитировано

6
Assessment of disaster mitigation capability oriented to typhoon disaster chains: A case study of Fujian Province, China DOI Creative Commons
Xiaoliu Yang, Xiaochen Qin, Xiang Zhou

и другие.

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112621 - 112621

Опубликована: Сен. 16, 2024

Язык: Английский

Процитировано

6
Assessing ecosystem service dynamics in China's coastal shelterbelt: Implications for ecosystem restoration DOI
Lixue Zhang, Lin Shi, Fan Yang

и другие.

Environmental Impact Assessment Review, Год журнала: 2024, Номер 106, С. 107515 - 107515

Опубликована: Апрель 18, 2024

Язык: Английский

Процитировано

5
Exploring the classification of China's ecosystem service networks and their driving factors based on current status and evolutionary trends DOI

Chaoyue Yu,

Jiahe Zhou, Zhengfeng Zhang

и другие.

Applied Geography, Год журнала: 2024, Номер 168, С. 103321 - 103321

Опубликована: Июнь 17, 2024

Язык: Английский

Процитировано

5
Sand fixation and human activities on the Qinghai-Tibet Plateau for ecological conservation and sustainable development DOI

Xiaohong Deng,

Heqiang Du,

Zongxing Li

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 912, С. 169220 - 169220

Опубликована: Дек. 12, 2023

Язык: Английский

Процитировано

13
Spatiotemporal heterogeneity and driving factors of ecosystem service relationships and bundles in a typical agropastoral ecotone DOI Creative Commons
Wei Dong, Xing Wu, Jianjun Zhang

и другие.

Ecological Indicators, Год журнала: 2023, Номер 156, С. 111074 - 111074

Опубликована: Окт. 21, 2023

Complex geographical environments and intensified human activities have induced significant spatiotemporal heterogeneity in ecosystem services (ESs) their relationships worldwide, especially agropastoral ecotones. Identifying the ecological socioeconomic driving factors of dynamics bundles among multiple ESs is essential for effective ecotone management restoration. Herein, five key ESs, including water yield (WY), carbon sequestration (CS), sand fixation (SF), soil conservation (SC), food production (FP), northern piedmont Yinshan Mountains (NPYM), a typical ecotone, were quantified from 1990 to 2018. Ecosystem service (ESBs) identified by K-means algorithm, trade-offs/synergies different each ESB investigated using Spearman's correlation analysis. Moreover, main ES ESBs detector model. Our results showed that NPYM exhibited high over last three decades, with WY, SC FP significantly improving time but CS SF slightly decreasing. Considering features spatial distribution, could be divided into four ESBs: grassland protection bundle (GPB), desertification control (DCB), agricultural (APB) forest (FCB). Although trade-offs between WY synergy occurred almost all ESBs, differences other observed ESBs. Generally, land use type was one dominant drivers most APB. However, climate, vegetation also played important roles various some bundles. substantial changes decades mainly due climatic factors. Overall, our indicated identifying provide useful guidance developing targeted zoning strategies

Язык: Английский

Процитировано

12
Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region DOI Creative Commons

Sijia Xiao,

Haonan Xia,

Jun Zhai

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(17), С. 3147 - 3147

Опубликована: Авг. 26, 2024

The Hexi region, located in a sensitive and fragile ecological zone northwest China, requires scientific assessment of ecosystem services their interactions. Identifying the main factors influencing spatial distribution is crucial for sustainable development effective management region. This study evaluates key services, including regulating (water conservation, soil carbon storage) provisioning (NPP), using Spearman’s correlation pixel-by-pixel analysis to calculate trade-offs synergies. Geographic detectors were used uncover underlying driving mechanisms. results show that: (1) From 2000 2020, NPP, storage showed fluctuating growth, while water conservation declined. Spatially, high-value areas storage, NPP concentrated central southern areas, high values mainly southeast regions. (2) synergies among various exhibit temporal shifts, along with scale effects heterogeneity. In area, proportion pixels showing trade-off relationship between accounts 48.21% 21.42%, respectively. These are southeastern regions, northwestern counties predominantly (3) Precipitation was dominant factor as well these services. Among natural factors, climatic significantly more influential than socio-economic interaction two had greater explanatory power single factors.

Язык: Английский

Процитировано

4