The Temporal and Spatial Evolution Characteristics and Driving Factors of Ecosystem Service Bundles in Anhui Province, China

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 736 - 736

Published: May 23, 2024

Identifying ecosystem service bundles and their long-term evolutionary characteristics is essential for the overall enhancement of regional services, as well division management functional areas, providing a basis decision-making in formulating ecological environmental protection policies, development planning. Based on land use, remote sensing, meteorological data obtained from Anhui Province, this study assessed six important functions, including food production (FP), water yield (WY), carbon sequestration (CS), soil conservation (SC), habitat quality (HQ), landscape aesthetics (LA), at township scale 2000, 2010, 2020. On basis, k-means clustering method was used to identify bundles, analyze spatio-temporal evolution trajectory explore driving factors using GeoDetector 2015 The results indicate following: (1) At spatial level, diverse services demonstrate pronounced differentiation. distribution pattern HQ, fixation, SC generally lower north higher south, with areas high value predominantly located western Dabie Mountains mountains Southern Anhui. Conversely, FP exhibit reverse pattern, WY display gradual increase while cultural are more dispersed, primarily Mountains, Yangtze River Basin, other locations. temporal scale, WY, SC, mainly an increasing trend, marked by significant increase, whereas tend present decreasing trend. (2) Province can be categorized into four distinct types bundles: grain bundle (GPB), mountain (MECB), urban living (ULB), core (CPB). Ecosystem clear differentiation, identical substantial space. Between 2000 2020, displayed evolution, prevalence GPBs diminishing, share ULBs progressively increased, number MECBs CPBs remained largely stable. (3) In process, average annual precipitation, proportion forest land, slope constitute principal natural influencing construction represents primary socio-economic factor, exerting influence than factors.

Language: Английский

---

Detecting and assessing the phased impacts of climate change and human activity on vegetation dynamics in the Loess Plateau, China

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(2)

Published: Jan. 1, 2025

Language: Английский

---

Detecting Land Use Changes in Ordos City Using the Google Earth Engine Remote Sensing Cloud Platform

International Journal of Agricultural and Environmental Information Systems, Journal Year: 2025, Volume and Issue: 16(1), P. 1 - 20

Published: Jan. 17, 2025

This study leverages Google Earth Engine's remote sensing cloud platform to examine land use changes in Ordos City between 2000 and 2017. The analysis focuses on the quantitative shifts, intensity, spatial structure of dynamics. Findings indicate significant transformation over period, with woodland increasing by 56% at a rapid annual growth rate 3.29%, while grassland, water areas, unused decreased. development pace various types accelerated 2010 2015, particularly for woodland, cultivated land, construction land. Spatial disparities were evident among different changes, more variations observed southeast compared northwest. Despite less noticeable differences from 2017, diversity grew gradually. These results offer valuable data-driven insights promoting sustainable strategies region.

Language: Английский

---

Identification and Optimization of Ecological Restoration Areas Coupled with Ecosystem Service Supply and Demand in the Northern Shaanxi Loess Plateau

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 287 - 287

Published: Jan. 30, 2025

Ecological restoration is crucial for maintaining national ecological security. Scientific and reasonable identification of key areas a difficult problem facing the current spatial planning. This study evaluated ecosystem service (ES) supply, demand, their spatio-temporal characteristics in Northern Shaanxi Loess Plateau (NSLP). A coupling coordination degree (CCD) model was applied to interactive relationship between ES supply demand. The improved ant colony optimization (ACO) explore priority restoration. results showed that balance had significant differences. Higher were distributed south, higher demand located central northern parts. exhibited similar increasing trend from north south. Temporally, distinct time-varying across different types services. Total decreased 2000 2020, while total first declined then rose. Moreover, decreasing 21.22% regions NSLP. Furthermore, CCD about 82% disorder status 2000, 90% 2010 2020. also implied most gradually Finally, four enhancement priorities further identified perspectives enhancing capacity. prioritization enhancements variations, with top 15% area east–central west–central regions. 5% services can bring 2,470,400 yuan benefits. research offer scientific theoretical guidance as well reference decision-makers undertake efforts.

Language: Английский

---

Ecosystem Service Trade-Offs and Synergies in a Temperate Agricultural Region in Northeast China

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 852 - 852

Published: Feb. 28, 2025

Ecosystem services (ESs) are essential for balancing environmental sustainability and socio-economic development. However, the of ESs their relationships increasingly threatened by global climate change intensifying human activities, particularly in ecologically sensitive agriculturally-intensive regions. The Songnen Plain, a crucial agricultural region Northeast China, faces considerable challenges sustaining its due to overexploitation land, degradation, variability. This study assessed five key Plain from 2000 2020 across multiple scales: habitat quality (HQ), soil conservation (SC), water yield (WY), food production (FP), windbreaking sand fixing (WS). We evaluated trade-offs synergies between these ESs, as well driving factors main ES trade-offs. Our findings indicate that provisioning (WY FP) regulating (SC WS) improved over time, with FP exhibiting most significant increase at 203.90%, while supporting (HQ) declined 32.61%. primary ecosystem service multifunctionality areas were those provided FP, SC, WY, accounting 58% total. varied spatial scales, stronger being observed pixel scale more pronounced county scale. Climate factors, precipitation temperature, played role shaping than anthropogenic factors. provides valuable insights into restoration sustainable management temperate regions, implications protection northeastern black safeguarding national security.

Language: Английский

---

Spatio-Temporal Dynamics and Drivers of Ecosystem Service Bundles in the Altay Region: Implications for Sustainable Land Management

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 805 - 805

Published: June 6, 2024

Understanding the dynamics of ecosystem services (ESs) in arid landscapes and socio-ecological systems is crucial for sustainable development human well-being. This study uses Invest model to quantify spatio-temporal changes four key ecosystems Altay from 1990 2020: water yield (water yield), carbon stock (carbon stock), soil retention (soil retention), habitat quality (habitat quality). The trade-offs/synergies between different ESs were investigated via Spearman’s correlation analysis. Ecosystem service bundles (ESBs) mapped using self-organizing mapping (SOM), drivers ES relationships ESBs revealed through redundancy results showed that increased by 33.7% 1.2%, while decreased 3.5% 1.24%, respectively. spatial distribution pattern had a clear zonal pattern, with northern mountainous areas higher than southern desert areas. six pairs ESs, general, mainly low trade-off high synergistic relationships, trade-offs stock, quality, decreasing trend over time. Four types distinguished, compositional differences within each ESB determined interactions landscape types. There are complex non-linear years. Before 2010, ecological factors influencing ESBs, whereas social environmental combined drive allocations after 2010. Additionally, this found implementation conservation measures, such as reforestation land management practices, positively influenced provision region. These findings underscore importance integrating efforts into use planning decision-making processes ensure delivery landscapes.

Language: Английский

---

Spatiotemporal Changes and Driving Factors of Land Use/Land Cover (LULC) in the Wuding River Basin, China: Impacts of Ecological Restoration

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10453 - 10453

Published: Nov. 28, 2024

Over the past two decades, large-scale ecological restoration in Loess Plateau has significantly transformed land use and cover (LULC) Wuding River Basin (WRB), improving governance environmental conditions. This study examines spatiotemporal evolution of LULC its driving factors from 2000 to 2020, employing methods such as dynamic degree, transfer matrix, migration trajectory, geographical detector. Results show that (1) grassland dominates basin’s (78.16%), with decreases cropland desert areas, expansions grassland, forest, urban areas. Water bodies minimal fluctuations. The mean annual degree types (from highest lowest) is follows: forest > water grassland. overall fluctuated, initially decreasing (0.85%–0.68%), then increasing (0.68–0.89%), followed by another decline (0.89–0.30%). (2) patterns follow a northwest-to-southeast gradient, primary transitions secondary urban, bodies. Spatial mainly shifts westward northward. (3) Under single-factor influence, natural factors, especially slope (7.2–36.4%) precipitation (6.1–22.3%), are drivers changes, population density (7.9%) GDP (27.5%) influencing In interaction topography climate (40.5–66.1%) primarily drive increases cropland, while human activities (24.8–36.7%) influence area expansion. Desert reduction largely driven climatic (40.3%). between shows either bi-factorial or nonlinear enhancement effect, suggesting their combined offers stronger explanatory power than any single factor alone. highlights significant changes WRB, both activities, contributing enhanced sustainability.

Language: Английский

---