An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services

Optimization of landscape ecological risk assessment method and ecological management zoning considering resilience

Currently, landscape ecological risk (LER) assessment faces issues such as strong subjectivity and limited applicability in ecological zoning. Therefore, this research attempts to optimize the LER assessment model by evaluating landscape vulnerability based on ecosystem services and to conduct ecological management zoning in combination with ecosystem resilience (ER), taking the Luo River Watershed in the eastern part of the Qinling Mountains as the study area. This research selected appropriate analytical granularity and assessment units at the watershed scale and evaluated the of LER and ER based on these. Subsequently, the spatial correlation between LER and ER was analyzed, and bivariate Moran's index was utilized for ecological management zoning. Finally, the main influencing factors of LER and ER were identified through the use of geographical detectors. From 2001 to 2021, the regional LER increased overall, with a spatial distribution showing lower values in the west and higher values in the east. Spatially, LER exhibited a decreasing trend as ER increased, with a relationship approximating a quadratic function. Based on LER and ER, the study area can be divided into ecological adaptation region, ecological conservation region, and ecological restoration region. The distribution differences between ecological conservation region and ecological restoration region were evident, and both zones exhibited an expanding trend. Land use type was a key factor influencing LER and ER, followed by elevation and climate. The improved LER assessment model helps to more reasonably reflect the regional LER level and provides support for optimizing LER assessment models. Additionally, this research enriches zoning methodologies in the field of ecological restoration and offers important references for the implementation of ecological management and ecological restoration strategies in similar regions.

Spatiotemporal evolution of landscape ecological risk and its driving factors of the Beijing-Tianjin-Hebei major mineral belt, 1985-2022

The Beijing-Tianjin-Hebei major mineral belt represents a significant economic development area in China. Effective monitoring and assessment of the regional landscape ecological risk can provide a scientific basis for an ecological protection strategy for the environmental protection of the Beijing-Tianjin-Hebei major mineral belt. In this study, a landscape ecological risk index was constructed based on land use/land cover, and the spatial and temporal variations of landscape ecological risk were subsequently analyzed. Furthermore, the contribution of the main driving factors of landscape ecological risk was quantified in the Beijing-Tianjin-Hebei major mineral belt. The results demonstrate that: (1) The land use types within the study area underwent significant changes from 1985 to 2022. The predominant type of transfer was cropland, which was primarily converted to construction land, grassland, and woodland. (2) The landscape ecological risk in central-northern and western parts of the Beijing-Tianjin-Hebei major mineral belt is higher, while the landscape ecological risk in the southwest parts is lower. Using 2015 as the time point, the landscape ecological risk in the study area was found to change, with the average value of landscape ecological risk for all classes of landscape ecological risk within 2015-2022 being lower than that of 1985-2015, with the exception of the high-risk area. The mean annual landscape ecological risk is obviously higher during the 1985-2015 period in comparison to the 2015-2022 period, with the exception of regions exhibiting high risk. (3) There is a significant positive spatial correlation between landscape ecological risks in different periods. The pattern of landscape ecological risk exhibits both 'high-high' aggregation and 'low-low' aggregation. The 'high-high' aggregations are primarily located in the northern, central and western parts of the study area, while the 'low-low' aggregation zones are mainly located in the southeastern study region. (4) The spatial distribution of landscape ecological risk is predominantly shaped by population density and slope. In the context of interactive factor detection, the positive interaction between slope and average annual temperature, night-time illumination and slope, population density and annual precipitation were identified as exerting a more significant influence on the observed spatial differentiation of landscape ecological risk. It was found that the interaction of multiple drivers had a more pronounced impact on landscape ecological risk than any single factor. The findings of the research project provide a scientific rationale and reference for future land use, resource optimization, landscape ecological risk differential management and ecological restoration. Furthermore, the findings are of considerable importance in terms of maintaining ecological security patterns.

Study on the spatial and temporal evolution of ecosystem service value based on land use change in Xi'an City

Ecosystem service value (ESV) is essential for understanding regional ecological benefits and resources. This study utilizes the fourth phase of land use data from the Resource and Environment Science Data Centre of the Chinese Academy of Sciences. We corrected the ESV coefficient using the equivalent factor method for value per unit area and integrated the biomass factor of farmland ecosystems in Shaanxi Province. This allowed us to adjust the equivalent factor for China's terrestrial ecosystem on a geographic scale. Based on these corrections, we analyzed changes in land use and the evolution of ecosystem service value over the past two decades in Xi'an, China. Our findings indicate that the proportions of cultivated and forest land in Xi'an remained stable from 2000 to 2020, despite an increase in construction land and a decrease in cultivated areas. Forest and unused lands remained stable, while grassland and water bodies fluctuated. The ESV related to land use in Xi'an increased by 938.8 million yuan during this period, with high-value areas primarily located in the forested regions south of the Qinling Mountains and along the Weihe, Bahe, and Chanhe Rivers. Low-value zones were concentrated in the urban core. This research enhances methodologies for quantifying urban ESV, providing vital support for land resource management, ecological conservation, and high-quality urban development in major cities in China. These findings will inform policy-making for sustainable urban growth.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.