Ecological Risk Assessment of Transboundary Region Based on Land-Cover Change: A Case Study of Gandaki River Basin, Himalayas

Spatial and temporal evolutionary characteristics of landscape ecological risks and their drivers on the Qinghai-Tibet Plateau

The changes in landscape ecological risk (LER) of the Qinghai-Tibet Plateau (QTP) profoundly affect the ecological environment of China and the world. We measured the evolution of the LER level and its driving factors through the past 40 years using meteorological data, population density information, and land use data acquired through remote sensing monitoring techniques spanning the years 1980 to 2020. Several key findings were derived: (1) The overall LER of the QTP was at a medium level during 1980-2020, with a fluctuating but decreasing overall trend. (2) Between 1980 and 2020, the spatial distribution of LER in the QTP was high in the west and low in the east; the LER level of the six provinces (districts) showed an overall decrease. (3) During 2000-2020, the LER of the QTP was influenced by a complex mechanism of action. The interactions between different influencing factors were mainly non-linear reinforcement and two-factor reinforcement, and factor interaction significantly enhanced the effect on LER. The findings are of significance for the prevention, control, and management of LER in the QTP.

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

Land-Use Transformation and Landscape Ecological Risk Assessment in the Three Gorges Reservoir Region Based on the “Production–Living–Ecological Space” Perspective

Rapid urbanization and land-use change cause risk in regional ecological security. It is very significance to explore the evolutionary trend of land-use change and landscape ecological risk (LER) in an ecologically fragile area, especially in terms of maintaining sustainable development in a regional ecological environment. We selected the Three Gorges Reservoir Region (TGRR) as the study area based on land-use and land-cover data for 2000, 2010, and 2020. The land-use classification system used here was constructed using the perspective of the production–living–ecological space (PLES). The GIS spatial-analysis technique and FRAGSTATS 4 software were used. We used the method of the land-use transfer matrix, the landscape ecological risk assessment model, the ecological contribution rate of land-use transfer, and spatial autocorrelation analysis. We performed quantitative analysis of the spatio-temporal pattern of PLES and its LER in the TGRR over the past 20 years. The results show that: (1) The area of human living space (HLS) has expanded significantly—by 1469.37 km2 (+326.66%), while the area of agricultural production space (APS) has been compressed by both the urban/rural living space (URLS) and the forestland ecological space (FES), particularly during the last 10 years; (2) The overall LER results were medium, but LER is increasing; (3) The LER in the northern area of the Yangtze River is higher than in the south. The Wanzhou district and the downstream areas had a lower LER; and (4) The transformation from agricultural production space to forestland ecological space and urban/rural living space has had a higher contribution rate to the LER compared to other events. These results can be used as a reference for land planning, sustainable development, and ecological civilization construction in ecologically fragile areas.

Towards Sustainable Management of Urban Ecological Space: A Zoning Approach Hybridized by Ecosystem Service Value and Ecological Risk Assessment

Massive growth is posing threat to the ecological security and sustainability of cities. Ecosystem service value (ESV) and ecological risk index (ERI) assessment can be conducted to enhance urban ecosystem management through the enhanced recognition of these values and risks in decision-making. This paper aimed to measure spatiotemporal ESV and ERI for Shizuishan City located in central China, and, based on this, how to zone urban ecological space using land cover data (for the years 2010, 2015, and 2020). The management options of different zones were suggested to mitigate and manage any potential negative impacts on urban ecological security. Results show that: (1) The spatial distribution characteristic of ESV is “high in the south and low in the north”. The total ESV exhibited an upward tendency from 2010 to 2020. (2) The high-ERI areas were distributed in the peripheral region, while the low-ERI areas were concentrated in the central region. The ERI of water was in decline continuously, whereas the ERI of wetland maintained a high level. (3) The zoning approach integrating ESV and ERI assessment can truly reflect the status of the environment and better clarify the direction of ecological development for different areas. Among four different ecological zones, the high-ESV and low-ERI areas (I) have abundant ecological resources, and they are set as “Priority Development Areas”. The low-ESV and low-ERI areas (II) are set as “Ecological Improvement Areas” because the area of ecological lands are confined. The low-ESV and high-ERI areas (III) have a fragile ecological environment, and they are set as “Exploitation-Prohibited Areas”. The high-ESV and high-ERI areas (IV) are mainly distributed near water and wetland, and they are set as “Research-focused Areas”.