Multi-Scenario Prediction of Landscape Ecological Risk in the Sichuan-Yunnan Ecological Barrier Based on Terrain Gradients

Abstract

Land use changes induced by human activities change landscape patterns and ecological processes, threatening regional and global ecosystems. Terrain gradient and anthropogenic multi-policy regulation can have a pronounced effect on landscape components. Forecasting the changing trend of landscape ecological risk (LER) is important for national ecological security and regional sustainability. The present study assessed changes in LER in the Sichuan-Yunnan Ecological Barrier over a 20-year period using land use data from 2000, 2010, and 2020. The enhanced Markov-PLUS (patch-generating land use simulation) model was used to predict and analyze the spatial distribution pattern of LER under the following three scenarios. These were business-as-usual (BAU), urban development and construction (UDC), and ecological development priority (EDP) in 2030. The influence of terrain conditions on LER was also explored. The results showed that over the past 20 years, the LER index increased and then decreased and was dominated by medium and low risk, accounting for more than 70% of the total risk-rated area. The highest and higher risk areas for the three future scenarios have increased in spatial extent. The UDC scenario showed the largest increase of 3341.13 km2 and 2684.85 km2, respectively. The highest-risk level has a strong selectivity for low gradients, with high-level risks more likely to occur at low gradients. The response of ecological risk to gradient changes shows a positive correlation distribution for high-gradient areas and a negative correlation distribution for low-gradient areas. The influence of future topographic gradient changes on LER remains significant. The value of multiscale geographically weighted regression (MGWR) for identifying the spatial heterogeneity of terrain gradient and LER is highlighted. It can play an important role in the formulation of scientific solutions for LER prevention and of an ecological conservation policy for mountainous areas with complex terrain.

Study on the Spatial Heterogeneity of the Impact of Forest Land Change on Landscape Ecological Risk: A Case Study of Erhai Rim Region in China

“…As a commonly used quantitative research method, the landscape pattern index method describes landscape patterns and their changes through a combination of multiple indices. In line with previous research results [46,47], the present study used the landscape disturbance index, landscape vulnerability, and landscape loss indices to construct an ecological risk assessment model that could be used to analyze the temporal and spatial evolution features of landscape ecological risk in the Erhai rim region. The formula is written as follows:…”

Variations of Habitat Quality and Ecological Risk and Their Correlations with Landscape Metrics in a Robust Human Disturbed Coastal Region—Case Study: Xinggang Town in Southern China

“…Given the ability of the respective landscape type to resist external influences, vulnerability index was assigned as follows: water body = 7; mudflat = 6; aquaculture land = 5; farmland = 4; unused land = 3; forest = 2; built-up land; and road = 1. The settings of these parameters are mainly based on previous studies [40,41]. An excessively small fishnet size will greatly increase the statistical workload, and too large a fishnet size reduces the accuracy of the study results.…”

Research on Land Use Pattern and Ecological Risk of Lanzhou–Xining Urban Agglomeration from the Perspective of Terrain Gradient

“…Therefore, scholars often choose the best research unit according to the needs of the ecological risk assessments for different purposes and regions to optimize the evaluation effect [20]. From the research content, it mainly includes the analysis of temporal and spatial evolution [21,22], dynamic monitoring [23], the influencing mechanism [24] and the driving factors [25] for ecological risk. In terms of research methods, the analytic hierarchy process [26], fuzzy evaluation method [27], principal component analysis method [28], multi-criteria evaluation method [29] and artificial neural network [30] are generally used for ecological risk assessment, although these methods have some shortcomings, such as the fuzzy evaluation method not being sensitive enough to reflect the ecological risk degreed of indicators, and the comprehensive evaluation method needing many indicators and complicated steps in the evaluation process [31].…”