Simulation, prediction and driving factor analysis of ecological risk in Savan District, Laos

Abstract

Introduction: In recent years, Laos, with its unique geographical advantages, has vigorously developed overseas trade and cultural tourism industry, which has promoted local economic development and introduced numerous ecological and environmental problems.Method: Therefore, this research took Savan District as the research area, built the landscape ecological risk assessment model to reveal the spatiotemporal evolution characteristics of Savan District’s ecological risk from 2000 to 2020. Then, the Geodetector model was utilized to explain the driving forces behind changes in the landscape’s ecological risk. Finally, the Markov-PLUS coupling model was used to simulate and predict the changes in the land use pattern and ecological risk in the Savan District in 2030.Results: The following results are presented. 1) The landscape types in Savan District are mainly cultivated land and forest land, with the lowest degree of landscape fragmentation and loss, and the highest degree of landscape loss in unused land. 2) From 2000 to 2020, the average ecological risk index of the whole study area was low and showed a decreasing trend yearly, with the area of low-risk areas accounting for more than 90%. 3) In 2030, the average ecological risk index of the entire Savan District and the two provinces will rise, meanwhile the low-risk areas in the midwest regions and high-risk areas in the northwest will increase.Discussion: This study can guide the future coordinated development of the social economy and ecological environment in Savan District, Laos, and has good reference significance for ecological construction in similar areas in Southeast Asia.

A landscape-based ecological hazard evaluation and characterization of influencing factors in Laos

“…These were the landscape fragmentation, isolation, dominance, disturbance, vulnerability, and loss indices (Table 2). The moving window method using a 15-km window was applied in Fragstats 4.2 to calculate the landscape pattern index of Laos (Bai and Weng, 2023;Xu et al, 2023). The window was moved from the left, upper section of the study area, and progressed at a single-grid step.…”

“…The geodetector used in the present study incorporated risk factor, ecological, and interaction detectors (Zhu et al, 2020). The present study selected the factor detector for use in the geodetector through reference to relevant previous research results (Huang et al, 2020;Liang et al, 2022;Xu et al, 2023). The present study identified 14 socioeconomic and regional accessibility factors affecting the landscape ecological hazard in Laos, including the digital elevation model (DEM), slope, fractional vegetation cover, annual average temperature, annual precipitation, organic matter content, population density, kilometer grid GDP, night light, distance from the road, distance to water, distance from the town center, and distance to parks and reserves.…”

Assessing Spatio-Temporal Variation and Associated Factors of Forest Fragmentation from Morphological Spatial Pattern Analysis and Geo-Detector Analyses: A Case Study of Xinyu City, Jiangxi Province of Eastern China

“…Based on the relevant studies [67][68][69] and the experimental comparisons, we reclassified the forest fragmentation severity of each analytical unit into five grades: low fragmentation, above-low fragmentation, medium fragmentation, sub-high fragmentation, and high fragmentation, by following the criteria of natural breakpoint reclassification.…”

Landscape Ecological Risk Evaluation Study under Multi-Scale Grids—A Case Study of Bailong River Basin in Gansu Province, China

“…Climate change and human activities pose increased risks to ecosystems and sustainable development [3,4]. The ecological environment is a prerequisite for social and economic development and a necessary safeguard for human survival [5]. The LERI reveals unfavourable consequences arising from natural or human activity [6].…”