基于PLS-SEM的生态系统健康变化及驱动因素分析以京津冀为例

Analysis on ecosystem health change and driving factors based on PLS-SEM: A case study of Beijing-Tianjin-Hebei region

  • 摘要: 明确区域生态系统健康时空变化及其驱动因素影响途径,对生态系统管理和恢复具有重要意义。基于“活力-组织-弹性-服务”模型,评估2000—2022年京津冀地区生态系统健康水平,从全局和分区(山区、平原)角度分析其动态演变特征,利用偏最小二乘-结构方程模型分析人为与自然因素对生态系统健康的影响路径,运用最优参数地理探测器模型识别主要驱动因子。结果表明:2000—2022年京津冀地区生态系统健康呈改善趋势,其中山区持续增长,平原为先降后升,空间分布呈山区高平原低的特征,山区北部和西部生态系统健康改善显著;人类活动对生态系统健康产生的负面影响高于自然因素产生的正面影响,全局和山区的地形和植被覆盖产生较高正面影响,景观组成则产生了显著的直接负面影响,而社会经济因素产生间接负面影响;平原地区景观组成、地形和植被覆盖因素均产生较高的直接正面影响,社会经济则产生为显著负面影响。单因子分析表明林地及建设用地占比、坡度和高程是全局生态系统健康的主要驱动因子,林地占比、耕地占比、建设用地占比和归一化植被指数为山区的主要驱动因子,而建设用地占比、归一化植被指数和夜间灯光为平原的主要驱动因子。未来可基于山区与平原生态系统健康驱动因子分区施策,加强山区生态保护政策的实施,优化平原地区土地利用与植被覆盖,以实现区域生态可持续发展。

     

    Abstract: Identifying the space-time variability of regional ecosystem health along with the influencing pathways of its driving factors is significant for ecosystem management and restoration. The ecosystem health level of the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2022 was assessed using the Vigor-Organization-Resilience-Services (VORS) model, and its dynamic evolution characteristics were analyzed from overall and sub-regional perspectives ( mountains and plains). The influencing pathways of human activity and natural factors on ecosystem health were analyzed by Partial Least Squares-Structural Equation Modeling (PLS-SEM), and the major driving factors of ecosystem health were identified by the optimal parametric geographical detector model. The results indicated that from 2000 to 2022, the ecosystem health showed a positive trend in the BTH region. In mountainous areas, it increased continuously, and in plains, it first decreased and then increased. The spatial distribution showed higher health in mountainous areas compared to plains, with significant improvements in the northern and western parts of the mountainous areas. The negative impacts of human activities on ecosystem health exceeded the positive impacts of natural factors. In overall and mountainous areas, topography and vegetation cover had high positive impacts, landscape composition had significant direct negative impacts, and socio-economic factors had indirect negative impacts. In plain areas, the landscape composition, topography, and vegetation cover had high direct positive impacts, and socio-economic factors caused significant negative impacts. The single factor analysis showed that the percentage of forested and constructed land, slope, and elevation were the main driving factors of overall ecosystem health. The percentage of forest land, cropland, and constructed land, and normalized difference vegetation index (NDVI) were dominant factors in mountains, while the percentage of constructed land, NDVI, and nighttime lighting were the main driving factors in plains. In the future, based on the driving factors of ecosystem health in mountains and plains, regional policies should be adopted to strengthen the implementation of ecological protection policies in mountains, and optimize land use and vegetation cover in plains, thus achieving ecologically sustainable development in the region.

     

/

返回文章
返回