资源型城市矿区NPP的时空演变及影响路径研究：以攀枝花市为例

Spatial-temporal evolution and driving mechanisms of NPP in mining areas of resource-based cities: a case study of Panzhihua City

关键词:  植被净初级生产力  CASA模型  PLS-SEM模型  攀枝花矿区

Key words:  net primary productivity (NPP)  CASA model  PLS-SEM model  Panzhihua mining area

摘要:

矿产资源开发对区域碳循环和生态系统功能具有重要影响，植被净初级生产力（NPP）是表征植被生长状况和生态系统固碳能力的重要指标，对矿区生态修复评价及生态安全保障具有重要意义。文章以资源型城市攀枝花市为研究对象，基于2000—2023年遥感影像、气象数据及地理空间数据，利用Carnegie-Ames-Stanford Approach（CASA）模型估算30m分辨率NPP，并结合Sen趋势分析、Mann-Kendall显著性检验、变异系数分析、偏相关分析和偏最小二乘结构方程模型（PLS-SEM），系统分析植被NPP的时空演变特征及驱动机制。结果表明：研究区NPP空间分布具有明显的地带性特征，总体呈北高南低的空间分布特征，以800—1200 g/(m²·a)（以碳计）分布为主，高值区主要集中于北部森林；总量呈波动上升趋势，在2023年达到峰值。但矿区核心区仍存在显著下降区域，矿区NPP呈现“先降后升”的阶段性变化特征，采矿扰动具有明显的空间梯度效应，1 km缓冲区受扰动显著，2 km干扰减弱。偏相关分析表明，NPP对气温和降水的响应具有显著空间异质性，其中降水对植被生产力的影响总体强于气温。PLS-SEM结果进一步表明，归一化植被指数（NDVI）始终是影响NPP空间分异的正向主导因子，其总正效应维持在0.766—0.842；地形总体表现为正向作用，而气候和人类活动总体表现为负向影响。研究可为矿区生态修复、生态保护分区管理及资源型城市绿色发展提供科学依据。未来应强化矿区及高扰动区的长期监测与差异化修复管理，促进生态系统功能稳定恢复。

Abstract:

Background, aim, and scope Mineral resource exploitation has profound impacts on regional carbon cycling and ecosystem functioning. Net primary productivity (NPP), an important indicator of vegetation growth and ecosystem carbon sequestration capacity, is widely used to evaluate ecological restoration in mining areas. This study focused on Panzhihua City and its typical mining areas to reveal the spatiotemporal dynamics of vegetation NPP from 2000 to 2023, identify the spatial extent of mining disturbances, and explore the driving mechanisms of natural and anthropogenic factors on the spatial differentiation of NPP. Materials and methods Based on remote sensing images, meteorological data, and geospatial datasets from 2000 to 2023, annual NPP at a spatial resolution of 30 m was estimated using the Carnegie-Ames-Stanford Approach (CASA) model. Sen's slope estimator, the Mann-Kendall significance test, coefficient of variation analysis, and partial correlation analysis were employed to characterize NPP trends, stability, and relationships with climatic factors. A partial least squares structural equation model (PLS-SEM) was constructed to quantify the direct and indirect effects of topography, climate, human activities, and the normalized difference vegetation index (NDVI) on the spatial differentiation of NPP. Results From 2000 to 2023, NPP exhibited an overall fluctuating increasing trend. Spatially, higher NPP values were mainly distributed in the northern part of the study area, whereas lower values occurred in the south, with 800—1200 g/(m²·a) (calculated as carbon, same below) representing the dominant NPP class. The pixel-scale NPP change rate ranged from −40.33 g/(m²·a) to 31.02 g/(m²·a). NPP in mining areas showed a two-stage pattern characterized by an initial decline followed by recovery. Buffer analysis revealed a distinct spatial gradient of mining disturbance, with the strongest impacts occurring in areas adjacent to mines. Partial correlation analysis showed that precipitation exerted a stronger influence on NPP than temperature. The PLS-SEM results showed that NDVI was consistently the dominant positive driving factor for NPP, with a total effect ranging from 0.766 to 0.842, whereas topography showed an overall positive effect, while climate and human activities generally exerted negative effects. Discussion The evolution of NPP in Panzhihua reflects the dynamic ecological response of mining ecosystems from disturbance to restoration. The overall recovery of vegetation productivity was mainly associated with ecological restoration projects and vegetation rehabilitation, whereas local mining areas remained under continuous disturbance from mining activities. The spatial gradient identified by the buffer analysis effectively captured the extent of mining impacts on surrounding ecosystems. The combined effects of natural environmental conditions and human activities jointly regulated NPP dynamics, among which improvements in vegetation cover played a key role in ecosystem recovery. Conclusions Vegetation productivity in the study area generally exhibited a recovery trend, although ecological restoration remained spatially heterogeneous across mining areas. Mining activities imposed significant spatially heterogeneous impacts on vegetation productivity. NDVI was identified as the dominant driver of NPP spatial differentiation, while topography, climate, and human activities jointly influenced vegetation productivity through direct and indirect pathways. Recommendations and perspectives Long-term monitoring of vegetation productivity in mining areas and their surrounding regions should be strengthened. Differentiated ecological restoration strategies should be implemented according to mining type and disturbance intensity. Future studies should integrate high-spatial- and high-temporal-resolution remote sensing data with multi-source environmental variables to further elucidate the mechanisms by which mining disturbances affect ecosystem functioning, thereby providing scientific support for ecological restoration assessment, territorial spatial planning, and sustainable development of resource-based cities.