| 太行山-燕山碳源/碳汇时空变化及驱动因素分析 |
| 摘要点击 148 全文点击 15 投稿时间：2024-06-23 修订日期：2024-08-22 |
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| 中文关键词 太行山 燕山 净生态系统生产力（NEP） 参数最优地理探测器（OPGD） 多尺度地理加权回归（MGWR） |
| 英文关键词 Taihang Mountains Yanshan Mountains net ecosystem productivity （NEP） optimal parameters-based geographical detector （OPGD） multi-scale geographic weighting regression（MGWR） |
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| 中文摘要 |
| 净生态系统生产力（NEP）作为生态系统极为重要的特征量，厘清其时空分异格局及驱动机制对太行山-燕山生态系统保护与修复具有重要意义. 基于MODIS遥感数据估算了净生态系统生产力（NEP），并结合自然和人类社会数据，采用趋势分析、Hurst指数、参数最优地理探测器和多尺度地理加权回归等方法，分析其时空变化特征及驱动因子对NEP时空变化的影响. 结果表明：①时间上，2002~2020年间太行山-燕山地区NEP整体呈现波动上升的趋势，增速为4.96 g·（m2·a）-1. 空间上，呈现出太行山地区“四周低，中间高”和燕山地区“北部高，南部低”的特点. ②因子探测结果表明，气温、GDP密度和高程是太行山-燕山NEP空间分异的主要影响因子. 各因子在交互作用后对NEP的解释程度远高于单因子作用，其中气温与植被覆盖度的交互作用大最大. ③各因子对NEP的作用存在显著差异. 其中，气温、GDP密度以及夜间灯光强度在整体上对NEP的作用是负向的；而植被覆盖度对NEP产生正向作用；降水、高程、坡度以及人口密度对NEP具有双向作用. |
| 英文摘要 |
| As an extremely important characteristic of ecosystems， clarifying the spatial and temporal patterns and driving mechanisms of net ecosystem productivity （NEP） is of great significance for the protection and restoration of the Taihang and Yanshan Mountains ecosystem. The net ecosystem productivity （NEP） was estimated using MODIS remote sensing data. By integrating natural and human-related data， we used methods such as trend analysis， Hurst exponent， optimal parameters-based geographical detector， and multi-scale geographically weighted regression to estimate regional NEP and analyzed its spatiotemporal variation characteristics and the impact of driving factors on this change. The results showed that： ① In terms of time， the NEP in the Taihang and Yanshan Mountains Region showed a fluctuating upward trend from 2002 to 2020， with a growth rate of 4.96 g·（m2·a）-1. In terms of space， the Taihang Mountains Region was characterized by “low surrounding areas and high central areas”， while the Yanshan Mountains Region was characterized by “high northern areas and low southern areas”. ② The factor detection results showed that temperature， GDP density， and elevation were the main influencing factors for the spatial differentiation of the Taihang and Yanshan Mountains NEP. The degree of explanation of NEP by each factor after an interaction was much higher than that of a single factor， and the interaction between temperature and fractional vegetation cover was the largest. ③ There were significant differences in the effects of various factors on NEP. Among them， temperature， GDP density， and nighttime light intensity had a negative impact on NEP as a whole； vegetation coverage had a positive effect on NEP； and precipitation， elevation， slope， and population density had bidirectional effects on NEP. |
