| 基于MODIS-NPP的我国东北地区植被NEP时空演变特征及驱动机制 |
| 摘要点击 813 全文点击 52 投稿时间：2025-06-07 修订日期：2025-09-09 |
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| 中文关键词 净生态系统生产力（NEP） 时空分异 驱动机制 岭回归模型 地理探测器 |
| 英文关键词 net ecosystem productivity （NEP） spatiotemporal differentiation drive mechanism ridge regression model Geodetector |
| DOI 10.13227/j.hjkx.202506081 |
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| 中文摘要 |
| 东北地区作为我国重要生态安全屏障与碳汇功能区，其植被净生态系统生产力（NEP）的时空演变对实现“双碳”目标具有重要意义. 本研究利用2001~2023年MODIS-NPP数据和气象数据计算NEP，结合Theil-Sen斜率、Mann-Kendall检验及Hurst指数，揭示NEP的时空动态及未来趋势，并通过岭回归模型和最优参数地理探测器解析其驱动机制. 结果表明：① 研究期内NEP均值达234.19 g·m-2，以3.43 g·（m2·a）-1的速率显著增长（P<0.05），2022年达峰值286.60 g·m-2；② 空间上NEP呈现“四周高、中间低”格局，大兴安岭、长白山等林区为高值区；③ 未来79.31%区域的NEP将持续增长（H=0.52）；④人口密度对NEP时间变化的相对贡献率最大（27.68%），其次是降水量（24.65%）；各因子正负贡献量均集中分布于-0.01~0.015 g·（m2·a）-1区间，土地利用、人口密度和降水量主要呈现正向作用，平均温度和最高温度的绝对贡献量呈现负向作用； ⑤多因子耦合分析表明，土地利用类型对植被NEP空间分异的解释能力最高，而降水量与最高温度的双因子交互作用对NEP空间异质性解释能力最高，凸显自然条件与人类活动的协同影响. 研究为区域碳汇管理及生态修复提供科学依据，对当地资源开发利用和碳交易具有重要意义. |
| 英文摘要 |
| As a crucial ecological security barrier and carbon sink functional zone in China， the spatiotemporal evolution of vegetation net ecosystem productivity （NEP） in Northeast China holds significant implications for achieving the “dual-carbon” goals. In this study， NEP was calculated using MODIS-NPP data and meteorological data from 2001 to 2023. Combined with the Theil-Sen slope， Mann-Kendall test， and Hurst index， the spatiotemporal dynamics and future trends of NEP were revealed. Additionally， its driving mechanisms were analyzed using a ridge regression model and optimal parameter Geodetector. The results showed that： ① During the study period， the average NEP reached 234.19 g·m-2， with a significant increase at a rate of 3.43 g·（m2·a）-1 （P<0.05）， peaking at 286.60 g·m-2 in 2022. ② Spatially， NEP presented a pattern of “high in the surrounding areas and low in the central region，” with forested areas such as the Greater Khingan Range and Changbai Mountains being high-value regions. ③ In the future， NEP will continue to increase in 79.31% of the study area （H=0.52）. ④ Population density had the largest relative contribution rate to the temporal variation of NEP （27.68%）， followed by precipitation （24.65%）. The positive and negative contributions of all factors were concentrated in the range of -0.01 to 0.015 g·（m2·a）-1. Land use， population density， and precipitation mainly showed positive effects， while the absolute contributions of average temperature and maximum temperature were negative. ⑤ Multi-factor coupling analysis indicated that land use type had the highest explanatory power for the spatial differentiation of vegetation NEP， while the two-factor interaction between precipitation and maximum temperature had the highest explanatory power for the spatial heterogeneity of NEP， highlighting the synergistic influence of natural conditions and human activities. This study provides a scientific basis for regional carbon sink management and ecological restoration and is of great significance for local resource development and utilization as well as carbon trading. |
