{"title":"[Analysis of Driving Factors for Spatiotemporal Changes in Carbon Sources/Sinks in Taihang and Yanshan Mountains].","authors":"Peng-Fei Tian, Feng Yan, Zong-Chao Wang, Yuan-Hang Li, Ze-Hua Wen, Ya-Heng Chen","doi":"10.13227/j.hjkx.202406227","DOIUrl":null,"url":null,"abstract":"<p><p>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·(m<sup>2</sup>·a)<sup>-1</sup>. 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.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 7","pages":"4403-4415"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202406227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
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.