Spatial and temporal variation of vegetation NPP and analysis of influencing factors in Heilongjiang Province, China

Xi, Zhilong; Chen, Guizhen; Xing, Yanqiu; Xu, Huadong; Tian, Zeyu; Ma, Yan; Cui, Jing; Li, Dan

doi:10.1016/j.ecolind.2023.110798

Cited by 16 publications

(1 citation statement)

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“…The coefficient of variation (CV) [38] is a method for measuring the degree of dispersion of NPP at the pixel scale. This study uses CV to evaluate the stability of the SDHP NPP in time series and calculates Formula (5):…”

Section: Analysis Of Trend and Coefficient Of Variationmentioning

confidence: 99%

The Impact of Climate Change and Human Activities on the Spatial and Temporal Variations of Vegetation NPP in the Hilly-Plain Region of Shandong Province, China

Wu,

Yang,

et al. 2024

Forests

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Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m−2·a−1. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m−2·a−1, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals.

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Section: Analysis Of Trend and Coefficient Of Variationmentioning

confidence: 99%