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    多源遥感数据和Boruta算法支撑下山东省植被碳利用率时空动态监测及驱动力分析

    Monitoring and driving factor analysis of the spatiotemporal dynamics of vegetation carbon use efficiency in Shandong Province based on multi-source remote sensing data and the Boruta algorithm

    • 摘要: 植被碳利用率(carbon use efficiency, CUE)是量化生物固碳能力的重要参数, 定量评估CUE的时空格局变化及驱动因素, 对区域生态和生产安全等具有重要的指导意义。该文基于MODIS数据、高程数据、土壤类型数据、气象数据,采用Sen’s趋势分析、显著性检验、变异系数、Hurst指数、Boruta算法等时空数据分析方法,对山东省2001—2023年植被CUE时空变化特征、未来发展趋势及驱动因素进行分析。结果如下: ①从时间维度看,山东省2001—2023年植被CUE的年均值呈下降趋势,年际变化趋势系数为-0.000 3,表明研究期内植被CUE变化幅度较小、总体较为平缓; ②从空间维度看,2001—2023年山东省植被CUE的均值为0.51,呈现自西向东逐渐升高的趋势, 植被CUE均值高值区域位于山东省东部的威海市、烟台市、日照市,低值则聚集在聊城市、德州市、菏泽市, 山东省52.83%的区域植被CUE呈下降趋势,39.18%的区域呈上升趋势; ③未来变化特征上,山东省57.12%区域的植被CUE值处于改善趋势,42.88%的区域处于退化趋势; ④驱动因素分析发现,降水和气温是影响山东省植被CUE变化的主要因素,其中降水总体略强于气温,土壤类型与高程次之,坡度和坡向的因素影响相对较弱。

       

      Abstract: The vegetation carbon use efficiency (CUE) acts as a crucial parameter for quantifying the carbon biosequestration capacity. Quantitatively assessing the spatiotemporal change patterns of vegetation CUE and their driving factors will provide critical guidance for regional ecological and production security. This study investigated the spatiotemporal change patterns of vegetation CUE in Shandong Province from 2001 to 2023 and analyzed the future change trends and driving factors. To this end, a series of spatiotemporal data were used, including the moderate resolution imaging spectroradiometer (MODIS) data, digital elevation model (DEM) data, soil type data, and meteorological data. Associated data analysis techniques included the Sen's slope estimator, significance test, coefficient of variation, Hurst exponent, and Boruta algorithm. The results indicate that, temporally, Shandong Province exhibited a downward trend in average annual vegetation CUE from 2001 to 2023, with an interannual change trend coefficient of -0.000 3, suggesting slight changes and an overall stable trend over the study period. Spatially, the vegetation CUE averaged 0.51, increasing progressively from west to east. Specifically, high mean CUE values were concentrated in Weihai, Yantai, and Rizhao in eastern Shandong, while low mean values were observed in Liaocheng, Dezhou, and Heze. Across Shandong Province, 52.83% of areas exhibited a downward trend in vegetation CUE, while 39.18% showed an upward trend. Regarding the future change trend, the vegetation CUE is expected to increase in 57.12% of areas, as opposed to a downward trend in 42.88%. These results indicate that the future environment in Shandong Province tends to improve in general. Analysis of driving factors reveals that precipitation and temperature are the primary factors influencing vegetation CUE changes in Shandong Province, with precipitation exerting a slightly stronger overall influence than air temperature, followed by soil type and elevation, while slope and aspect have relatively weaker effects.

       

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