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    杨灿, 魏天兴, 李亦然, 郑粮, 陈宇轩. 黄土高原水蚀风蚀交错区退耕还林工程前后NDVI时空变化特征[J]. 北京林业大学学报, 2021, 43(6): 83-91. DOI: 10.12171/j.1000-1522.20210128
    引用本文: 杨灿, 魏天兴, 李亦然, 郑粮, 陈宇轩. 黄土高原水蚀风蚀交错区退耕还林工程前后NDVI时空变化特征[J]. 北京林业大学学报, 2021, 43(6): 83-91. DOI: 10.12171/j.1000-1522.20210128
    Yang Can, Wei Tianxing, Li Yiran, Zheng Liang, Chen Yuxuan. Spatiotemporal variations of NDVI before and after implementation of Grain for Green Project in wind-water erosion crisscross region of the Loess Plateau[J]. Journal of Beijing Forestry University, 2021, 43(6): 83-91. DOI: 10.12171/j.1000-1522.20210128
    Citation: Yang Can, Wei Tianxing, Li Yiran, Zheng Liang, Chen Yuxuan. Spatiotemporal variations of NDVI before and after implementation of Grain for Green Project in wind-water erosion crisscross region of the Loess Plateau[J]. Journal of Beijing Forestry University, 2021, 43(6): 83-91. DOI: 10.12171/j.1000-1522.20210128

    黄土高原水蚀风蚀交错区退耕还林工程前后NDVI时空变化特征

    Spatiotemporal variations of NDVI before and after implementation of Grain for Green Project in wind-water erosion crisscross region of the Loess Plateau

    • 摘要: 【目的】探究黄土高原退耕还林工程实施前后植被覆盖变化特征,评估生态修复效果,对于制定更为有效的生态修复战略政策具有重要意义。【方法】该研究以黄土高原水蚀风蚀交错区为研究区,以EOT算法为核心,基于GIMMS NDVI数据集和MODIS NDVI数据集的重叠时期数据,构建1982—2000年1 km分辨率的NDVI数据集(EOT NDVI),并联合MODIS NDVI数据(2001—2019年),形成1982—2019年逐月1 km NDVI数据集(EM NDVI),以探究研究区的NDVI时空动态变化。【结果】(1)EOT算法对GIMMS NDVI降采样应用是合理的,同时期EOT NDVI与MODIS NDVI之间的平均误差(ME)、平均值绝对误差(MAE)、均方根误差(RMSE)均较小,且决定系数(R2)较高;(2)1982—2019年年均NDVI呈现先下降后上升趋势,年均NDVI增长速率为0.007 8;(3)1982—2019年植被增长速率呈现空间异质性,且退耕还林工程实施前(1982—2000年)年均NDVI呈下降趋势的地区是退耕还林工程实施后(2001—2019年)年均NDVI增加趋势显著的地区。【结论】研究成果可为评估生态恢复措施实施效果和未来生态环境建设提供理论依据和数据支撑。

       

      Abstract: To propose more effective strategies and policies for ecological restoration, it is imperative to explore the variations in vegetation coverage before and after the implementation of “Grain for Green Project” and evaluate the effect of ecological restoration in the Loess Plateau. In present research, the wind-water erosion crisscross region of the Loess Plateau was selected as study area. Based on the EOT algorithm, GIMMS NDVI data and MODIS NDVI data during overlapping period were applied to establish GIMMS NDVI data (EOT NDVI) from 1982 to 2000 with the resolution of 1 km. Then, a monthly NDVI data set (EM NDVI) from 1982 to 2019 with the resolution of 1 km was integrated with the EOT NDVI data and the MODIS NDVI data from 2001 to 2019. Consequently, the spatiotemporal variations in NDVI of the study area were analyzed. The results showed that: (1) EOT algorithm was suitable for the GIMMS NDVI resampling application in the study area. Mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of EOT NDVI and MODIS NDVI in the same period were relatively low but the coefficient of determination (R2) was relatively high. (2) In 1982−2019, the average annual NDVI of the study area showed a decline trend first but then increased with the average annual NDVI growth rate of 0.007 8. (3) In 1982−2019, heterogeneity was discovered in the spatial growth rate of vegetation. The area where the average annual NDVI had used to show a downward trend before the implementation of the project (1982−2000) turned to show a significant upward trend in the average annual NDVI after the implementation (2001−2019). The results of the study can provide theoretical basis and data support for assessing the implementation effect of ecological restoration measures and future ecological environment construction.

       

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