联合多源遥感数据的黄土高原(甘肃区) 森林覆盖变化及驱动力分析
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兰州理工大学土木工程学院

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P237

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国家自然科学基金(42261069),兰州理工大学学生科技创新(kcjj2362)


Analysis of Forest Cover Changes and Driving Forces in the Loess Plateau ( Gansu Region) Based on Multisensor Remote Sensing Images
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Lanzhou University of Technology

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    摘要:

    黄土高原作为我国西部地区的自然生态屏障,为国家可持续发展做出了积极贡献。黄土高原(甘肃区)生态环境的治理与修复对我国生态文明建设战略的实施具有关键作用,为监测2008-2018年黄土高原(甘肃区)森林资源变化情况,基于Google Earth Engine(GEE)云平台,融合Landsat、PALSAR及地形数据,探究光谱指数、后向散射、纹理及地形特征在森林资源信息获取方面的优势,运用随机森林特征优选算法获取研究区10年间森林覆盖时空分布并基于地理探测器进行因子探测。结果表明:随机森林特征优选算法可以有效筛选特征重要信息,总体精度可达91.88%,Kappa系数0.91。融合Landsat、PALSAR及地形数据的实验方案精度明显高于使用单一数据源的森林分类结果,四期分类结果总体精度分别为86.65%、88.23%、90.15%、89.86%。10年间研究区森林面积净增加0.60×104 km2;森林增加的区域主要分布于庆阳市中东部、平凉市东部、天水市中部和临夏回族自治州西部地区,而森林退化主要出现在定西市西南部和临夏回族自治州中东部地区。单因子探测中土地利用类型(X10)是森林覆盖变化的主导因子,适宜性的土壤类型(X9)的空间分布和降雨量(X5)的辅助作用,为植树造林成活率和森林健康生长提供了良好的自然条件。

    Abstract:

    The Loess Plateau, as a natural ecological barrier in the western region of China, has made positive contributions to the sustainable development of the nation. The governance and restoration of the ecological environment on the Loess Plateau (Gansu Region) plays a critical role in the implementation of China's ecological civilization construction strategy. To monitor the changes in forest resources on the Loess Plateau (Gansu Region) from 2008 to 2018, based on the Google Earth Engine (GEE) cloud platform, Landsat, PALSAR, and terrain data were integrated to explore the advantages of spectral index, backscatter, texture, and terrain features in obtaining forest resource information. The random forest feature selection algorithm was utilized to obtain the spatiotemporal distribution of forest cover in the study area for 10 years, and factor detection was conducted using geographic detectors. The results indicate that the random forest feature selection algorithm can effectively screen important feature information, with an overall accuracy of 91.88% and a Kappa coefficient of 0.91. The experimental scheme that integrates Landsat, PALSAR, and terrain data presents significantly higher accuracy compared to the forest classification results using a single data source. The overall accuracy of the four classification results is 86.65%, 88.23%, 90.15%, and 89.86% respectively. Over the past 10 years, the net increase in forest area in the study area was 0.60×104 km2; The areas with increased forests are primarily distributed in the central and eastern parts of Qingyang City, Pingliang City, Tianshui City, and the western region of Linxia Hui Autonomous Prefecture, while forest degradation primarily occurs in the southwestern part of Dingxi City and the central and eastern areas of Linxia Hui Autonomous Prefecture. In single-factor detection, land use type (X10) is the dominant factor in forest cover change, and the spatial distribution of suitable soil type (X9) and the auxiliary effect of rainfall (X5) provide favorable natural conditions for the survival rate of afforestation and the healthy growth of forests.

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刘博,牛全福,王刚,等. 联合多源遥感数据的黄土高原(甘肃区) 森林覆盖变化及驱动力分析[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-03-26
  • 最后修改日期:2024-04-23
  • 录用日期:2024-05-02
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