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中国沙漠 ›› 2022, Vol. 42 ›› Issue (3): 187-195.DOI: 10.7522/j.issn.1000-694X.2021.00149

• • 上一篇    

基于多源遥感和机器学习方法的科尔沁沙地植被覆盖度反演

张亦然1(), 刘廷玺1,2(), 童新1,2, 段利民1,2, 贾天宇1, 季亚新1   

  1. 1.内蒙古农业大学,水利与土木建筑工程学院,内蒙古 呼和浩特 010018
    2.内蒙古农业大学,内蒙古自治区水资源保护与利用重点实验室,内蒙古 呼和浩特 010018
  • 收稿日期:2021-09-06 修回日期:2021-12-13 出版日期:2022-05-20 发布日期:2022-06-01
  • 通讯作者: 刘廷玺
  • 作者简介:刘廷玺(E-mail: txliu1966@163.com
    张亦然(1996—),女,内蒙古巴彦淖尔人,硕士研究生,主要从事生态环境遥感研究。E-mail: 534805685@qq.com
  • 基金资助:
    国家自然科学基金项目(51620105003);内蒙古自然科学基金项目(2018ZD05);教育部创新团队发展计划项目(IRT_17R60);科技部重点领域科技创新团队(2015RA4013);内蒙古自治区草原英才产业创新创业人才团队项目;内蒙古农业大学寒旱区水资源利用创新团队项目(NDTD2010-6)

Inversion of vegetation coverage based on multi-source remote sensing data and machine learning method in the Horqin Sandy LandChina

Yiran Zhang1(), Tingxi Liu1,2(), Xin Tong1,2, Limin Duan1,2, Tianyu Jia1, Yaxin Ji1   

  1. 1.College of Water Conservancy and Civil Engineering /, Inner Mongolia Agricultural University,Hohhot 010018,China
    2.Inner Mongolia Key Laboratory of Protection and Utilization of Water Resource, Inner Mongolia Agricultural University,Hohhot 010018,China
  • Received:2021-09-06 Revised:2021-12-13 Online:2022-05-20 Published:2022-06-01
  • Contact: Tingxi Liu

摘要:

植被覆盖度是监测生态系统及其功能的关键参数,如何提高大区域植被覆盖度的反演精度,对生态脆弱区环境可持续发展至关重要。本研究基于人工神经网络、支持向量回归和随机森林等机器学习方法,利用无人机、Worldview-2与Landsat 8 OLI遥感数据,对科尔沁沙地植被覆盖度进行多尺度反演。结果表明:随机森林模型比人工神经网络、支持向量回归模型表现佳,可在单元(试验区)、区域(研究区)尺度上较高精度地反演沙地的植被覆盖度,反演值与无人机实测值均在线性水平上呈显著相关(P<0.01);在单元、区域尺度上,构建的植被覆盖度反演模型测试集R2分别为0.84、0.80,MSE分别为0.0145、0.0370,一致性指数d分别为0.9576、0.8991。利用多源遥感数据和机器学习方法,通过局部区域的高精度反演逐步实现低空间分辨率遥感影像的大区域植被覆盖度反演,不仅可有效提高沙地植被覆盖度的反演精度(R2=0.78,大于0.63),也为区域生态环境监测与生态系统健康评价提供支持。

关键词: 植被覆盖度, 多源遥感, 机器学习, 科尔沁沙地

Abstract:

Fractional vegetation coverage is a key parameter for monitoring ecosystems and its functions. How to improve the retrieval accuracy of fractional vegetation coverage in large areas is very important for the sustainable development of the environment in ecologically fragile areas. Based on machine learning methods such as back propagation neural network (BP-ANN), support vector regression (SVR) and random forest (RF), this study uses UAV, Worldview-2 and Landsat 8 OLI remote sensing data to carry out multi-scale inversion of fractional vegetation coverage in Horqin Sandy Land. The results show that: (1) The RF model performs better than the BP-ANN and SVR model. It can invert the vegetation coverage of sandy land with higher accuracy on the scale of unit (experimental area) and regional (research area). There was a significant correlation between the inversion value and the measured value of UAV at the linear level (P is less than 0.01). (2) On the unit and regional scales, the test set of the constructed vegetation coverage inversion model are R2 of 0.84 and 0.80, MSE of 0.0145 and 0.0370, and the consistency index d of 0.9576 and 0.8991 respectively. The method of gradually realizing the large area fractional vegetation coverage inversion of low spatial resolution remote sensing images by using multi-source remote sensing data and machine learning method, which can not only effectively improve the inversion accuracy of fraction vegetation coverage in sandy land (R2=0.78, less than 0.63), but also provide support for regional ecological environment monitoring and ecosystem health assessment.

Key words: fractional vegetation cover, multi-source remote sensing, machine learning method, Horqin Sandy Land

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