文献类型： 外文期刊

第一作者： Hu, Tianyu

作者： Hu, Tianyu;Cao, Mengqi;Zhao, Xiaoxia;Liu, Xiaoqiang;Liu, Zhonghua;Huang, Zhengying;Chen, Leiyi;Liu, Guofang;Song, Chuangye;Yang, Xuejun;Ye, Xuehua;Liu, Lingli;Su, Yanjun;Hu, Tianyu;Cao, Mengqi;Zhao, Xiaoxia;Liu, Xiaoqiang;Liu, Zhonghua;Huang, Zhengying;Chen, Leiyi;Liu, Guofang;Song, Chuangye;Yang, Xuejun;Ye, Xuehua;Zhang, Shuren;Liu, Lingli;Su, Yanjun;Hu, Tianyu;Cao, Mengqi;Zhao, Xiaoxia;Liu, Xiaoqiang;Liu, Zhonghua;Su, Yanjun;Liu, Liangyun;Li, Liping;Tao, Shengli;Tang, Zhiyao;Guo, Yanpei;Ji, Chengjun;Zheng, Chengyang;Fang, Jingyun;Tao, Shengli;Tang, Zhiyao;Guo, Yanpei;Ji, Chengjun;Zheng, Chengyang;Fang, Jingyun;Guo, Yanpei;Wang, Guoyan;Hu, Xiaokang;Zhou, Luhong;Cheng, Yunxiang;Ma, Wenhong;Wang, Yonghui;Cheng, Yunxiang;Ma, Wenhong;Wang, Yonghui;Zhang, Pujin;Fan, Yuejun;Yu, Feihai;Wang, Zhong;Qiao, Xiujuan;Cheng, Xiaoli;Yin, Chunying;Yin, Chunying;Ma, Hongyuan;Yang, Yan;Luo, Wanyin;Gong, Yanming;Wang, Lei;Zhang, Shuren;Zhu, Xiangyun;Wang, Qinggang;Li, Shijin;Yang, Yongchuan;Kelly, Maggi;Kelly, Maggi

作者机构：

关键词： Grassland; Canopy cover; Drone imagery; Machine learning; Scale mismatch

期刊名称：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING （ 影响因子：12.2； 五年影响因子：13.7 ）

ISSN： 0924-2716

年卷期： 2024 年 218 卷

页码：

收录情况： SCI

摘要： Canopy cover is a crucial indicator for assessing grassland health and ecosystem services. However, achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. In this study, we addressed these challenges by proposing a regression-based approach to estimate large-scale grassland canopy cover, leveraging the integration of drone imagery and multisource remote sensing data. Specifically, over 90,000 10 x 10 m drone image tiles were collected at 1,255 sites across China. All drone image tiles were classified into grass and non-grass pixels to generate ground-truth canopy cover estimates. These estimates were then temporally aligned with satellite imagery-derived features to build a random forest regression model to map the grassland canopy cover distribution of China. Our results revealed that a single classification model can effectively distinguish between grass and non-grass pixels in drone images collected across diverse grassland types and large spatial scales, with multilayer perceptron demonstrating superior classification accuracy compared to Canopeo, support vector machine, random forest, and pyramid scene parsing network. The integration of extensive drone imagery successfully addressed the scale-mismatch issue between traditional ground measurements and satellite imagery, contributing significantly to enhancing mapping accuracy. The national canopy cover map of China generated for the year 2021 exhibited a spatial pattern of increasing canopy cover from northwest to southeast, with an average value of 56 % and a standard deviation of 26 %. Moreover, it demonstrated high accuracy, with a coefficient of determination of 0.89 and a root-mean- squared error of 12.38 %. The resulting high-resolution canopy cover map of China holds great potential in advancing our comprehension of grassland ecosystem processes and advocating for the sustainable management of grassland resources.

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