Refined land-cover classification mapping using a multi-scale transformation method from remote sensing, unmanned aerial vehicle, and field surveys in Sanjiangyuan National Park, China
Refined land-cover classification mapping using a multi-scale transformation method from remote sensing, unmanned aerial vehicle, and field surveys in Sanjiangyuan National Park, China
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使用遥感、无人机和实地调查的多尺度变换方法对中国三江源国家公园进行精细的土地覆盖分类制图
DOI：
10.1117/1.jrs.15.014513
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发表时间：
2021-01
影响因子：
1.7
通讯作者：
Li Hao
中科院分区：
文献类型：
--
作者：
Han Shuai;Meng Qingkai;Liu Haocheng;Peng Ying;Han Jianping;Jin Shenghong;Fan Shixiong;Xin Bingchang;He Lili;Li Hao
抽象的。精细土地覆盖分类图（RLCM）是评价生态变化和理解生态系统服务功能的基本策略。在RLCM生成过程中，普遍存在遥感数据与样方尺度不匹配的问题，导致分类结果不准确。以三江源国家公园为例，研究了遥感、无人机和野外调查相结合的多尺度转换方法。利用无人机对大量虚拟生物量样方进行重采样和插值，确定高寒草甸和草原不同植被覆盖度的定量阈值，提高土地覆盖分类精度。基于1990 - 2017年RLCM的时空分析，整个生态覆盖率正在变好，其驱动因素归因于政府政策和气候变化。本研究可为SNP的管理和可持续发展提供参考。
Abstract. Mapping the refined land-cover classification mapping (RLCM) is a primary and essential strategy for evaluating the ecological change and understanding the ecosystem services. A common problem during the generation of RLCM is a scale mismatch between remote sensing (RS) data and field quadrat, which leads to inaccuracy of the classification result. A multi-scale transformation method was developed via integrating RS, unmanned aerial vehicle (UAV), and field surveys in Sanjiangyuan National Park (SNP). With the help of UAV, a large number of virtual biomass quadrats were resampled and interpolated, and the quantitative thresholds of different vegetation coverage in alpine meadow and steppe were determined to improve land-cover classification accuracy. Based on the spatial-temporal analysis of RLCM from 1990 to 2017, the whole ecological coverage was becoming better, and its driving factor was attributed to government policy and climate change. This study can provide a practical suggestion for the management and sustainable development in SNP.
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DOI：
10.1007/bf03323761
发表时间：
2012-07
期刊：
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影响因子：
--
作者：
Jürgen Müller
通讯作者：
Jürgen Müller
影响因子：
4.8
作者：
Fan, Jiangwen;Zhong, Huaping;Yue, Yanzhen
通讯作者：
Yue, Yanzhen
影响因子：
13.5
作者：
D. Fawcett;D. Fawcett;W. Verhoef;D. Schläpfer;F. Schneider;M. Schaepman;A. Damm;A. Damm
通讯作者：
D. Fawcett;D. Fawcett;W. Verhoef;D. Schläpfer;F. Schneider;M. Schaepman;A. Damm;A. Damm
影响因子：
3.4
作者：
Bartholomé, E;Belward, AS
通讯作者：
Belward, AS
影响因子：
1.7
作者：
S. Jawak;A. J. Luis
通讯作者：
S. Jawak;A. J. Luis