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黄河流域多源遥感土地覆被数据精度评价与一致性分析

吴宗洋 蔡卓雅 郭英 王彦芳

吴宗洋, 蔡卓雅, 郭英, 王彦芳. 黄河流域多源遥感土地覆被数据精度评价与一致性分析[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−11 doi: 10.12357/cjea.20220816
引用本文: 吴宗洋, 蔡卓雅, 郭英, 王彦芳. 黄河流域多源遥感土地覆被数据精度评价与一致性分析[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−11 doi: 10.12357/cjea.20220816
WU Z Y, CAI Z Y, GUO Y, WANG Y F. Accuracy evaluation and consistency analysis on multi-source remote sensing land cover data in the Yellow River Basin[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−11 doi: 10.12357/cjea.20220816
Citation: WU Z Y, CAI Z Y, GUO Y, WANG Y F. Accuracy evaluation and consistency analysis on multi-source remote sensing land cover data in the Yellow River Basin[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−11 doi: 10.12357/cjea.20220816

黄河流域多源遥感土地覆被数据精度评价与一致性分析

doi: 10.12357/cjea.20220816
基金项目: 国家自然科学基金重大专项(42041007-02)、河北省高校基本科研业务费资助“河北地质大学科技创新团队项目”(KJCXTD-2021-03)和河北省省级科技计划软科学研究专项(21557401D)资助
详细信息
    作者简介:

    吴宗洋, 主要研究方向为环境遥感与应用。E-mail: 2213588801@qq.com

    通讯作者:

    王彦芳, 主要研究方向为农业资源遥感。E-mail: wangyanfang@hgu.edu.cn

  • 中图分类号: P237

Accuracy evaluation and consistency analysis on multi-source remote sensing land cover data in the Yellow River Basin

Funds: The study was supported by the National Natural Science Foundation of China (42041007-02), the Basic Scientific Research of Universities in Hebei Province for the Science and Technology Innovation Team Project of Hebei GEO University (KJCXTD-2021-03), the Soft Science Research Project of Science and Technology Plan of Hebei Province (21557401D).
More Information
  • 摘要: 开源、多分辨率、及时的土地覆盖产品为了解全球地表覆盖状况、陆面过程模型模拟以及社会经济发展决策等提供了丰富的数据支撑, 但多源的数据存在不同程度的不确定性, 在区域尺度如何选择合适的土地覆被产品成为应用中的难题。本研究以黄河流域为例, 对分辨率从30 m到1000 m的CLCD_v01_2020、GLOBELAND30、GLC_FCS30_2020、LANDCOVER(300 m)、MCD12Q1(500 m)和CNLUCC1000(1000 m)等6种2020年土地覆被产品进行区域尺度精度评价和一致性分析。基于Google Earth采集的1540个样本点分析6种数据在黄河流域的总体精度, 以最高精度的数据为参考对其他数据进行面积一致性分析, 并对6种数据进行类别混淆分析和混淆图谱分析。结果表明, 6种数据中分类精度最高的为CLCD_v01_2020, 总体精度(overall accuracy, OA)达88.12%; 其次是GLOBELAND30 (OA=85.32%)、GLC_FCS30_2020 (OA=84.09%)、LANDCOVER300 (OA=77.79%)、MCD12Q1 (OA=73.38%)、CNLUCC1000 (OA=71.82%), 30 m土地覆被产品的KAPPA系数均在0.8以上, 随着空间分辨率的下降, 分类精度下降。 6种数据的土地覆被类别组成的相对比例总体上趋于一致, 但在耕地和草地两类土地覆被类别上仍存在较大差异, GLC_FCS30_2020与参考数据CLCD_v01_2020的相关性最高, R2达到0.9976。通过类别混淆分析可知6种数据普遍对耕地、林地和草地的混淆较为严重。类别混淆空间分析表明, 验证数据与参考数据在黄河上游的草地, 中下游部分耕地和建设用地等类型较为单一的区域一致性较高, 而在陕西北部、山西西部的一致性较差, 主要表现为草地和林地的混淆。针对黄河流域土地覆被数据一级分类, 本研究建议, 30 m分辨率的数据中选择CLCD_v01_2020, 百米级分辨率数据中选择LANDCOVER300, 二级分类则可以根据所需的分类体系选择合适的数据。
  • 图  1  研究区概况图

    Figure  1.  Overview of the study area

    图  2  基于Google Earth的黄河流域样本点分布

    Figure  2.  Sample points distribution of the Yellow River basin based on Google Earth

    图  3  黄河流域土地覆被类别面积组成与面积偏差

    Figure  3.  Deviation of land cover category area composition and area in the Yellow River Basin

    图  4  多源遥感土地覆被类型混淆程度

    Figure  4.  Degree of confusion between multi-source remote sensing land cover types

    图  5  多源遥感产品与参考数据的类别混淆空间图谱

    图中R表示参考数据, V代表验证数据。In the figure, R represents the reference data and V represents the verification data

    Figure  5.  Multi-source remote sensing products confuse spatial atlases with the categories of reference data

    表  1  研究所用土地覆被产品的参数表

    Table  1.   Parameters of land cover products used in the study

    产品名称
    Product name
    来源
    Source
    分辨率
    Resolution (m)
    区域
    Region
    制图单位
    Cartographic organization
    分类方法
    Classification method
    传感器
    Sensor
    分类体系
    Classification system
    CLCD_v01_2020https://zenodo.org./30中国
    China
    武汉大学
    Wuhan University
    随机森林
    Random forest
    LandsatLCCS(9)
    GLOBELAND30https://GlobeLand30/30全球
    Global
    国家基础地理
    信息中心等
    National Basic Geographic Information Center, etc
    监督分类
    Supervise classification
    LandsatLCCS(10)
    GLC_FCS30_2020https://data.casearth.cn/30全球
    Global
    中国科学院空天信息创新研究院
    Institute of Aerospace Information Innovation, Chinese Academy of Sciences
    监督分类
    Supervise classification
    LandsatIGBP(29)
    LANDCOVERhttps://cds.climate.copernicus.eu/300全球
    Global
    欧洲航天局
    European Space Agency
    非监督分类
    Unsupervised classification
    MERIS
    PROBA-V
    Sentinel-3
    LCCS(36)
    MCD12Q1https://www.earthdata.nasa.gov/500全球
    Global
    波士顿大学
    Boston University
    监督分类
    Supervise classification
    MODISIGBP(17)
    CNLUCC1000https://www.resdc.cn/1000中国
    China
    中国科学院地理科学与资源研究所
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
    目视解译
    Visual interpretation
    LandsatLCCS(25)
    下载: 导出CSV

    表  2  研究所用土地覆被产品类别聚合表

    Table  2.   Aggregation table of land cover product categories used by the Institute ·

    产品名称
    Product name
    耕地
    Cropland
    林地
    Forest
    草地
    Grassland
    水域
    Waters
    冰川
    Glacier
    裸地
    Barren
    建设用地
    Construction land
    CLCD_v01_202012, 345, 9678
    GLOBELAND301020, 4030, 7050, 601009080
    GLC_FCS30_202010, 2012~12211, 130~153180, 210220200~202190
    CNLUCC100011, 1221~2431~3341~43, 45, 46, 64, 994461~63, 65~6751~53
    MCD12Q112, 141~78~1011, 17151613
    LANDCOVER10~3040~122, 170, 180130, 150210220200~202190
      表中的数字代表不同遥感产品原始分类体系类别代码。The numbers in the table represent the original classification system category codes for different remote sensing products.
    下载: 导出CSV

    表  3  不同遥感产品的精度结果

    Table  3.   Accuracy results of different remote sensing products %

    验证数据
    Validate data
    CLCD_v01_2020GLOBELAND30GLC_FCS30_2020LANDCOVER300MCD12Q1CNLUCC1000
    UAPAUAPAUAPAUAPAUAPAUAPA
    耕地 Cropland 92.38 96.48 87.03 99.44 93.28 82.22 80.74 89.26 84.99 93.33 79.40 83.52
    林地 Forest 97.42 90.10 95.56 80.89 95.99 89.76 93.88 89.08 99.48 65.19 93.45 73.04
    草地 Grassland 64.97 93.63 60.32 73.04 48.78 88.24 43.60 73.53 40.09 91.18 45.57 73.04
    水域 Waters 100.00 73.63 96.21 69.78 97.45 84.07 100.00 46.70 91.43 17.58 70.45 34.07
    冰川 Glacier 100.00 56.90 100.00 67.24 100.00 53.45 100.00 50.00 100.00 37.93 100.00 44.83
    裸地 Barren 81.61 67.62 71.13 65.71 90.36 71.43 76.00 36.19 62.79 51.43 55.74 64.76
    建设用地 Construction land 91.08 90.51 97.50 98.73 99.33 94.30 97.45 96.84 95.27 89.24 75.56 86.08
    OA 88.12 85.32 84.09 77.79 73.38 71.82
    KAPPA 84.91 81.22 80.10 71.54 66.06 64.30
      表中UA表示使用者精度, PA表示生产者精度, OA表示总体精度。In the table, UA represents consumer accuracy, PA represents producer accuracy, and OA represents overall accuracy.
    下载: 导出CSV
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  • 收稿日期:  2022-10-20
  • 录用日期:  2023-01-11
  • 修回日期:  2023-02-01
  • 网络出版日期:  2023-02-10

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