地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (11): 1563-1572.doi: 10.3724/SP.J.1047.2016.01563

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河南省GlobeLand30数据精度评价及对比分析

马京振(), 孙群, 肖强, 温伯威   

  1. 信息工程大学,郑州 450001
  • 收稿日期:2016-06-01 修回日期:2016-07-08 出版日期:2016-11-20 发布日期:2016-11-20
  • 作者简介:

    作者简介:马京振(1993-),男,硕士生,研究方向为多源数据融合与处理。E-mail: zb50mjz@163.com

  • 基金资助:
    国家自然科学基金项目(41571399、41071297)

Accuracy Assessment and Comparative Analysis of GlobeLand30 Dataset in Henan Province

MA Jingzhen*(), SUN Qun, XIAO Qiang, WEN Bowei   

  1. Information Engineering University, Zhengzhou 450001, China
  • Received:2016-06-01 Revised:2016-07-08 Online:2016-11-20 Published:2016-11-20
  • Contact: MA Jingzhen E-mail:zb50mjz@163.com

摘要:

全球地表覆盖数据在气候变化研究、地理国情监测、生态环境保护等方面发挥着重要作用,2014年中国国家基础地理信息中心推出了全球最高30 m分辨率的地表覆盖遥感制图数据产品GlobeLand30。本文以2010年1:10万中国土地利用数据为参考,采用空间统计、面积一致性以及误差矩阵等分析方法,对河南省GlobeLand30、GlobCover2009、MCD12Q1数据进行精度评价和对比分析研究,结果表明:① 3种地表覆盖数据对河南省土地构成的描述基本一致,即以耕地、林地为主,草地、水体和人造地表为辅,但分类土地面积存在大小不同的差异;② GlobeLand30的总体精度和Kappa系数最高,MCD12Q1次之,GlobCover2009最低。3种数据中耕地和林地的精度均较高,草地的精度较差,GlobeLand30中水体和人造地表的生产者精度远高于其他2种数据,使用者精度相差不大;③ 地表覆盖数据与参考数据在空间上存在类型混淆情况,混淆主要发生于林地、草地、水体、人造地表与耕地之间,GlobeLand30的混淆程度要低于其他2种数据。

关键词: 地表覆盖, GlobeLand30, 误差矩阵, 精度评估, 对比分析

Abstract:

Global land cover data plays an important role in climate change research, geographical conditions monitoring and ecological environment protection. It' s of great significance to produce and evaluate the global land cover data at a specific spatial scale. In 2014, the National Geomatics Center of China (NGCC) produced GlobeLand30 of the remote sensing mapping product with the world’s highest 30 m resolution. In this paper, the 1:100 000 land use data of Henan Province was used as the reference data to validate global land cover data of GlobeLand30, GlobCover2001 and MCD12Q1. The accuracy assessment and comparative analysis of these data were conducted with three methods, including spatial statistics, area relevance and consistency, and confusion matrix. The results show that the three land cover products have a good consistency for description of land forms with the reference data, and the area relevance is higher than 0.9. Cropland and forestland are the main land cover types, followed by grassland, water body and artificial surface, but the classified land has different area in these products. By evaluating accuracy of the three land cover products, the overall accuracy and Kappa coefficient of GlobeLand30 are the highest, followed by MCD12Q1 and those of GlobCover2009 are the lowest. In terms of specific land type, although cropland and forestland have high precision in these products, the accuracy of grassland classification is poor. The producer accuracy of water body and artificial surface in GlobeLand30 is much higher than the other two products, but the difference of the user accuracy is small. The three land cover products show the spatial confusion especially in forestland, grassland and cropland with the reference data. The confusion degree of GlobeLand30 is lower than the other two kinds of data. This paper illustrates that GlobeLand30 has higher accuracy than other products and it will play a more and more important role in many fields. Not only can the methods and conclusions in this paper pave the way for further research in other areas, but also they can have great significance on promoting the application and value of GlobeLand30. Moreover, because of the spatial resolution of GlobeLand30 is much higher than other land cover products, the use of GlobeLand30 for further application and research is the focus in the next step. In addition, there are a lot of remote sensing images, vector data, and other multi-source data and how to improve the quality of the global land cover data is one of the problems that should be considered.

Key words: land cover, GlobeLand30, error matrix, accuracy assessment, comparative analysis