摘要
土地覆盖数据是全球环境变化相关研究和应用的重要数据基础,在诸多领域中被广泛运用。FROM-GLC30 2017数据是最新的全球高分辨率(30 m)公开土地覆盖数据集之一。土地覆盖数据集的精度是其在不同领域应用中的重要问题。本研究旨在探讨FROM-GLC30 2017数据集精度在全国范围内的空间分布,并比较不同土地覆盖类型精度的区域差异。本研究以亚米级高分辨率遥感影像为基础,对布设在中国范围内的6434个小流域抽样单元进行了人工目视解译,获得了土地覆盖参考数据。通过野外实地调查验证了参考数据,在此基础上对FROM-GLC30 2017数据集进行了精度评价。研究结果表明:(1) FROM-GLC30 2017数据集中各土地覆盖类型的面积比例基本符合我国的实际情况;(2)数据集在中国的总体精度为75.39%,在7大地理分区中,华东地区的总体精度最高,华南地区的总体精度最低;(3)在7种土地覆盖类型中,裸地、森林以及农田的精度相对较高,灌丛的精度最低。研究结果可为大区域土地覆盖数据精度评价研究提供理论支持,促进公开土地覆盖数据集的有效应用。
Land cover data play an important role in global environment change studies and applications and are widely used in many fields. The FROM-GLC30 2017 dataset is one of the latest global high-resolution(30-meter) land cover datasets. The accuracy of this land cover dataset is of great interest and important for its application in other fields. The aim of this study was to evaluate the spatial accuracy of the FROM-GLC30 2017 dataset at the national scale and analyze the spatial variation of its accuracy for different land cover types. In our study, the reference land cover data were obtained through visual interpretation based on sub-meter highresolution remote sensing images collected from 6434 small watersheds in China. The reference dataset was validated by field survey. Based on this, the accuracy of the FROM-GLC30 2017 dataset was further assessed.Our results show that:(1) the area proportion of each land cover type of the FROM-GLC30 2017 dataset generally matched the real field condition in China;(2) the overall accuracy of this dataset in China was 75.39%. Among the seven geographical divisions, the overall accuracy in east China was the highest, and the south China has the lowest accuracy;and(3) the accuracy of the bare land, forest, and cropland was relatively high, and the accuracy of the shrubland was the lowest among the seven land cover types. Our results provide theoretical support for large-scale land cover data accuracy assessment and promote the application of free land cover datasets.
作者
郭紫甜
王春梅
刘欣
庞国伟
朱梦阳
王晋卿
GUO Zitian;WANG Chunmei;LIU Xin;PANG Guowei;ZHU Mengyang;WANG Jinqing(Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,Northwest University,Xi'an 710127,China;College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China;Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions,Xi'an 710048,China)
出处
《地球信息科学学报》
CSCD
北大核心
2021年第3期524-535,共12页
Journal of Geo-information Science
基金
中国科学院战略性先导科技专项(A类)(XDA20040202)
国家自然科学基金项目(41977062、41601290)
国家大学生创新创业训练计划项目(201910697042)。
