Global Land Cover Assessment Using Spatial Uniformity Validation Dataset
Global Land Cover Assessment Using Spatial Uniformity Validation Dataset
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DOI：
10.3390/rs13152950
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Y. Ishii;K. Iwao;T. Kinoshita
中科院分区：
文献类型：
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作者：
Y. Ishii;K. Iwao;T. Kinoshita
Degree Confluence Project（DCP）是一个基于志愿者的验证数据集，包含全球土地覆盖图验证的有用信息。然而，使用DCP点作为土地覆盖图准确性评估的验证数据存在问题。虽然典型的全球土地覆盖图的分辨率是几百米到几公里，但DCP点只能保证几十米的面积，这可以通过地面照片来确认。因此，本研究的目的是建立一个土地覆被图验证数据集，增加了空间均匀性信息，使用卫星图像和DCP点。为此，我们设计了一种新的方法来半自动地保证DCP验证数据点在任何分辨率下的空间均匀性。该方法能够对验证数据进行一致性判断，用户准确度为0.954。此外，我们进行了现有的全球土地覆盖图的DCP验证数据的精度评估，保证空间均匀性，并发现不同的类和地区的趋势。
The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.