State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
PLoS One. 2020 May 29;15(5):e0233164. doi: 10.1371/journal.pone.0233164. eCollection 2020.
Building a density map over large areas could provide essential information of land development intensity and settlement condition. It is crucial for supporting studies and planning of human settlement environment. The Global Human Settlement Layer (GHSL) is a comprehensive data set of mapping human settlement at a global scale, which was produced by the Joint Research Centre (JRC), European Commission. The built-up density is an important layer of GHSL data set. Currently, the validation of the GHSL built-up area products was preliminarily conducted over the United States and European countries. However, as a typical East Asian region, China is quite different from the United States, Europe, and other regions in terms of building forms and urban layouts. Therefore, it is necessary to perform an accuracy assessment of GHSL data set in Asian countries like China. With individual building footprint data of 20 typical cities in China, this paper presents our effort to validate the GHSL built-up area products. The aggregation mean and neighborhood search based algorithms are adopted for matching building footprint data and the GHSL products, through the regression analysis at per-pixel level, the building density map in raster format are generated as validation data. The accuracy index of GHSL built-up area was calculated for the study areas, and the validation methods were explored for GHSL built-up products at large scale. The results show that the built-up layer aggregated by the building footprint have the highest correlation with the coarse resolution GHSL built-up products, but GHSL tends to underestimate the building density of low-density areas and overestimate the areas with high density. This study suggests that GHSL built-up area products in 20 representative Chinese cities of China could provide quantitative information about built-up areas, but the product accuracy still need to be improved in the regions with heterogeneous formations of human settlements like China. There is a big picture of mapping high accuracy built-up density of China with the training data set acquired by the study.
构建大面积的密度图可以提供土地开发强度和居民点状况的重要信息，这对于支持人类住区环境的研究和规划至关重要。全球人类住区层（GHSL）是一个全球性人类住区制图的综合数据集，由欧盟委员会联合研究中心（JRC）制作。建成区密度是 GHSL 数据集的一个重要图层。目前，已经对 GHSL 建成区产品在美国和欧洲国家进行了初步验证。然而，中国作为一个典型的东亚地区，在建筑形式和城市布局方面与美国、欧洲和其他地区有很大的不同。因此，有必要在中国等亚洲国家对 GHSL 数据集进行精度评估。本文利用中国 20 个典型城市的单体建筑足迹数据，对 GHSL 建成区产品进行验证。采用聚合均值和邻域搜索算法，通过逐像素的回归分析，将建筑足迹数据与 GHSL 产品进行匹配，生成栅格格式的建筑密度图作为验证数据。计算了研究区域内 GHSL 建成区面积的精度指标，并探讨了大规模 GHSL 建成区产品的验证方法。结果表明，基于建筑足迹聚合的建成区与低分辨率 GHSL 建成区产品相关性最高，但 GHSL 倾向于低估低密度地区的建筑密度，高估高密度地区的建筑密度。本研究表明，GHSL 建成区产品可以为中国 20 个有代表性的城市提供建成区的定量信息，但在像中国这样人类住区形态多样的地区，产品精度仍需提高。通过本研究获取的训练数据集，可以对中国高精度建成区密度进行大比例尺制图。
