Building density map (BDM) product derived from remotely sensed imagery over large areas is the vital data set for supporting studies of human settlement environment and land use planning. GHSL (global human settlement layer) is a mapping project for developing human settlement map product with remotely sensed data. It is sponsored by the Joint Research Center of European Commission. The BDM is an important component of GHSL. Currently, the BDM of GHSL has been validated over Europe and America. However, the shapes of buildings and the landscape patterns of urban areas in East Asia, especially in China, are significantly different from those in the EU and US, and the validation of BDM over Asia is vital for further applications of the GHSL products. This study takes large area individual building footprint data sets over 5 typical mega cities (Shanghai, Xi'an, Changchun, Kunming and Urumqi) as ground truth through the pixel aggregation and the neighboring points searching and location matching, and the ground truth map of the building density is produced as the validation data set. With regression analysis, the method for BDM assessment is explored by calculating accuracy indicators over large metro-areas in China. The results show that the GHSL building density products of built-up areas reflect the densities of buildings in China at lower resolutions, but at higher resolutions they overestimate the densities in high-value areas. This study provides a basis for research and development of built-up area density products in China.
WANG Shuai
,
QIN Yuchu
,
YANG Fukun
. Validation for building density remote sensing products of built-up areas at large scale[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(1)
: 83
-92
.
DOI: 10.7523/j.issn.2095-6134.2020.01.010
[1] Ban Y F,Gong P,Gini C. Global land cover mapping using Earth observation satellite data:recent progresses and challenges[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,103:1-6.
[2] Crocker-Buque T,Mindra G,Duncan R,et al. Immunization, urbanization and slums:a systematic review of factors and interventions[J]. BMC Public Health,2017,17(1):556.
[3] 杨朝斌,张树文,卜坤,等. 高分辨率遥感影像在城市LUCC中的应用[J]. 中国科学院大学学报,2016,33(3):289-297.
[4] 陈晋,卓莉,史培军,等. 基于DMSP/OLS数据的中国城市化过程研究:反映区域城市化水平的灯光指数的构建[J]. 遥感学报,2003,7(3):168-175.
[5] Pesaresi M,Corbane C,Julea A,et al. Assessment of the added-value of Sentinel-2 for detecting built-up areas[J]. Remote Sensing,2016,8(4):299.
[6] Pesaresi M,Ehrlich D,Ferri S, et al. Global human settlement analysis for disaster risk reduction[J]. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,2015,XL-7/W3(7):837-843.
[7] Chrysoulakis N,Feigenwinter C,Triantakonstantis D,et al. A conceptual list of indicators for urban planning and management based on earth observation[J]. ISPRS International Journal of Geo-Information,2014, 3(3):980-1002.
[8] 何春阳,曹鑫,史培军,等. 基于Landsat 7 ETM+全色数据纹理和结构信息复合的城市建筑信息提取[J]. 武汉大学学报(信息科学版),2004,29(9):800-804.
[9] Godefroid S,Koedam N, Urban plant species patterns are highly driven by density and function of built-up areas[J]. Landscape Ecology,2007, 22(8):1227-1239.
[10] Pesaresi M,Syrris V,Julea A. Benchmarking of the symbolic machine learning classifier with state of the art image classification methods:application to remote sensing imagery[R]. EUR-Scientific and Technical Research Reports JRC97964,2015.
[11] Pesaresi M,Daniele E,Stefano,et al. Operating procedure for the production of the global human settlement layer from Landsat data of the epochs 1975, 1990, 2000, and 2014[R]. EUR-Scientific and Technical Research Reports JRC97705,2016.
[12] Eurostat. LUCAS, the EU's land use and land cover survey[M]. Luxembourg:The Publications Office of the European Union,2013.
[13] 王永刚,王振亚,王宏斌. 基于高分辨率遥感影像的建筑物轮廓信息提取[J]. 国土资源信息化,2008(5):7-11.
