干旱区生态环境知识资源中心(Arid): Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

Arid

DOI	10.3390/rs13020212
	Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data
	Li, Fanggang; Li, Erzhu; Zhang, Ce; Samat, Alim; Liu, Wei; Li, Chunmei; Atkinson, Peter M.
通讯作者	Li, EZ (corresponding author), Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:2
英文摘要	Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R-2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas).
英文关键词	impervious surface random forest feature selection Asia multi-temporal data
类型	Article
语种	英语
开放获取类型	gold, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000611558800001
WOS关键词	TEMPORAL MIXTURE ANALYSIS ; REAL TIME DETECTION ; LARGE-SCALE ; ENDMEMBER VARIABILITY ; URBANIZATION DYNAMICS ; RESOLUTION IMAGERY ; BEETLE INFESTATION ; LANDSAT ; CLASSIFICATION ; FRAMEWORK
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	中国科学院新疆生态与地理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351474
作者单位	[Li, Fanggang; Li, Erzhu; Liu, Wei; Li, Chunmei] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China; [Zhang, Ce; Atkinson, Peter M.] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England; [Zhang, Ce] UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England; [Samat, Alim] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China
推荐引用方式 GB/T 7714	Li, Fanggang,Li, Erzhu,Zhang, Ce,et al. Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data[J]. 中国科学院新疆生态与地理研究所,2021,13(2).
APA	Li, Fanggang.,Li, Erzhu.,Zhang, Ce.,Samat, Alim.,Liu, Wei.,...&Atkinson, Peter M..(2021).Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data.REMOTE SENSING,13(2).
MLA	Li, Fanggang,et al."Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data".REMOTE SENSING 13.2(2021).