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Open AccessArticle

Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

by 1,†, 1,*,†, 2,3,†, 4, 1, 1 and 2
1
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
3
UK Centre for Ecology & Hydrology, Library Avenue, Lancaster LA1 4AP, UK
4
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2021, 13(2), 212; https://doi.org/10.3390/rs13020212
Received: 16 November 2020 / Revised: 6 January 2021 / Accepted: 7 January 2021 / Published: 9 January 2021
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 R2 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). View Full-Text
Keywords: impervious surface; random forest; feature selection; Asia; multi-temporal data impervious surface; random forest; feature selection; Asia; multi-temporal data
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MDPI and ACS Style

Li, F.; Li, E.; Zhang, C.; Samat, A.; Liu, W.; Li, C.; Atkinson, P.M. Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data. Remote Sens. 2021, 13, 212. https://doi.org/10.3390/rs13020212

AMA Style

Li F, Li E, Zhang C, Samat A, Liu W, Li C, Atkinson PM. Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data. Remote Sensing. 2021; 13(2):212. https://doi.org/10.3390/rs13020212

Chicago/Turabian Style

Li, Fanggang; Li, Erzhu; Zhang, Ce; Samat, Alim; Liu, Wei; Li, Chunmei; Atkinson, Peter M. 2021. "Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data" Remote Sens. 13, no. 2: 212. https://doi.org/10.3390/rs13020212

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