A Novel Sample Selection Method for Impervious Surface Area Mapping Using JL1-3B Nighttime Light and Sentinel-2 Imagery

Urbanization has attracted wide and active interests due to the impact on regional sustainable development. As an important indicator of urbanization, impervious surface area (ISA) should be accurately monitored. In scenario of identifying ISA by supervised classification from satellite images, the training samples are usually labeled manually, which is highly labor-intensive and time-consuming. High-resolution nighttime light image provides a unique footprint of human activities and settlements which are strongly correlated with ISA. In view of this, a novel ISA training sample selection method is proposed by integrating the JL1-3B high-resolution nighttime light imagery and Sentinel-2 time series imagery, and the random forest is applied to classify ISA from Sentinel-2 imagery. The quality of the automatically selected samples was quantitatively validated. There were over three study areas, and the overall classification accuracies were above 97%, showing reliable and robust performance. Compared with conventional methods, the proposed approach achieves satisfactory results in separating bare land from ISA. This study provides a data fusion way which can automatically generate sufficient and high-quality training samples for ISA mapping, and suggests that high-resolution nighttime imagery could demonstrate a promising potential for urban remote sensing.

[1]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[2]  Ranga B. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[4]  Lorenzo Bruzzone,et al.  A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Hui Lin,et al.  A comparison study of impervious surfaces estimation using optical and SAR remote sensing images , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Xiao Xiang Zhu,et al.  Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network , 2019, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[7]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[8]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[9]  Feature Reduction , 2010, Encyclopedia of Machine Learning.

[10]  Bin Chen,et al.  Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.

[11]  Peter R. J. North,et al.  Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area , 2019, IEEE Geoscience and Remote Sensing Letters.

[12]  Qihao Weng,et al.  A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B , 2018, Remote Sensing of Environment.

[13]  Stuart R. Phinn,et al.  A new source for high spatial resolution night time images — The EROS-B commercial satellite , 2014 .

[14]  Lorenzo Bruzzone,et al.  An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection , 1995, IEEE Trans. Geosci. Remote. Sens..

[15]  Liang Cheng,et al.  Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data , 2019, Remote. Sens..

[16]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[17]  C. Elvidge,et al.  Remote sensing of night lights: A review and an outlook for the future , 2020, Remote Sensing of Environment.

[18]  Michael Jendryke,et al.  Mapping urban expansion using night-time light images from Luojia1-01 and International Space Station , 2020 .

[19]  O. Mutanga,et al.  Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry , 2009 .

[20]  Yuyu Zhou,et al.  Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States , 2018 .

[21]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[22]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[23]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[24]  Hashem Akbari,et al.  Effect of increasing urban albedo on meteorology and air quality of Montreal (Canada) – Episodic simulation of heat wave in 2005 , 2016 .

[25]  Tingting Shi,et al.  Derivation of Tasseled Cap Transformation Coefficients for Sentinel-2 MSI At-Sensor Reflectance Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Chunyang He,et al.  Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data , 2017 .

[27]  Li Wang,et al.  Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA , 2015, Remote. Sens..

[28]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[29]  T. Esch,et al.  Breaking new ground in mapping human settlements from space – The Global Urban Footprint , 2017, 1706.04862.

[30]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[31]  Xing Zhao,et al.  Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Noam Levin,et al.  Analyzing spatial variability in night-time lights using a high spatial resolution color Jilin-1 image – Jerusalem as a case study , 2020 .

[33]  Chen Shi,et al.  Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data , 2018, Remote. Sens..

[34]  Xue Liu,et al.  Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data , 2019, Remote. Sens..

[35]  Yuyu Zhou,et al.  Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 , 2020 .

[36]  Peijun Du,et al.  Multisource Earth Observation Data for Land-Cover Classification Using Random Forest , 2018, IEEE Geoscience and Remote Sensing Letters.

[37]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[38]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[39]  Jinpei Ou,et al.  Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[40]  Neil A. Dodgson,et al.  Decolorize: Fast, contrast enhancing, color to grayscale conversion , 2007, Pattern Recognit..

[41]  Chen Peng,et al.  Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[42]  Chenghu Zhou,et al.  Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives , 2019, Remote. Sens..

[43]  C. Deng,et al.  BCI: A biophysical composition index for remote sensing of urban environments , 2012 .

[44]  Christopher D. Elvidge,et al.  Automatic Boat Identification System for VIIRS Low Light Imaging Data , 2015, Remote. Sens..

[45]  Hao Wu,et al.  Integration of Satellite Images and Open Data for Impervious Surface Classification , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[47]  Noam Levin,et al.  Quantifying urban light pollution — A comparison between field measurements and EROS-B imagery , 2016 .

[48]  P. Tarolli,et al.  Flood dynamics in urbanised landscapes: 100 years of climate and humans’ interaction , 2017, Scientific Reports.

[49]  Cong Lin,et al.  Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 , 2019, Remote. Sens..

[50]  Philip H. Swain,et al.  Remote Sensing: The Quantitative Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Jianping Wu,et al.  Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[53]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[54]  Xiuping Jia,et al.  Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[55]  Gallego Pinilla Francisco,et al.  Validation of Copernicus High Resolution Layer on Imperviousness degree for 2006, 2009 and 2012 , 2016 .

[56]  Hui Xiong,et al.  Understanding and Enhancement of Internal Clustering Validation Measures , 2013, IEEE Transactions on Cybernetics.

[57]  Erzhu Li,et al.  Advances of Four Machine Learning Methods for Spatial Data Handling: a Review , 2020, Journal of Geovisualization and Spatial Analysis.

[58]  Guoqing Sun,et al.  Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest , 2019, Remote. Sens..