Abstract
Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).
Highlights
Impervious surfaces are usually covered by anthropogenic materials which prevent water penetrating into the soil (Weng, 2012) and are primarily composed of asphalt, sand and stone, concrete, bricks, glass, etc. (Chen et al, 2015)
Global impervious surface maps are still produced by optical imagery alone or by using a combination of optical and Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) or Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) imagery (Huang et al, 2016; Liu et al, 2018; Schneider et al, 2010)
A global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform
Summary
Impervious surfaces are usually covered by anthropogenic materials which prevent water penetrating into the soil (Weng, 2012) and are primarily composed of asphalt, sand and stone, concrete, bricks, glass, etc. (Chen et al, 2015). As an important indicator in the monitoring of the intensity of human activity and of ecological and environmental changes, the mapping of impervious surfaces is of great interest in many disciplines (Xie and Weng, 2017). Elvidge et al (2007) combined the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and LandScan population count data to produce a 1 km global impervious surface area map. Because of the complex characteristics of impervious landscapes and inherent resolution of human activity, coarse-resolution global impervious surface maps are not suitable for many applications and policymakers at local or regional scales, for example, for urban–rural pattern planning and road network monitoring, which usually require fine-spatial-resolution impervious surface products (Gao et al, 2012)
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