Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data

Abstract

Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While high-resolution urban extent data products - including the Global Human Settlements Layer (GHSL), the Global Man-Made Impervious Surface (GMIS), the Global Human Built-Up and Settlement Extent (HBASE), and the Global Urban Footprint (GUF) - have recently become available, intermediate-resolution urban extent data products including the 1 km SEDAC’s Global Rural-Urban Mapping Project (GRUMP), MODIS 1km, and MODIS 500 m still have many users and have been demonstrated in a recent study to be more appropriate in urbanization process analysis (around 500 m resolution) than those at higher resolutions (30 m). The objective of this study is to improve large-scale urban extent mapping at an intermediate resolution (500 m) using machine learning methods through combining the complementary nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) and daytime Moderate Resolution Imaging Spectroradiometer (MODIS) data, taking the conterminous United States (CONUS) as the study area. The effectiveness of commonly-used machine learning methods, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), has been explored. Our results show that these machine learning methods can achieve similar high accuracies across all accuracy metrics (>95% overall accuracy, >98% producer’s accuracy, and >92% user’s accuracy) with Kappa coefficients greater than 0.90, which have not been achieved in the existing data products or by previous studies; the ESB is not able to produce significantly better accuracies than individual machine learning methods; the total misclassifications generated by GBM are more than those generated by RF, NN, and ESB by 14%, 16%, and 11%, respectively, with NN having the least total misclassifications. This indicates that using these machine learning methods, especially NN and RF, with the combination of VIIRS nighttime light and MODIS daytime normalized difference vegetation index (NDVI) data, high accuracy intermediate-resolution urban extent data products at large spatial scales can be achieved. The methodology has the potential to be applied to annual continental-to-global scale urban extent mapping at intermediate resolutions.

Meteorological and Ancillary Data Resources for Climate Research in Urban Areas

“…The LC product includes information on land cover data, and other information highly relevant for urban climate analysis, such as artificial surfaces (habitation, industrial and mining area, transportation facilities, and interior urban green zones and water bodies, etc.). GlobeLand30 was exploited by [104] for investigating the urban expansion impact on a global scale.…”

“…The GlobeLand30 (GLOB) is a high-resolution dataset (30 m) of global land use/cover, based on TM5 and ETM + of America Land Resources Satellite (Landsat) and the multispectral images of China Environmental Disaster Alleviation Satellite (HJ-1) from 2010) [104]. The LC product includes information on land cover data, and other information highly relevant for urban climate analysis, such as artificial surfaces (habitation, industrial and mining area, transportation facilities, and interior urban green zones and water bodies, etc.).…”

Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

“…The RF classifier integrates a set of Classification and Regression Trees (CARTs) in a concise framework, and has received a significant amount of interest [50,51]. It is a kind of ensemble learning method that can minimize over-fitting and redundancy problems [10,47]. The RF classifier has also proven to be successful in integrating data from multiple sources for feature extraction or classification, including tree species [52], canopy cover [53], and forest habitats [54].…”

“…Remote sensing (RS) technology can be used to map the extent of urbanization at different spatial scales. An intermediate spatial resolution dataset is suitable for large-scale studies that consider a comprehensive range of factors [3,9,10]. In addition to traditional diurnal imagery, night-time light (NTL) imagery has been introduced to map urban areas due to its simpler and more uniform characteristics [3,11].…”

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

“…Although NTL image can acquire ISA information simply, it also has some limitations due to the blooming effect [28]. Fortunately, the integration of NTL data with daytime RS images are expected to solve this problem [29], [30]. Owing to the coarse spatial resolution of aforementioned NTL imagery, it is difficult to meet the requirements of fine ISA extraction.…”