Abstract
An accelerating trend of global urbanization accompanying various environmental and urban issues makes frequently urban mapping. Nighttime light data (NTL) has shown great advantages in urban mapping at regional and global scales over long time series because of its appropriate spatial and temporal resolution, free access, and global coverage. However, the existing urban extent extraction methods based on nighttime light data rely on auxiliary data and training samples, which require labor and time for data preparation, leading to the difficulty to extract urban extent at a large scale. This study seeks to develop an unsupervised method to extract urban extent from nighttime light data rapidly and accurately without ancillary data. The clustering algorithm is applied to segment urban areas from the background and multi-scale spatial context constraints are utilized to reduce errors arising from the low brightness areas and increase detail information in urban edge district. Firstly, the urban edge district is detected using spatial context constrained clustering, and the NTL image is divided into urban interior district, urban edge district and non-urban interior district. Secondly, the urban edge pixels are classified by an adaptive direction filtering clustering. Finally, the full urban extent is obtained by merging the urban inner pixels and the urban pixels in urban edge district. The proposed method was validated using the urban extents of 25 Chinese cities, obtained by Landsat8 images and compared with two common methods, the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM). The Kappa coefficient ranged from 0.687 to 0.829 with an average of 0.7686 (1.80% higher than LOT and 4.88% higher than INNL-SVM). The results in this study show that the proposed method is a reliable and efficient method for extracting urban extent with high accuracy and simple operation. These imply the significant potential for urban mapping and urban expansion research at regional and global scales automatically and accurately.
Highlights
Urbanization is speeding up the consumption of natural resources, energy, and altering land use and cover, which is closely related to almost all aspects of global change [1,2]
An alternative nighttime light (NTL) data provided by the Visible Infrared Imaging Radiometer Suite Day/Night Band carried by the Suomi National Polar-orbiting Partnership (NPP-VIIRS) is available from April 2012 to present [17]
Shi et al [19] evaluated the potential of NPP-VIIRS NTL data for extracting urban extent and found that the urban extent extracted from NPP-VIIRS NTL data has higher spatial accuracies than that extracted from DMSP-OLS NTL data
Summary
Urbanization is speeding up the consumption of natural resources, energy, and altering land use and cover, which is closely related to almost all aspects of global change [1,2]. The threshold method based on high-resolution remote sensing images has better accuracy and reliability, and is widely used in urban extent extraction. The local optimal threshold (LOT) method has been used in recent studies, which estimates optimal thresholds for each city or tile at a regional scale based on the relationships between nightlight magnitude in NTL data and urban morphology interpreted from high-resolution images [9,18,24,25] These relationships and parameters change across regions and vary by time, which requires a large amount of work on interpreting from high resolution images [14]. Liu et al [29] explored the effectiveness of commonly used machine learning methods for urban extent extraction from NTL data, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), and the results showed that these machine learning methods can achieve similar high accuracies. The urban extent derived from the Landsat OLI image was used as the reference for accuracy assessment, and the most commonly used methods the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM) [33] were used to compare, the validity, reliability, and robustness of our method are verified
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