An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data

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.

Study on the substitutability of nighttime light data for SDG indicators: a case study of Yunnan Province

“…Besides, the activity degree of service industry is closely associated with the water consumption, so TNLI presents a significant correlation with the domestic water consumption. The value of PM 2.5 or PM 10 has a negative correlation with TNLI (Chen et al, 2022). This is consistent with Zhang's results of predicting show that the nighttime light index has a significant correlation with PM 2.5 (Xu et al, 2015).…”

“…Wang, Chen, and Zhen et al employed linear regression analysis to examine the relationship between NTL and various socioeconomic factors, including the economy, population, transportation, energy consumption, environment, and other relevant indicators (Wang et al, 2020;Zheng et al, 2020;Chen et al, 2022). Linear regression offers robust modeling and interpretability, making it the preferred choice for exploring the connection between nighttime lighting and diverse sustainable development metrics.…”

Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light Data

“…SVM is a statistical classification method widely used in remote sensing image classification. The method can find the optimal solution between training samples and objective features based on limited information [22]. The basic idea of SVM is to construct an optimal surface according to the training samples, using which the pixel element matrix of the input image is divided into matching and non-matching features.…”

“…Nighttime light (NTL) imagery has the characteristic of detecting weak light on the ground, which is easy to be acquired for long time series without shadows. NTL imagery can also be used to distinguish the surface features at night, which is beneficial to the identification of roads and buildings [20][21][22]. At present, the main NTL satellites include the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS), National Polar Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), and Luo Jia 1-01(LJ-1).…”

Spatiotemporal Evolution Characteristics of Urbanization in the Xiamen Special Economic Zone Based on Nighttime-Light Data from 1992 to 2020

“…In 1978, Croft et al [20] proposed the use of DMSP/OLS NTL data to extract UB. Later, with the improvement of NTL data, many methods were developed to extract UB, including the threshold segmentation method [21][22][23][24][25][26], the machine learning method [27][28][29][30][31], and the construction index method [12,[32][33][34][35]. Threshold segmentation methods include the experience threshold method [21,22], the mutation detection method [23,24], the statistical data-comparison method [25], and the clustering threshold method [26].…”