Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data

Abstract

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications.

Remote Sensing of Night Lights—Beyond DMSP

“…To curb the fast yet disorderly urbanization trends, especially in developing countries, urban boundaries are treated as effective administrative tools to confine urban development within areas encompassed by identified urban boundaries. Xue et al [26] found that by combining VIIRS/DNB nighttime light data with daytime Landsat-8 multispectral data, they can effectively detect urban boundaries at a 30 m spatial resolution, which can otherwise be often time-consuming and ineffective. They first identified a rough urban boundary for an urbanized region of interest by a search mode of the concentric zones model and a variance-based approach.…”

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

“…However, their producer's accuracy (how often real urban areas on the ground are correctly shown on the classified map) is generally low (IMPSA and GLC2000 < 50%, MODIS 500 m and MODIS 1km around 75%, and GRUMP nearly 90%), and their user's accuracies (how often the urban areas on the map are actually present on the ground) is also low (MODIS 500 m around 73%, GLC2000 and IMPSA are 66% and 65%) with the Kappa coefficients ranging from only 0.28 to 0.65 [10]. More recent studies at intermediate resolutions [16,[36][37][38][39][40][41][42][43][44][45][46][47][48] reported overall accuracies from 73% to 99% for all urban and non-urban features, with Kappa coefficients from 0.29 to 0.84, and the producer's accuracy around 80%, and user's accuracy close to 90% for only urban features. Therefore, research is still needed to develop an intermediate-resolution urban extent mapping methodology that can achieve consistent high accuracies (overall accuracy, producer's accuracy, user's accuracy, and Kappa), is repeatable for different times, and is scalable to continental-to-global scale applications [7].…”

“…Therefore, the combination of nighttime light and daytime spectral data has the potential to overcome their individual limitations. However, only a few former studies have combined nighttime light data and daytime spectral data [16,19,20,[46][47][48].…”

Planar Block Adjustment for China’s Land Regions with LuoJia1-01 Nighttime Light Imagery

“…The representation of nighttime light distribution and intensity information based on remote sensing is closely related to human socio-economic development. Nighttime light data have been widely used for urban expansion analysis [3][4][5][6][7], humanitarian disaster evaluation [8][9][10], economic evaluation [11][12][13][14][15][16][17][18][19], investigation of artificial light pollution [20][21][22][23], carbon emission analysis [24], and information extraction [25][26][27][28] and have become one of the important datasets for socio-economic parameter space simulation.…”