Abstract
Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management.
Highlights
In addition to an environment-based remote sensing dataset such as Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), a nighttime light (NTL) remote sensing dataset is more recognized as human-oriented [1], reflecting the distribution and intensity of human activities
Proposing error-attributing indices is helpful to guide the source detection of the NTL dataset. Addressing these shortcomings, this study mainly focuses on detecting the NTL sources within the urban area through the parcel-oriented temporal linear unmixing method (POTLUM), and proposing two series of indices, namely the parcel purity index (PPI) and source sufficiency index (SSI), to attribute the unmixing error
We have developed an urban NTL source detection method called parcel-oriented temporal linear unmixing method (POTLUM) and proposed two indices to attribute the unmixing errors
Summary
In addition to an environment-based remote sensing dataset such as Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), a nighttime light (NTL) remote sensing dataset is more recognized as human-oriented [1], reflecting the distribution and intensity of human activities. A number of recent studies have been devoted to detecting the relation between NTL and human activities through time series perspective, for example, interpreting human activities from the seasonal fluctuation of NTL datasets [20] by using points of interest (POI) or optical remote sensing datasets [21] Another obstacle impeding the source detection of NTL is the lack of objective assessing indices. Light sources were manually merged, and different study areas were selected to check the practicability of SSI This simple method proposed here is helpful to interpret long-term human activity footprint and can be applied in a better and finer estimation of socioeconomic variables, or its relation to estrogen-dependent diseases. This study helps to excavate the potential usage of NTL, and better bridge remote sensing community and socioeconomic society
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