Despite their important contribution to the economic domain, active heat-releasing industrial plants have significant implications for human health and climate change. However, a spatially detailed dataset of various heat-releasing industrial sectors and large-scale characterization of heat emissions from industrial sources have not been reported yet. In this study, a dataset of heat-releasing industries was established using a national detection map of thermal anomalies produced by a novel and more accurate method employing daily nighttime visible infrared imaging radiometer suite thermal infrared images corresponding to 1 year. Subsequently, we quantified the dimensional features of heat radiation fluxes of China's industrial plants. A total of 12 114 industrial objects were structured in a two-level hierarchical dataset of heat-releasing industries, representing a magnitude of at least 1 order higher than the number enumerated in the state-of-the-art inventory of industrial heat sources across China. The satellite observations helped more completely characterize industrial heat plumes, which represent the industrial heat radiation fluxes with higher levels of densities that prevail in the central-eastern part of China having spatial clustering islands. Our results could be used to inform policy and environmental management in relation to meaningful dynamic industrial supervision, targeting extreme polluters and differentiated emission mitigation measurements.

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Abstract:Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

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