Satellite-Based Detection and Characterization of Industrial Heat Sources in China

Abstract

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.

Application Potential of Satellite Thermal Anomaly Products in Updating Industrial Emission Inventory of China

“…There are 2,055 high-confidence industrial heat sources identified, which is close to the result (2,491) from Liu et al (2018) but much fewer than that (16,505 polygons) by Zhang et al (2019). The large difference could be due to that thermal anomaly points in a large-area factory is divide into several polygons by the three-sliding window algorithm used in Zhang et al (2019).…”

Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering

“…A simple TAI index based on high-resolution ASTER data has introduced by Xia [5] for the extracting of hot spots, but it still requires the aid of the modified normalized difference water index (MNDWI) out of Landsat 8 to remove water bodies and Google Earth data to preclude the wildfires and other nonindustrial seasonal heat resources. Withal, Zhang [23] has also constructed a three-sliding widow approach based on empirical thermal anomaly index using VIIR Nightfire product to flag and extract heat-releasing industries in China.…”

“…In fact, it remains quite challenging to precisely distinguish the industrial heat releasers from other fire hotspots like wildfires, and the related works are relatively few. Recently, some studies have relied on the empirical thermal anomaly index (TAI)-based approaches [5,23] which normally need prior knowledge and extra data for the removal of water, wildfires, and other nonindustrial seasonal burnings. It is also worth noticing that a novel object-oriented approach [24] has guided the way to building a frequency-based metric index to determine the degree of spatial and temporal aggregation on time-serial VIIRS Nightfire products.…”

Sharpening of Thermal Satellite Imagery From Klang Industrial Area in Peninsular Malaysia Using the Tsharp Approach

“…Consequently, The advancement in remote sensing technology offers an opportunity to provide a reliable, consistent, and repeatable approach within the working frame from local to a global scale, as well as long-term monitoring of oil spillage operations (Bromley et al, 2015;Casagli et al, 2017) The development and changes related to land cover and urban features have been associated with industrial heat emission, where the surface air temperature is becoming higher compared to the surrounding environment (Lee et al, 2003). There is a need for industrial heat regulations which will attach to green space to monitor the heat intensity from industrial heat emissions through a descriptive technology (Liu et al, 2018;Zhang et al, 2019) In connection with ground truth measurement of air temperature (Takebayashi, 2017).…”