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

Li, Rong; Tao, Minghui; Zhang, Meigen; Chen, Liangfu; Wang, Yi; He, Xiaohua; Wei, Lifei; Mei, Xin; Wang, Jun

doi:10.1029/2021gl092997

Cited by 9 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the degree of uncertainty in these data collection efforts varies among industries and geographical areas 16 . These methods are unsuitable for near real-time monitoring at a large scale, particularly for global IHS 17 , 18 .…”

Section: Background and Summarymentioning

confidence: 99%

“…As a result, 15,199 global inventories of industrial heat sources (accuracy 77%) operating between 2012 and 2016 in Liu’s product were detected. The NPP VIIRS active fire product (ACF) with a 375-m resolution for daily/nighttime thermal anomalies 22 was applied to identify IHS based on a density-based spatial-temporal clustering model to detect much smaller IHS in China 1 , 18 . A total of 4,143 IHS operating between 2012 and 2017 were identified by Ma 1 , and 2,055 high-confidence IHS were identified by Li 18 in 2016 in China.…”

Section: Background and Summarymentioning

confidence: 99%

“…The NPP VIIRS active fire product (ACF) with a 375-m resolution for daily/nighttime thermal anomalies 22 was applied to identify IHS based on a density-based spatial-temporal clustering model to detect much smaller IHS in China 1 , 18 . A total of 4,143 IHS operating between 2012 and 2017 were identified by Ma 1 , and 2,055 high-confidence IHS were identified by Li 18 in 2016 in China. Flint night light data was introduced to remove biomass heat source objects caused by slash and burn agriculture 29 in India 30 .…”

Section: Background and Summarymentioning

confidence: 99%

See 2 more Smart Citations

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

Ma,

Li,

Sui

et al. 2024

Sci Data

View full text Add to dashboard Cite

The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012–2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user’s accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

See 1 more Smart Citation

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

Ma,

Li,

Sui

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

“…The complex spatial distribution and large number of industrial heat sources necessitate the use of remote sensing methods as alternatives to traditional approaches for obtaining relatively accurate information [11,12]. Currently, satellite remote sensing monitoring is being applied in various fields, such as tracking the expansion of urban construction [13], changes in forest cover [14], radiant emissions from urban nighttime lighting [15], and heat release activities in industrial areas [16].…”

Section: Introductionmentioning

confidence: 99%

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

Xie,

Ma,

Zhao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

show abstract

“…Lui et al [14] constructed a global map of 15,199 industrial infrared emitters from four years of VNF data. Other investigators have assembled regional maps of infrared emitters using either VNF or midwave infrared hotspot detections [15][16][17]. In this paper we present results from a new inventory of industrial IR emitters worldwide based on the nightly record of VNF detections from 2012 through 2022.…”

Section: Introductionmentioning

confidence: 99%

Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions

Elvidge,

Zhizhin,

Sparks

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

This paper reports on the first daily global monitoring program for natural gas flaring and industrial sites producing waste heat based on satellite observed infrared emissions. The Visible Infrared Imaging Radiometer Suite (VIIRS) collects nightly global infrared data in spectral bands ranging from near infrared (NIR) to longwave infrared (LWIR), providing a unique capability to detect and characterize infrared emitters at night. The VIIRS nightfire (VNF) algorithm identifies infrared (IR) emitters with multiple spectral bands and calculates the temperature, source area, and radiant heat via Planck curve fitting and physical laws. VNF data are produced nightly and extend from 2012 to the present. The most common infrared emitter is biomass burning, which must be filtered out. Industrial IR emitters can be distinguished from biomass burning based on temperature and persistence. The initial filtering to remove biomass burning was performed with 15 arc second grids formed from eleven years of VIIRS data, spanning 2012–2022. The locations and shapes of the remaining features were used to guide the generation of super-resolution pixel center clouds. These data clouds were then analyzed to define bounding vectors for single emitters and to split larger clusters into multiple emitters. A total of nearly 20,000 IR emitters were identified; each was assigned an identification number, and the type of emitter was recorded. Nightly temporal profiles were produced for each site, revealing activity patterns back to 2012. Nightly temporal profiles were kept current with weekly updates. Temporal profiles from individual sites were aggregated by country to form monthly profiles extending back to 2012. The nightly and monthly temporal profiles were suitable for analyzing industrial production, identifying disruption events, and tracking recovery. The data could also be used in tracking progress in energy conservation and greenhouse gas emission inventories.

show abstract

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

Cited by 9 publications

References 23 publications

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions

Contact Info

Product

Resources

About