Identification and spatiotemporal distribution analysis of global biomass burning based on Suomi-NPP VIIRS Nightfire data

Wan, Yi; Chen, Yunhao; Li, Kangning

doi:10.1016/j.jclepro.2022.131959

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…With global warming, the spatial and temporal distribution of BB and its impact on air quality have increasingly attracted the attention of scholars [12][13][14][15][16]. Over the past 20 years, global fire points have shown significant regional and seasonal variations [17].…”

Section: Introductionmentioning

confidence: 99%

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Huang,

Jin,

Sun

et al. 2024

Remote Sensing

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Despite the significant impacts of biomass burning (BB) on global climate change and regional air pollution, there is a relative lack of research on the temporal trends and geographic patterns of BB in Northeast China (NEC). This study investigates the spatial–temporal distribution of BB and its impact on the atmospheric environment in the NEC region during 2004 to 2023 based on remote sensing satellite data and reanalyzed data, using the Siegel’s Repeated Median Estimator and Mann–Kendall test for trend analysis, HDBSCAN to identify significant BB change regions, and Moran’s Index to examine the spatial autocorrelation of BB. The obtained results indicate a fluctuating yet overall increasing BB trend, characterized by annual increases of 759 for fire point counts (FPC) and 12,000 MW for fire radiated power (FRP). BB predominantly occurs in the Songnen Plain (SNP), Sanjiang Plain (SJP), Liaohe Plain (LHP), and the transitional area between SNP and the adjacent Greater Khingan Mountains (GKM) and Lesser Khingan Mountains (LKM). Cropland and urban areas exhibit the highest growth in BB trends, each surpassing 60% (p < 0.05), with the most significant growth cluster spanning 68,634.9 km2. Seasonal analysis shows that BB peaks in spring and autumn, with spring experiencing the highest severity. The most critical periods for BB are March–April and October–November, during which FPC and FRP contribute to over 80% of the annual total. This trend correlates with spring planting and autumn harvesting, where cropland FPC constitutes 71% of all land-cover types involved in BB. Comparative analysis of the aerosol extinction coefficient (AEC) between areas with increasing and decreasing BB indicates higher AEC in BB increasing regions, especially in spring, with the vertical transport of BB reaching up to 1.5 km. County-level spatial autocorrelation analysis indicates high–high clustering in the SNP and SJP, with a notable resurgence of autocorrelation in the SNP, suggesting the need for coordinated provincial prevention and control efforts. Finally, our analysis of the impact of BB on atmospheric pollutants shows that there is a correlation between FRP and pollutants, with correlations for PM2.5, PM10, and CO of 0.4, 0.4, and 0.5, respectively. In addition, the impacts of BB vary by region and season, with the most significant impacts occurring in the spring, especially in the SNP, which requires more attention. In summary, considering the escalating BB trend in NEC and its significant effect on air quality, this study highlights the urgent necessity for improved monitoring and strategic interventions.

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Section: Introductionmentioning

confidence: 99%

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Huang,

Jin,

Sun

et al. 2024

Remote Sensing

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“…In 2011, NOAA (National Oceanic and Atmospheric Administration) began to operate one of the remote sensing technologies called VIIRS (Visible Infrared Imaging Radiometer Suite) [6], which was produced by the Raytheon Company. It is a sensor mounted on the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP), NOAA-20, and NOAA-21 meteorological satellites, and is now being used by more researchers, including [7][8][9][10]. In 2018, a new product of VIIRS was released, namely the VIIRS Nighttime Imagery, which collects the earth's surface and atmosphere by utilizing a sensor created to catch low-light emission Sources under a variety of lighting situations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Suhardiman,

Hermanto

2023

Proceedings of International Symposium on Grids &Amp; Clouds (ISGC) 2023 in Conjunction With HEPiX Spring 2023 Workshop — PoS(I

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Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of 'flame' status occurring. We conclude that the ANN method can give more accurate results than the existing classification.

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An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021

Ma,

Sui,

Guan

et al. 2024

Geoscience Data Journal

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The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

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Identification and spatiotemporal distribution analysis of global biomass burning based on Suomi-NPP VIIRS Nightfire data

Cited by 7 publications

References 39 publications

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

An industrial heat source dataset based on remotely sensed active fire/hotspot detection in China from 2012 to 2021

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