VIIRS Nightfire Remote Sensing Volcanoes

Trifonov, Grigory; Zhizhin, Mikhail; Melnikov, D.V.; Poyda, Alexey

doi:10.1016/j.procs.2017.11.189

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…However, there are few studies that have focused on the detection of heavy industrial heat sources. The NPOESS Preparatory Project (NPP) Visible Infrared Imaging Radiometer (VIIRS) is a mature nighttime thermal anomaly product that was successfully applied to volcanic activity [8] and oil and gas exploitation [9]. Liu et al [2] and Sun et al [10] used NPP VIIRS Nightfire product data to identify industrial heat sources by combining time-space-temperature information.…”

Section: Introductionmentioning

confidence: 99%

Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data

Yang

Chen

et al. 2018

Sustainability

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Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.

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Section: Introductionmentioning

confidence: 99%

Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data

Yang

Chen

et al. 2018

Sustainability

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“…But the radiant heat RH generated by these burning sources are not statistically analyzed. For example, these nightfire data have been used in remote sensing active volcanoes [18]. It has been used to map gas flaring and policy compliance in Yasuni biosphere reserve, in a study of burnable and unleakable carbon in western amazon [19].…”

Section: Results and Analysismentioning

confidence: 99%

Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data

Devkota

2020

E-Journal of Analysis and Applied Mathematics

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The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.

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“…Recent use of DBSCAN in geographical clustering includes retail agglomerations [20], and location recommendation in location-based social networks [21]. Recent research also applied the DBSCAN to detect clusters in raster images [22]. Despite years of proposed improvements, the original DBSCAN could still perform well as long as we pick a reasonable parameter [23].…”

Section: Dbscan Clusteringmentioning

confidence: 99%

Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

Anwar

Hadikurniawati

Winarno

et al. 2019

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Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;

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VIIRS Nightfire Remote Sensing Volcanoes

Cited by 8 publications

References 7 publications

Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data

Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data

Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data

Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

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