Improving Nocturnal Fire Detection With the VIIRS Day–Night Band

Polivka, T. N.; Wang, Jun; Ellison, Luke; Hyer, E. J.; Ichoku, Charles

doi:10.1109/tgrs.2016.2566665

Cited by 69 publications

(34 citation statements)

References 61 publications

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“…While the peak radiation at thermal wavelengths (8-12 µm) is related to a normal environmental temperature, hot temperature by forest fires can be detected at a shorter wavelength than the Earth's surface, especially 3-4 µm (band 7) [5]. The large BT difference between the shortwave (3-4 µm) and thermal bands can be observed in fire pixels, and thus the BT difference has been used in other fire detection algorithms [9,15,33,34]. High radiance values of band 5 are related to near the center of fires [12], and 2.2 µm (band 6) wavelength is sensitive to hot targets [50].…”

Section: Forest Fire Detectioinmentioning

confidence: 99%

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Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Jang

Kang

et al. 2019

Remote Sensing

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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy~99.16%, probability of detection (POD) 93.08%, probability of false detection (POFD)~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD~93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD~50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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Section: Forest Fire Detectioinmentioning

confidence: 99%

“…The large difference between the BTs in the shortwave (3-4 µm) and longwave (~11 µm) bands is related to fires [32]. Thus, it has been widely used in other fire detection algorithms [9,15,33,34]. The threshold value of this parameter was also defined by empirical tests using the reference data.…”

mentioning

confidence: 99%

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Jang

Kang

et al. 2019

Remote Sensing

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“…Fire behaviour and measures of the fuel consumed have been quantified through the analysis of thermal infrared imagery [55][56][57]. Infrared (IR) sensors and Infrared Line Scanning Systems on aircrafts allow land managers to detect actively burning areas, spot fires, estimate the energy radiated from the fire as it burns and to analyse fire behaviour.…”

Section: Innovation In Data Collectionmentioning

confidence: 99%

Improving Fire Behaviour Data Obtained from Wildfires

Filkov

Duff

Penman

2018

Forests

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Abstract:Organisations that manage wildfires are expected to deliver scientifically defensible decisions. However, the limited availability of high quality data restricts the rate at which research can advance. The nature of wildfires contributes to this: they are infrequent, complex events, occur with limited notice and are of relatively short duration. Some information is typically collected during wildfires, however, it is often of limited quantity and may not be of an appropriate standard for research. Here we argue for a minimum standard of data collection from every wildfire event to enhance the advancement of fire behaviour research and make research findings more internationally relevant. First, we analyse the information routinely collected during fire events across Australia. Secondly, we review research methodologies that may be able to supplement existing data collection. Based on the results of these surveys, we develop a recommended list of variables for routine collection during wildfires. In a research field typified by scarce data, improved data collection standards and methodologies will enhance information quality and allow the advancement in the development of quality science.

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“…Unlike its predecessor, the Operational Linescan System (OLS) on the Defense Meteorological Satellite Program (DMSP) platforms, the 20 DNB is calibrated and can be used for quantitative applications. DNB imagery has proven useful for many applications including cloud property retrievals (Walther et al, 2013), detection of low-altitude circulation centres in tropical cyclones (Hawkins et al, 2017), forest fire detection and estimation of fire radiative power Polivka et al, 2016), lightning detection (Bankert et al, 2011), identification of fishing boats and fishery monitoring (Elvidge et al, 2015 and, mapping power outages in the wake of natural disasters , and monitoring socioeconomic activity 25 (Ma et al, 2014).…”

mentioning

confidence: 99%

Assessing the Stability of Surface Lights for use in Retrievals of Nocturnal Atmospheric Parameters

Solbrig¹,

Miller²,

Zhang³

et al. 2019

Preprint

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Detection and characterization of aerosols is inherently limited at night due to a lack of sensitivity-information typically provided by visible spectrum observations. The VIIRS Day/Night Band (DNB) onboard the Suomi-NPP satellite is a first-of-its-kind calibrated sensor capable of collecting visible/near-infrared observations during both day and night. Multiple studies have suggested that anthropogenic light emissions such as those from cities and gas flares may be useable as light 10 sources for retrieval of atmospheric properties including cloud and aerosol optical depth. However, their use in this capacity requires proper characterization of their intrinsic variation, which represents a source of retrieval uncertainty. In this study we use 18 months of cloud-cleared VIIRS data collected over five selected geographic domains to assess the stability of anthropogenic light emissions and their response to varied satellite and lunar geometries. Timeseries are developed for each location in each domain for DNB radiance, four infrared channels, and satellite and lunar geometric variables, and spatially-15 resolved correlation coefficients are computed between DNB radiance and each of the other variables. This analysis finds that while many emissive light sources are too unstable to be used reliably for atmospheric retrievals, some sources exhibit a sufficient stability (relative standard deviation < 20%). Additionally, we find that while the radiance variability of surrounding surfaces (i.e. unpopulated land and ocean) is largely dependent on lunar geometry, the anthropogenic light sources are more strongly correlated to satellite viewing geometry. Understanding the spatially-resolved relationships between DNB radiance 20 and other parameters is a necessary first step towards characterizing anthropogenic light emissions and establishes a framework for a model to describe variability in a more general sense.

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Improving Nocturnal Fire Detection With the VIIRS Day–Night Band

Cited by 69 publications

References 61 publications

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Improving Fire Behaviour Data Obtained from Wildfires

Assessing the Stability of Surface Lights for use in Retrievals of Nocturnal Atmospheric Parameters

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