Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Th...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ning Zhang, Lin Sun, Zhendong Sun, Yu Qu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	visible infrared imaging radiometer suite active fire identification mid-infrared screening weighted fire algorithm adaptive threshold Science Q
Acceso en línea:	https://doaj.org/article/d6e94ba670eb4232bf954a68b48c1865
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d6e94ba670eb4232bf954a68b48c1865
record_format	dspace
spelling	oai:doaj.org-article:d6e94ba670eb4232bf954a68b48c18652021-11-11T18:50:36ZDetecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm10.3390/rs132142262072-4292https://doaj.org/article/d6e94ba670eb4232bf954a68b48c18652021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4226https://doaj.org/toc/2072-4292The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.Ning ZhangLin SunZhendong SunYu QuMDPI AGarticlevisible infrared imaging radiometer suiteactive fire identificationmid-infrared screeningweighted fire algorithmadaptive thresholdScienceQENRemote Sensing, Vol 13, Iss 4226, p 4226 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	visible infrared imaging radiometer suite active fire identification mid-infrared screening weighted fire algorithm adaptive threshold Science Q
spellingShingle	visible infrared imaging radiometer suite active fire identification mid-infrared screening weighted fire algorithm adaptive threshold Science Q Ning Zhang Lin Sun Zhendong Sun Yu Qu Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
description	The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate.
format	article
author	Ning Zhang Lin Sun Zhendong Sun Yu Qu
author_facet	Ning Zhang Lin Sun Zhendong Sun Yu Qu
author_sort	Ning Zhang
title	Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
title_short	Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
title_full	Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
title_fullStr	Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
title_full_unstemmed	Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm
title_sort	detecting low-intensity fires in east asia using viirs data: an improved contextual algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d6e94ba670eb4232bf954a68b48c1865
work_keys_str_mv	AT ningzhang detectinglowintensityfiresineastasiausingviirsdataanimprovedcontextualalgorithm AT linsun detectinglowintensityfiresineastasiausingviirsdataanimprovedcontextualalgorithm AT zhendongsun detectinglowintensityfiresineastasiausingviirsdataanimprovedcontextualalgorithm AT yuqu detectinglowintensityfiresineastasiausingviirsdataanimprovedcontextualalgorithm
_version_	1718431731834421248

Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm

Ejemplares similares