Fire Monitoring - The use of medium resolution satellites (AVHRR, MODIS, TET) for long time series processing and the implementation in User Driven Applications and Services

Fuchs, Eva-Maria; Stein, Enrico; Strunz, Günter; Strobl, Christian; Frey, Corinne

doi:10.5194/isprsarchives-xl-7-w3-797-2015

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…2017, 9, 30 3 of 22 An extensive round robin approach which tested and validated different methods and thresholds has shown that the contextual algorithm performs best for hotspot detection with AVHRR [70]. Therefore, a further enhancement of the contextual algorithm of Giglio et al [61] is used for final implementation (cf.…”

Section: Methods For Active Fire Detectionmentioning

confidence: 99%

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

2017

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Abstract:The German Aerospace Center's (DLR) TIMELINE project aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)-Advanced Very High Resolution Radiometer (AVHRR) raw data into L1b, L2 and L3 products. This article presents the current status of the fully automated L2 active fire hotspot detection processor, which is based on single-temporal datasets in orbit geometry. Three different probability levels of fire detection are provided. The results of the hotspot processor were tested with simulated fire data. Moreover, the processing results of real AVHRR imagery were validated with five different datasets: MODIS hotspots, visually confirmed MODIS hotspots, fire-news data from the European Forest Fire Information System (EFFIS), burnt area mapping of the Copernicus Emergency Management Service (EMS) and data of the Piedmont fire database.

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Section: Methods For Active Fire Detectionmentioning

confidence: 99%

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

2017

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“…Fire point recognition methods such as image enhancement method, threshold method (single threshold method, multi threshold method and dynamic threshold method), NDVI index method [11][12][13]. Fuchs et al [19] were the first to use the Threshold Method based on mid-infrared and far-infrared channels to monitor fires. In this method, the discrimination threshold is set based on the surface temperature rise and the abnormal brightness temperature of mid-infrared and far-infrared channels when a wildfire occurs.…”

Section: Introductionmentioning

confidence: 99%

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

Zhang

Sun

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The GF-4 satellite has a moderate spatial resolution and is capable of high temporal frequency observation. Thus, it can play an important role in the acquisition of information regarding fires. In this study, the characteristics of brightness temperature difference between fire point area and non-fire point area are comprehensively analyzed. Employing the advantages of high-frequency continuous observation of these data, an improved algorithm for joint spatial attribute detection using multi-temporal data is proposed. This method is based on the traditional Contextual Method and incorporates the concept of time sequence, and uses the changes in time phase before and after the fire to reduce the false alarm caused by the high heterogeneity of pixels in a single spatial scene in the traditional method. In order to evaluate the accuracy of the algorithm, the perfect MODIS thermal anomaly product is used as the verification data to evaluate the feasibility of the algorithm. At the same time, it is necessary to compare the algorithm in this study with the fire algorithm currently applied to GF-4 with the four typical regions. The improved algorithm shows better drawing ability, and the consistency of fire description of biomass combustion for many days is improved. In the case of the Heilongjiang region on October 30, 2017, the accuracy rate is improved by 62%, compared with the Contextual Method.

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“…In general,for early detection of forest fires,it is necessary to use satellite data with at least medium spatial resolution (-1 km)and high temporal resolution (Schroeder et al (2008), Fuchs et al (2015)).…”

Section: Introductionmentioning

confidence: 99%

Duration time detection of Forest Fire by Advanced HIMAWARI-8 Imager (AHI) for Indonesian peat fires in 2015.

Nakazono

Takeuchi

Moriyama

2022

jsprs

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We investigated the feasibility of using the Himawari-8, a Japanese meteorological geostationary satellite with high temporal resolution (receiving data at 10-minute intervals), to measure the time from fire outbreak to extinction. We selected the study area in Central Kalimantan, in Indonesia, containing peatland. We first investigated which band of Advanced Himawari Imager (AHI) is more sensitive for detecting fire areas and which band is suitable for tracking time-series change of fire area. Therefore,we first used the Landsat 8 data of 2015/10/ 22 for visual interpretation of the fire area and then selected pixels corresponding to the fire area from the AHI data acquired at about the same time as the Landsat 8 data. We found that band 7 of the AHI is the most suitable for detecting fire area even if the ratio of fire area in one pixel is low,and we assumed that not only high values of band 7 but also the short-term fluctuations are characteristics of fires. To establish this assumption,we separated the value of band 7 into 2 components,mean value and short-term fluctuations,μtm,and Dtm,respectively. Then, we applied these two indices to the data for September 2015 and confirmed how fire occurrence and extinguishment were captured by these two indices.

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Fire Monitoring - The use of medium resolution satellites (AVHRR, MODIS, TET) for long time series processing and the implementation in User Driven Applications and Services

Cited by 4 publications

References 37 publications

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

Duration time detection of Forest Fire by Advanced HIMAWARI-8 Imager (AHI) for Indonesian peat fires in 2015.

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