Spectral Analysis of the Himawari-8 Data for Hotspot Detection From Land/Forest Fires in Sumatra

Fitriana, Hana Listi; Sulma, Sayidah; Suwarsono, Nfn; Zubaidah, Any; Prasasti, Indah

doi:10.30536/j.ijreses.2018.v15.a2836

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…The third plot type shows the fireline intensity calculated by Prototype 2 in time series and compares it with fire radiation power of the hotspots monitored by two types of satellite sensors: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership Program (S-NPP), and the AHI sensor on the Himawari-8 geostationary satellite (JAXA, 2022;Lee, 2014;ORNL DAAC, 2018;Schroeder, 2017). SNPP and Himawari-8 were launched in 2011 and 2016 respectively (Listi et al, 2018;Yumimoto et al, 2016). Spatial and temporal resolution varies between the two satellite hotspot products.…”

Section: Wind Vector Map and Numerical Weather Model Vertical Soundin...mentioning

confidence: 99%

“…Spatial and temporal resolution varies between the two satellite hotspot products. The temporal resolution in VIIRS data is poor with observations only six times a day at most (Lee, 2014;Schroeder, 2017;Wang, 2021), while its spatial resolution is high, with a pixel size of 375 m. On the other hand, the spatial resolution in the Himawari-8 hotspot detections is low, with 2 km pixels, but its temporal resolution is extremely good, due to the geostationary nature of the satellite, with detection frequency of up to 10 minutes (Listi et al, 2018;Yumimoto et al, 2016). The fireline intensity is plotted regardless of the starting time of fire and the location of the fireline intensity in this plot is only for a pseudo station.…”

Section: Wind Vector Map and Numerical Weather Model Vertical Soundin...mentioning

confidence: 99%

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Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

Ozaki¹,

Williamson²,

Fox‐Hughes³

et al. 2023

Preprint

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Although mountain areas account for approximately one fifth of the terrestrial surface, there has been less research focused on fire in these areas compared to lowlands. Mountain fires have distinct behavior due to dynamic winds interacting with the terrain, which can influence the fireline intensity and propagation. For the sake of fire safety of fire crews, it is essential to know how difficult to control the fire is in the mountain regions, with fireline intensity providing a useful indicator of risk and suppressibility. We studied one of the major disasters, wildfire, in Australia in such a highland by using the Great Pine Tier Fire, which occurred 15th January in 2019, ending up burning approximately 511.86km2. Weather and fire intensity at pseudo weather stations located at key points of fire progression were analyzed by wind vector maps and numerical weather model vertical sounding (NWMVS). Fire propagation was then simulated in Prototype 2, a fire simulator capable of detecting the potential for lateral fire channeling (LFC), and simulating fireline intensity using Australian vegetation sub-models. We found that the synoptic wind appeared to be modified by the interaction with the terrain in windward and the fire intensified the most in its leeward. In practice, the fire moved out of the valley axis and up its sidehill by following the wind which had been modified by local vertex of the curved valley axis before reaching this location.

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Section: Wind Vector Map and Numerical Weather Model Vertical Soundin...mentioning

confidence: 99%

Section: Wind Vector Map and Numerical Weather Model Vertical Soundin...mentioning

confidence: 99%

Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

Ozaki¹,

Williamson²,

Fox‐Hughes³

et al. 2023

Preprint

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“…Kebakaran hutan dan lahan diidentifikasi dari sebaran asap yang dihasilkan pada olahan citra dan dikonfirmasi dengan titik panas dari citra MODIS. Penelitian lain dilakukan oleh Fitriana et al (2018) yang menggunakan pola spektral untuk mendeteksi kebakaran hutan dan lahan di pulau Sumatera, didapatkan citra satelit Himawari-8 memiliki kemampuan yang baik dalam deteksi kebakaran, ditunjukkan dengan pola yang jelas pada saluran 3 (inframerah dekat) dan 7 (inframerah tengah). Namun dua penelitian diatas belum menghasilkan titik panas.…”

Section: Pendahuluanunclassified

Deteksi Kebakaran Hutan Dan Lahan Menggunakan Citra Satelit Himawari-8 Di Kalimantan Tengah

Sepriando

Hartono

Jatmiko

2020

JSTMC

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IntisariKebakaran hutan dan lahan terjadi hampir setiap tahun di Indonesia, terutama di wilayah Sumatera dan Kalimantan saat musim kemarau. Deteksi kebakaran hutan dan lahan dengan citra satelit menggunakan indikator yang disebut titik panas. Titik panas yang digunakan saat ini di Indonesia diperoleh dari pengolahan data citra satelit berorbit polar (MODIS dan VIIRS) dengan resolusi temporal yang rendah, yaitu hanya 6 kali dalam sehari. Tujuan dari penelitian ini adalah memanfaatkan data citra satelit Himawari-8 untuk deteksi kebakaran hutan dan lahan yang menghasilkan titik panas dengan resolusi temporal 10 menit, dimana hasilnya di validasi dengan citra polar dan data kebakaran lapangan. Lokasi penelitian berada di Provinsi Kalimantan Tengah dan waktu penelitian adalah bulan September 2019. Data yang digunakan untuk pengolahan adalah 5 saluran Advanced Himawari Imager, peta batas administrasi dan tutupan lahan. Pemrosesan data citra satelit mencakup pemilihan piksel penutup lahan dan batas administrasi, penentuan waktu pengamatan, eliminasi piksel awan, Algoritma Pemantau Kebakaran Aktif, dan validasi hasil. Data citra Himawari-8 dapat diolah menjadi titik panas dengan temporal 10 menit. Validasi terhadap citra polar memiliki tingkat akurasi 66,2%-75,4%, comission error 28,2-46,9% dan omission error 24,6-33,8%. Tingginya comision error terhadap citra VIIRS dikarenakan citra VIIRS memiliki resolusi spasial yang jauh lebih tinggi dibandingkan dengan citra Himawari-8. AbstractForest and land fires occur almost every year in Indonesia, especially in Sumatra and Kalimantan during the dry season. Detection of forest and land fires with satellite imagery uses an indicator called a hotspot. The hotspots used today in Indonesia are obtained from the processing of polar orbital satellite image data (MODIS and VIIRS) with a low temporal resolution, which is only six times a day. The purpose of this study is to utilize Himawari-8 satellite imagery data for the detection of forest and land fires that produce hotspots with a temporal resolution of 10 minutes, where the results are validated with polar imagery and field fire data. The research location is in Central Kalimantan Province, and the time of the study is September 2019. Data used for processing are 5 Advanced Himawari Imager channels, administrative boundary maps, and land cover. Processing of satellite imagery data includes the selection of cover pixels and administrative boundaries, determination of observation time, elimination of cloud pixels, Active Fire Monitoring Algorithm, and validation of results. Himawari-8 image data can be processed into hotspots with a temporal 10 minutes. Validation of polar images has an accuracy rate of 66.2% -75.4%, commission error 28.2-46.9% and omission error 24.6-33.8%. The high commission error on the VIIRS image is because the VIIRS image has a much higher spatial resolution compared to the Himawari-8 image.

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“…In addition, the fire intensity throughout the study period is monitored with the infrared channels from two satellites: Suomi National Polar-orbiting Partnership Program (S-NPP) satellite and Himawari-8, which is a high temporal resolution geostationary satellite. SNPP and Himawari-8 were launched in 2011 and 2016 respectively (Listi et al, 2018;Yumimoto et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Riveaux Road Fire Driven by Dynamic Winds in Tasmania, Australia

Ozaki¹,

Williamson²,

Fox‐Hughes³

et al. 2023

Preprint

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Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km2 in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.

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Spectral Analysis of the Himawari-8 Data for Hotspot Detection From Land/Forest Fires in Sumatra

Cited by 5 publications

References 13 publications

Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

Deteksi Kebakaran Hutan Dan Lahan Menggunakan Citra Satelit Himawari-8 Di Kalimantan Tengah

Riveaux Road Fire Driven by Dynamic Winds in Tasmania, Australia

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