A spatiotemporal contextual model for forest fire detection using Himawari-8 satellite data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Author(s)

  • Zixi Xie
  • Weiguo Song
  • Rui Ba
  • Xiaolian Li
  • Long Xia

Detail(s)

Original languageEnglish
Article number1992
Journal / PublicationRemote Sensing
Volume10
Issue number12
Online published8 Dec 2018
Publication statusPublished - Dec 2018

Link(s)

Abstract

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data's spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel's diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

Research Area(s)

  • AHI, Brightness temperature, DTC, Forest fire detection, Kalman filter, Otsu method, Spatiotemporal contextual model (STCM)

Citation Format(s)

A spatiotemporal contextual model for forest fire detection using Himawari-8 satellite data. / Xie, Zixi; Song, Weiguo; Ba, Rui; Li, Xiaolian; Xia, Long.

In: Remote Sensing, Vol. 10, No. 12, 1992, 12.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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