Abstract
Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.
Highlights
The use of remote sensing satellites for detecting and imaging natural disasters has steadily increased with the availability and reliability of sensors in orbit
This study make use of the robust matching algorithm described in (Roberts and Wooster, 2014), which adapted a form of single value decomposition described in (Black and Jepson, 1998) inclusive of a method to reduce the influence of outliers on the derived diurnal temperature cycle (DTC)
The skin surface temperature of the earth is influenced by a number of factors - rain lowers surface temperatures very effectively, convective cooling and heating due to air masses can influence surface temperatures, and percentage land cover can affect the magnitude of temperature change - but the most notable influence is heating by solar radiation
Summary
The use of remote sensing satellites for detecting and imaging natural disasters has steadily increased with the availability and reliability of sensors in orbit. Changes to the thermal signature of the land surface can be caused by a number of disaster types - flooding will cause a flattening of temperature response in a diurnal cycle, with cooler high temperatures and warmer minimums, and fire will cause temperatures to increase, with a significant impact in the medium wave infra-red due to fire temperatures and their corresponding blackbody radiation outputs. These changes can occur rapidly with the spread of the event, so they can be used to determine the extents of the affected area
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