Comparative analysis of daytime fire detection algorithms using AVHRR data for the 1995 fire season in Canada: Perspective for MODIS

Abstract

Two fixed-threshold (CCRS and ESA) and three contextual (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in non-forest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors. The poor performance of the algorithms (in terms of omission errors) is not only due to their quality but also to cloud cover, low satellite overpass frequency, and the saturation of AVHRR channel 3 at about 321 K. Great improvement in global fire detection can probably be achieved by exploring the use of a wide variety of channel combinations from the data-rich MODIS instruments. More sophisticated algorithms should be designed to accomplish this.

Modelling of long-range transport of Southeast Asia biomass-burning aerosols to Taiwan and their radiative forcings over East Asia

“…With biomass-burning fires occurring in spring, its impact on the air quality and the regional climate are the major concerns to the countries affected. Recent advent of satellite monitoring systems, such as the Along-Track Scanning Radiometer (ATSR) (Kasischke et al, 2003), Advanced Very High Resolution Radiometer (AVHRR) (Ichoku et al, 2003), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Giglio et al, 2006a, Giglio et al, 2006bvan der Werf et al, 2006;Boschetti et al, 2008), has provided useful information for identifying fire events and the duration of burning. However, the information provided by satellites is still limited, especially in terms of the critical information on composition of aerosols.…”

Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization

“…Satellite remote sensing offers a complementary, cost-effective tool for spatially comprehensive northern monitoring but at a coarser spatial resolution. Remote sensing has successfully been used to detect and map large-area landscape changes related to Arctic lake extent [3,4], storm-surge vegetation dieback [5], glacier retreat [6], vegetation greening [7], and forest fires [8][9][10] over Canada's North.…”

Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI

“…the intense thermal emission from combustion [31], [43], to the albedo and spectral reflectance of newly burned surfaces, and to the presence of smoke plumes containing trace gas and aerosols in highly elevated concentrations [32], [66]. For these reasons and because of the widespread but highly variable nature of global biomass burning activity, observations from Earthorbiting remote sensing satellites are considered the key to better characterizing the extent and influence of this phenomenon.…”