An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Wang, Wanting; Qu, John J.; Hao, Xianjun; Liu, Yongqiang; Sommers, William T.

doi:10.1016/j.rse.2006.11.009

Cited by 63 publications

(31 citation statements)

References 21 publications

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“…As mentioned in the introduction, some previous studies demonstrated that the multi-threshold method is simple and practical [18,21,[26][27][28][29]. However, it has some limitations such as the thresholds need to be adjusted due to the geographic differences.…”

Section: Results Of the Multi-threshold Methodsmentioning

confidence: 99%

“…In order to distinguish smoke plumes from other cover types more accurately and identify thin dispersed smoke, the integrated detection algorithm based on multi-threshold method [18,28] and BPNN [37] consists of the following steps:…”

Section: Algorithmmentioning

confidence: 99%

“…Multi-thresholds adopted in this study were modified based on the smoke detection algorithm developed by Xie et al [28] and Wang et al [18].The following criteria were used to identify smoke pixels:…”

Section: Multi-thresholds For Various Cover Typesmentioning

confidence: 99%

“…Even though there are different kinds of clouds in the atmosphere, in this study, we make an assumption that clouds are treated as a unified entity (large and cool clouds). The following conditions have been proven to be adequate for identifying large and cool clouds [18,39]. Thus, the pixel that satisfies the following criteria was considered as cloud: …”

Section: Multi-thresholds For Various Cover Typesmentioning

confidence: 99%

“…It has potential applications in air quality assessment, fire detection and fire behavior analysis [17], e.g., the detection of small and cool fires [18,19] and deduction of the process of fire propagation [20]. Smoke detection based on remote sensing has been a hotspot in the past several decades.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Song

Lian

et al. 2015

Remote Sensing

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Abstract:Satellite remote sensing provides global observations of the Earth's surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification algorithm was improved based on the spectral analysis among the smoke, cloud and underlying surface. In order to get satisfactory results, a multi-threshold method is used for extracting training sample sets to train back-propagation neural network (BPNN) classification for merging the smoke detection algorithm. The MODIS data from three forest fires were used to develop the algorithm and get parameter values. These fires occurred in (i) China on 16 October 2004, (ii) Northeast Asia on 29 April 2009 and (iii) Russia on 29 July 2010 in different seasons. Then, the data from four other fires were used to validate the algorithm. Results indicated that the algorithm captured both thick smoke and thin dispersed smoke over land, as well as the mixed pixels of smoke over the ocean. These results could provide valuable information concerning forest fire location, fire spreading and so on.

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Section: Results Of the Multi-threshold Methodsmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Multi-thresholds For Various Cover Typesmentioning

confidence: 99%

Section: Multi-thresholds For Various Cover Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Song

Lian

et al. 2015

Remote Sensing

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Comparison of GEOS‐Chem aerosol optical depth with AERONET and MISR data over the contiguous United States

Garay

Rees

et al. 2013

JGR Atmospheres

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[1] Aerosol optical properties simulated by the global 3-D tropospheric chemistry and transport model Goddard Earth Observing System (GEOS)-Chem (GC) from 2008 to 2010 over the contiguous United States were evaluated with ground observations from Aerosol Robotic Network (AERONET) sites and aerosol products reported by the Multiangle Imaging Spectroradiometer (MISR). Overall, the correlation coefficient (r) and regression slope between AERONET and GC 2°× 2.5°( 2°latitude × 2.5°longitude) daily total column aerosol optical depth (AOD) were 0.6 and 0.51, respectively. After using the nested GC 0.5°× 0.667°m odel to control for spatial variability, removing several outliers, and averaging over a monthly timescale, the agreement was significantly improved to an r of 0.84 and a slope of 0.75. Seasonal, hourly, and geographical statistics for GC 0.5°× 0.667°a nd AERONET AODs show a similar data range and variation, with higher mean values in the summer, the evening, and in the eastern U.S. Smaller correlation coefficients are seen in the summer and winter, in the evening, and in the western U.S. To investigate the optical properties of major GC tracers, MISR level 2 aerosol products were used to calculate inorganic aerosol, dust, and absorbing non-dust AOD. Both GC and MISR suggest that on average, inorganic aerosol has the highest AOD (GC: 0.071, MISR: 0.089) nationally, followed by absorbing non-dust species (GC: 0.025, MISR: 0.041), and dust (GC: 0.013, MISR: 0.014). The large discrepancies in our intercomparison are due to GC underestimation of inorganic aerosol levels during all four seasons in the western U.S. and dust during summer in the eastern U.S., along with overestimation of summertime-absorbing non-dust species over the northwestern U.S. These uncertainties are attributed to underestimation of inorganic aerosol emissions in more polluted western regions, the transport of Sahara dust in the summer, misuse of the fire files, MISR retrieval uncertainties in the surface, and choice of aerosol models.

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Comparative Analysis of Fire Detection Algorithms in North China

Guo

Liu

et al. 2021

6th International Symposium of Space Optical Instruments and Applications

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An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Cited by 63 publications

References 21 publications

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Comparison of GEOS‐Chem aerosol optical depth with AERONET and MISR data over the contiguous United States

Comparative Analysis of Fire Detection Algorithms in North China

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