Smoke plume detection in the eastern United States using MODIS

Xie, Yonghong; Qu, John J.; Xiong, X.; Hao, Xianjun; Che, Nianzeng; Sommers, William T.

doi:10.1080/01431160701236795

Cited by 36 publications

(29 citation statements)

References 16 publications

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“…In the analysis we focused on four typical cover types, i.e., smoke, cloud, water and vegetation, in the study areas. The bare soil can be masked by certain criteria that were mentioned in Xie's study [28].…”

Section: Spectral Analysis Of Potential Smoke Plumes and Underlying Smentioning

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%

“…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%

“…Generally speaking, the method employs a set of thresholds to automatically check all image pixels to filter non-smoke pixels step by step [21,[26][27][28][29]30]. Baum and Trepte [27] adopted a grouped threshold method for scene identification in NOAA/AVHRR imagery that may contain clouds, fire, smoke or snow.…”

Section: Introductionmentioning

confidence: 99%

“…Randriambelo et al [26] proposed improved multispectral methods for detecting fire smoke plumes in NOAA/AVHRR imagery by using a multi-channel in visible and thermal ranges in south-eastern Africa and Madagascar. Xie et al [28,30] developed a multi-threshold method for smoke detection by using eight MODIS spectral bands based on the spectral characteristics analysis of different cover types. Zhao et al [29] used MODIS images for smoke detection based on spectral and spatial threshold test along with some uniformity texture.…”

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: Spectral Analysis Of Potential Smoke Plumes and Underlying Smentioning

confidence: 99%

Section: Multi-thresholds For Various Cover Typesmentioning

confidence: 99%