TY - JOUR
T1 - A new algorithm for forest fire detection method with statistical analysis using NOAA AVHRR images
AU - Kalpoma, Kazi A.
AU - Kudoh, Jun Ichi
PY - 2006/9/20
Y1 - 2006/9/20
N2 - Various researchers have carried out forest fire analysis using NOAA satellite images. There are several methods of doing this, and most can detect a fire. However, many false fires were also detected, and, in some cases, actual fires were missed. We analysed four satellite-based fire detection methods using data from AVHRR of NOAA-16 over a period of three to six months for the Sakhalin region and the Japan region. Considering the fundamental differences, problems, and effectiveness of these methods, we have constructed an improved fire detection method with statistical analysis. Our method has reduced false fire detection significantly, as well as detected actual fire with accuracy.
AB - Various researchers have carried out forest fire analysis using NOAA satellite images. There are several methods of doing this, and most can detect a fire. However, many false fires were also detected, and, in some cases, actual fires were missed. We analysed four satellite-based fire detection methods using data from AVHRR of NOAA-16 over a period of three to six months for the Sakhalin region and the Japan region. Considering the fundamental differences, problems, and effectiveness of these methods, we have constructed an improved fire detection method with statistical analysis. Our method has reduced false fire detection significantly, as well as detected actual fire with accuracy.
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U2 - 10.1080/01431160600784226
DO - 10.1080/01431160600784226
M3 - Article
AN - SCOPUS:33750086337
VL - 27
SP - 3867
EP - 3880
JO - International Joural of Remote Sensing
JF - International Joural of Remote Sensing
SN - 0143-1161
IS - 18
ER -