Snow category extraction of NOAA/AVHRR images by using three dimensional histogram

It is important to judge the presence of the snow for prevention of the snow damage to which crops and livestock suffer. However, because of the few meteorological observation point, enough information is not obtained in the plateau region like Mongolia. In this research, the snow area is classified only by the NOAA/AVHRR image assuming application in Mongolia, using three dimensional histogram, and using neither the specialist´s judgment nor the grand truth data. The validity of this method is examined compared with snow information that the specialist classified, as the preliminary research in the Tohoku region in Japan. In this method, snow data inside the NOAA images are collected by visual observation, and the snow database is constructed. However, because the image with the snow was few, the data base of an enough size was not able to be constructed. Then, the form of a snow category is extracted using the advantage in which three dimensional histogram was able to be analyzed interactively, and a snow area was classified. As for verification, misclassification rate is that the difference between classified snow area by this method and by specialist´s method is divided by the specialist´s classification. Misclassification rate became about 50% as a result. Furthermore, misclassification area found that it concentrated on the boundary of the snow. In the boundary, snow is very vague with a resolution of NOAA/AVHRR. When 1 pixel is permitted from the boundary, misclassification rate became about 25%. And when three pixels permitted, it became about 11%. From the above, snow could be classified in the considerable precision by using neither specialist´s judgment nor ground truth data. It is applicable in the area with few meteorological observation points like Mongolia, by using this method. The possibility of prevention of damage, such as early precaution of the snow damage, was able to be found