Monday, November 13, 2006 - 8:45 AM
41-2

Identifying the Area Varied by Soil-Water Using Spectral Reflectance ofCrown of Trees Water-stressed.

Ishiguro Etsuji1, Takeshi Yuda1, Dai-taro Ishiwaka1, Hiroki Hiyama2, Toshio Iwamatsu1, Takashi Fukaro3, and Hiroaki Nanba3. (1) Kagoshima Univ, 1-21-24 Korimoto, Kagoshima, 890-0065, Japan, (2) kagoshima university, 1-21-24 Kohrimoto, kagoshima, Japan, (3) Pasco Ltd., 1-21-24 Kohrimoto, Kagoshima, Japan

This study focused on how to identify a disaster stricken area hit by localized heavy rains using remote sensing data of trees grown there. It has been known that plant condition reflects soil water content. There is a good possibility, therefore, that trees in the area caught in a big downpour should show some observable change in the remote sensing data. The spectral responses of coniferous trees, Japanese cedar (Cryptomeria japonica) and broadleaf trees, Castanopsis cuspidate (Thunb) Schottky, var. sieboldii Nakai were measured by a handheld spectroradiometer at different water-stress levels. From this fundamental experiment, a couple of indices were found to be candidates; Normalized Differential Vegetation Index, NDVI and the ratio of green and red regions, RVI, which could represent the level of water-stress. These results were confirmed using aerial photographs taken before and after the disaster and also video camera images taken after the disaster. The concept of activity index, ACT, representing the changes of water-stress was introduced. ACT was derived by the following equation: ACT = [RVIB – RVIA]/RVIB, where RVIB and RVIA represent the RVI indices of before and after the disasters, respectively. These ACT values represented the degrees of hazard level in a location. Images of a video camera with band pass filters were analyzed. Among them three band pass filters, 550nm, 660nm and 770nm, could be used. Indices were, then, derived using the following equations, NDVIBP = (R770 – R660)/(R770 + R660), RVIBP=R550/R660, respectively. This study shows the possibility of identifying the locating the secondary disaster prone areas using aerial photographs taken before and after disasters. Some issues on photo taking conditions are also discussed. Moreover, the effectiveness of using video camera images is demonstrated for situation in which the satellite data or aerial photographs are not available.

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