It is well known that the heavy soil dust has been transported from the China continent to Japan on westerly winds, especially in spring. The satellite observation is an effective tool for global monitoring of the Asian dust. A new algorithm for detection of Asian dust from space is proposed based on the multispectral satellite data. The derived space-based results are validated with the ground-based measurements and/or model simulations. The sun/sky photometry has been undertaken at NASA/AERONET stations at Osaka. The suspended particulate matter (SPM) sampling and NIES/LIDAR network equipment have been simultaneously working there. However, it is difficult to relate these ground-based measurements directly to the column space data. Therefore, in order to validate the satellite results with the surface-level data, an aerosol transportation model should be simulated. The measurements are examined with the model simulations. As a result, the distribution of Asian dust aerosols is retrieved in a comprehensive way in this study.
Kharga Oasis is the biggest oasis in the desert region between Egypt and Libya. The geographical location of this oasis suggests its importance in the large network system connecting the Western Desert, the Nile Valley, Nubia and Libya, which functioned as caravan route for slave trading from Sudan to middle Egypt in the Roman age. Egyptian government plan, regarding current population increase, is to develop this area for agriculture and settlement by driving water from the Aswan. This study aims to provide a tentative map of temple and water environment around Kharga Oasis based on ALOS satellite pan-sharpened imagery and information collected from field surveys 2003-2008. We expect the map as a supporting tool for archaeological studies of ancient Egyptian civilization and regional development policies in Kharga Oasis.
While the human population continues to grow and the economy is developing, biodiversity is decreasing in the wake of land development, for instance. However, biodiversity is the essential for humans to obtain ecosystem services. Therefore it is required to preserve biodiversity and its sustainable development. Wildlife monitoring is necessary for its preservation. Recently we can obtain the accurate position of wildlife by GPS (Global Positioning System) telemetry. However the use of telemetry is allowed for a limited number of wildlife. Therefore it is expected to increase the ecological knowledge through our constructing the system which obtains ecological information from remote sensing images. We developed DTR algorithm which is for computer aided detection of animal tracks in the snow using high spatial resolution remote sensing images. DTR algorithm reduces hard labor to find out directly the tracks by visually examination of remote sensing images and avoids overlooking the tracks. This time we apply DTR algorithm to the aerial images taken in Sarufutsu in Hokkaido. And we distinguished species which left the detected tracks by visual examination and discriminant analysis based on field investigation. As a result, animals which left detected tracks were interpreted as sika deer (Cervus Nippon yesoensis) according to length and width of one set of the footprints. Also we estimated population density of target animals by applying INTGEP (Intersection Points Counting Method Based on Geometrical Probability) method to lengths of sika deer's tracks. Comparisons of footprints from the DTR algorithm and from visually examination proved that 76% of the footprints in the snow could be detected using the DTR algorithm. It is shown that automatic detection of the tracks in the snow in remote sensing images is possible using the DTR algorithm.
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