Satellite remote sensing can obtain a wide picture of the earth surface at the same time. But, the data collected from the satellite position are affected by the atmospheric conditions due to the weather. In order to improve quality of the remote sensing image the authors introduce a sophisticated image processing technique, i.e., a connection method. We propose an edge detection and enhancement algorithm to decrease an error probability of the classification of earth surface objects as much as possible. The remote sensing image reflects complex geographical structure. Hence, edges obtained from the corresponding LANDSAT TM data may become complex according to the fluctuation of the earth surface. Furthermore, edges included in the image have many branch points to separate the edges to different directions. In this case, it is difficult to apply directly a traditional method to detect the edges. The connection method stated above is to detect edges on the image by applying the differential operator to the remote sensing data and thresholding the resultant output. Next, the detected edges are connected by the nearest neighborhood method. Applying the above method to the LANDSAT TM data on October 8, 1984, we show the effectiveness of our method to find the edges on the satellite remote sensing image.
Mt. Sakurajima is continually active at present affecting greatly the surrounding living environment with its volcanic ashes. The Volcanic eruption plume emitted from the crater disperses into the atmosphere and is greatly influenced by the wind velocity and the wind direction in the upper layer. It is well known that Landsat MSS 4 and 5 band data are more effective than the other band ones to observe the volcanic eruption plume. Using the Landsat MSS data, we grasped the actual patterns of volcanic ash dispersion over extensive areas, and analyzed its seasonal characteristics and the fluctuation of dispersion as the occasional cases. Analyzing graphically the MSS data, we obtained the relation between spectral emissivity and the distance from the crater, and the relation between the width of dispersion and the distance from the crater. The spectral emissivity decreases exponentially according to the distance from the crater. The width of dispersion to the distance is not always constant, and differs by the conditions of the upper layer. We were convinced that the method of remote sensing using Landsat data is effective for grasping the actual patterns of dispersion of volcanic eruption plumes.
Actual vegetation maps consist of detailed information mainly of association and community classification from the vegetation sociological view point. However, in many cases, they have been left unrevised without the correction due to changes by the elapse of years and update of information being given thereto since the time of their compilation. In this study, we tried to update the vegetation information using TM and SPOT data as well as arranged actual vegetation map scaled 1/50, 000, or and overlaying the images by the unit of pixel. In updating the information, the following methods were employed. 1) Automatic input of actual vegetation map data and its transformation into images 2) Automatic classification by TM and SPOT data, and image interpretation 3) Information update by the overlaying analysis of images by the unit of pixel 4) Output in the form of vector map by converting the information update results from raster data to vector data As a result, we were successful in enhancing the accuracy and establishing method of updating information. In addition, we were successful not only in outputting them in the form of vector map like existing actual vegetation maps. As the problems in the future, it should be necessary to improve the smoothing processing after raster/vector conversion, as well as to develop the smoothing method of vector data itself, to produce maps more similar to the existing actual vegetation maps.