The study using the Landsat image, MSS and RBV pictures with the SAR data, aerial photograph and a topographical map of scale 1:500, 000 reveals that Kyushu island consists of several blocks like a mosaic, and the volcanism takes place along the fractures between these blocks resulting in the formation of ore deposits of gold and silver, geothermal field and hot springs. Based on the interpretation of the lineaments of the Landsat image, the main lineaments are divided into three patterns by its direction and geological significance : NE-SW and N-S series, and NW-SE series which sometimes cut the others. The analysis of the lineament patterns suggests that both the remarkable bent feature of the Plio-Pleistocene Miyazaki Group and the genesis of the Kagoshima graben, can be explained as the result of left-lateral movement along the NW-SE fracture with the opening of the Kagoshima bay since the PIio- Pleistocene.
This paper attempts to extract damaged areas due to two forest fires occurred on April 27 in 1983 simultaneously in Tohoku District. The test sites are Kuji-shi in Iwate-Ken and the northen part of Sendai-shi in Miyagi-ken, which includes Sendai-shi, Izumi-shi, Rifu-cho, Tomiya-cho and Taiwa-cho. The supervised maximum likelihood classification method is employed to extract forest type and land cover distribution from the MSS data taken before the forest fire and to extract the damaged area from the data taken after the forest fire. By combining the two classification images, the areas for forest damage are computed by the degree of damage and by forest type. Also, the damaged areas by administrative district are computed using the administrative district mesh data of Digital National Land Information. As an alternative method of supervised classification, an unsupervised classification, cluster analysis, is tried to estimate the damaged forest area. These estimated areas by Landsat data are compared with the statistical report by Miyagi and Iwate Authorities. As the result of comparison, about plus/minus ten percent accuracy was obtained in total damaged area estimation at the test sites using both of supervised and unsupervised classification method, although the error includes both of the error for forest area estimation and the damaged area estimation. The estimation error is modified using the forest area estimation error obtained by the comparison between Landsat classification and the land use data of Digital National Land Information. According to the result of this modification, very accurate estimation which error is lower than one percent is expected in total damaged area estimation using the supervised classification method. In the case of unsupervised cluster analysis, the estimation accuracy is considered to be rather lower than that of the supervised classification, however, the method is expected to be useful in the application of Landsat data for forest disaster assesment in foreign countries.
It is so far from a simple problem to identify the true relation between the actual measurements on the ground or sea and the remote sensing MSS data that some statistical model like the canonical correlation model or the simple regression model must be assumed for the approximate description of the phenomena. Thus the correlation and regression analysis is very widely used for the remote sensing study like the water characteristics estimation, but what statistical models should be is hardly discussed, while so many misuses of the statistical methods are done with the inference based on the wrong model. The main purpose of this paper is to explain how to build a statistical regression model for the remote sensing study. In this paper the view that the observation for ground (or sea) truth data is done to calibrate the measurement by the remote sensing, is presented, that is, the remote sensing data are considered as the substiture characteristic of the truth data. Then the following 2 points become very important in theuse of the regression model; 1) Not regression of ground truth on remote sensing data, but regression of remote sensing data on ground truth. 2) Meaninglessness of the correlation coefficient of the bivariate data obtained by some experiments. Some example of the real data analysis is also given to clarify the idea.
This paper describes the feasibility of analyzing multi-spectral data using a micro-computer, in which a mini-floppy disk is used as a data storage device. The mini-floppy disk has a storage capacity of 256 pixel times 200 line times 4 channel data by 8 bits. The system configuration used for this study was as follows ; 1. Micro-computer (MZ-2000) /RAM memory 64K bytes 2. Mini-floppy disk/two drives, 320K bytes per drive 3. RGB color monitor/200 rows of 640 columns in 8 colors Using the above system, the following results were obtained. (1) land classified image by 8-level slice of Landsat MSS7 data, (2) ocean current by 19-level slice image of NOAA/AVHRR ch 4 data, (3) false color expression of Landsat MSS data by three gradations, (4) land classified image based on aerial MSS data, (5) expression of the earth by level slicing of Meteosat visible data, (6) landscape of central Japan in false color from NOAA/AVHRR data. These results have given us some idea of the capabilities of the present-day micro-computer for analyzing multi-spectral data. Total cost of this system was \430, 000 (about $1, 800) as of January 1983. Informations to be extracted from presented images: Plate 1. Landuse of Kansai district Plate 2. Kuroshio current off Kii peninsula Plate 3. Vegetation of Imperial Palace and vicinity Plate 4. Ocean and Land of the Western Hemisphere Plate 5. Ocean and Land of the Mediterranean Sea Plate 6. Topography of Kansai district Plate 7. Vegetation and urbanized area in Tokyo Plate 8. Landscape of Central Japan
Remotely sensed imagery data, represented by Landsat MSS data are very useful for research of regional environment, however, just the results of the analyzed data and its display are not always sufficient for putting them into practical use. In order to investigate a regional environment from various viewpoints on the spot, geographical and social data ought to be considered as well as the remotely sensed imagery data. An interactive image processing system is developed for this purpose. In this paper the development of Hiroshima Image Processing System (HIPS) is described. HIPS consists of the hardware of micro-computers and peripheral I/O devices for image data processing and of the softwares for interactively processing remotely sensed imagery data and other social data. HIPS was designed and manufactured on an experimental basis within limited research expense, aiming not only to contribute to education and research of remote sensing in the institute, but also to perform further researches of various fields.
Remote sensing systems usually require large computers for processing large amounts of data. However, recent expansion of application fields demand some easily handled and also low cost system. Due to recent improvements in performance and cost, micro-computer can now fill that role. This paper addresses systems for analyzing the data of remote sensing using micro-computer.