Wetland monitoring and especially wetland vegetation classification are crucial for preserving valuable wetland ecosystems. The development of remote sensing technique for wetland monitoring is of urgent necessity. In order to improve the accuracy of vegetation classification, we have investigated the wetland vegetation classification using multi-temporal Landsat TM images. Because the growth pattern of a wetland vegetation changes according to the vegetation type, we can used this difference of temporal growth pattern which appear in the multitemporal images for classifying the vegetation types. In order to clarify this temporal growth pattern of wetland vegetation types, we have conducted sampling experiments to measure the biomass growth during the growing season. And also spectral reflectance measurements were conducted to see the spectral difference between the vegetation types as well. As the result of supervised classifications using the multitemporal Landsat TM image, an accurate wetland vegetation classification map has been produced.
In general, recording for archeological sites is performed by plane table surveying, leveling or section drawing, expending a great deal of time and labor. Digital photogrammetry is expected to become a useful tool in this field. However, time consumed in geodetic surveying for camera calibration is still an issue which needs to be resolved. By using information such as distance which is included in the image as proposed in this paper, time consuming aspects of geodetic surveying can be improved. This paper describes on a real-time photogrammetric system for site recording by using wireless CCD camera.
Generally, a dynamic analysis of human motion has been performed under a condition that camera position and rotation are fixed and some markers are fitted on the body. Therefore, it is possible to calibrate the camera parameters in advance. Also, automated recognition of some human feature points such as the head, elbow or knees is possible. In order to understand a dynamic analysis of the most natural human motion, limitation of the camera and any marker on the body should be removed. For this ideal dynamic analysis, however, camera orientation parameters should be acquired in real time while recording a moving object. Furthermore, automated recognition of some human feature points should be performed. The effectiveness of the video theodolite system for dynamic analysis of human motion has been indicated by the authors. This paper describes the dynamic analysis of human motion using sequential images which are taken by video theodolite. Also, image processing techniques are described.
Canopy reflectance model is a useful tool to correct the effect of measuring conditions like as the changes in observation angle and solar irradiance angle. So that, if we use the corrected reflectance data by the model for estimating vegetation index such as NDVI, we can derive more accurate information of vegetative distribution from satellite data. Nowadays, the famous model for canopy reflectance is the SAIL model. (Verhoef, 1984) This study notices that the leaf reflectance parameter of the SAIL model is considered only front surface of leaves. And so, we survey the influence on canopy reflectance. As a result of it, we find that it is not adequate to predict canopy reflectance by using only front surface reflectance of leaves.