The objective of this study is to clarify the effect of the surface under tree crown to the remote sensing imagery.Using the airborne hyperspectral sensor imagery, the second differential absorbance in row of trees of Prunus × yedoensis in Rissho University was calculated.In reflectance of the road and grass under crown, the values in the infrared region was different each other.But the reflectance spectrum was similar.The influence of the different surface under crown was not able to be judged from the difference of the value of reflectance.On the other hand, the second differential absorbance showed the difference of the surface under crown.The difference appeared at a point of the peak and trough in the distribution of the second differential absorbance.The classification error in the crown part was about 20% in reflectance, and about 2% in the second differential absorbance.About 70% of the edge of the crown where was above the road was recognized as the result of the influence of the road from the first principal component score. This study showed that the difference of the surface under the crown could be detected by the second differential absorbance of the forest structure calculated by the hyperspectral imagery.
We propose an automatic and nearly versatile method for detecting forest canopy gaps using the digital fish-eye camera images. By analyzing data obtained by a field research during the entire growing-season in a cool-temperate deciduous broad-leaf forest in Takayama, Japan, we found the following criteria mostly applicable except for canopies with autumn-color : 1. The pixels with the digital number of the blue band less than 150 were at the vegetation (leaves and stems) . 2. The pixels with the digital number of the blue band more than 200 were at the canopy gaps. 3. The pixels with the digital number of the blue band between 150 and 200 were at the canopy gaps if their digital numbers of red were less than those of blue. Otherwise they were at the vegetation. Estimated canopy gap fraction by these criteria corresponded well with those estimated by the conventional and subjective approach. We compared gap fraction by these criteria with gap fraction by the other approaches and a transmittance by LAI2000. These comparisons indicated necessity of consideration about sensitivity and exposure of the digital cameras. The values of the leaf area index estimated from those gap distributions were much less than the estimation by the canopy light transmittance approach but the pattern of their seasonal changes (except for autumncolor) were close to each other.
An unmixing method for hyperspectral data based on subspace method with learning process is proposed. Unmixing methods can be divided into three categories, inversion method, SVD (Singular Value Decomposition) based method, and subspace method. Although these methods works well if the distribution of the hyperspectral data in feature space can be represented as somewhat convex function, it is now allways true. Unmixing method proposed here does work even if the distribution is expressed with concave function because the method adjust the axis of subspace through a learning process. Through experimental studies with AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) data, it is found that the proposed method achieves 16.3% of improvement of the unmixing accuracy in terms of root mean square error in comparison to the well known least square unmixing method. Also it is found that the proposed method shows 15.0% better accuracy in comparison to the subspace based unmixing method. The reasons for the improvement are clarified in a comprehensive manner with the simple example of the 3D feature space together with two categories.
Vegetation was classified using aerial hyperspectral sensor (CASI-3) data and field survey data for the beech-forested Shirakami Mountains in Aomori Prefecture, Japan. Previous study on German forest reported that different plants show similar hyperspectral response patterns and that the accuracy of vegetation classification using aerial hyperspectral sensor data alone was 66%. In this study, radiance in each grid was divided by average radiance in a vegetation-coverage area. This“normalized”spectral response pattern enabled finer plant discrimination than the non-normalized patterns used in the previous study. Plants typifying those in the study area were selected, their radiances were investigated at ground truth sites, and eight vegetation classifications were selected : walnut (high and low radiance), beech (high and low radiance), altherbosa, dwarf bamboo, bare ground, and other. Classification accuracy was 77.6% at the ground truth sites, but the classification accuracy for each of altherbosa, bamboo grass, and bare ground was 100%. Walnut classification accuracies (63.6% for high radiance and 50.0% for low radiance) were equal to or lower than beech classification accuracies (50.0% for high radiance and 88.9% for low radiance) . When radiance is disregarded, classification accuracy was 76.5% for walnut and 80.0% for beech. The classification accuracy of 76.5% for walnut 80.0% for beech suggests that aerial hyperspectral sensor data can be used to map vegetation.
The topographic information on the mountainous area is used in many engineering fields. Hydrological analysis is one of these fields. In these application fields, the spatial resolution of DEM is required for analysis specifications. Usually, the spatial resolution of the available DEM data is not sufficient to analyze. However, the ‘SRTM 90m DEM’ produced by NASA, is available over the world wide area. It has a spatial resolution of approximately 90 meter. In this paper, the new technique of the DEM subdivision was examined with practical use. This new technique preserves the topographic breakline. The raster coordinate values of the topographic breakline are extracted first in the new technique. The extracted raster breakline data is converted into vector data. Adding the breakline vector data to the DEM subdivided by mathematical interpolation, the subdivided DEM which preserved the topographic breakline is regenerated. Regenerated DEM was compared with the DEM generated from the scale of 1: 25, 000 topographic maps which have a spatial resolution of 10 meter.
This paper introduces the availability of satellite image interpretation on the failures of irrigation weirs on a small stream in northeast Thailand. For the purposes, high resolution satellite imageries, QuickBird, were employed to carry out visual interpretations. As the results, the weir failures such as river side collapse due to obstructed flood flow and sedimentation on riverbeds can be identified. Through a series of image interpretations, the effectiveness of high-resolution satellite imagery for field science and investigation was confirmed. Also the possibilities were indicated that the actual status of agricultural facilities can be monitored remotely for maintenances and managements by using high resolution satellite imagery.
This research focuses on a near-real time network based active fire monitoring over Asia using MODIS onboard Aqua and Terra satellite. Firstly, the algorithm used for fire mapping is described that is basically improved on the heritage of AVHRR using 4- and 11-μm thermal channels. Secondly, our fire product is described that is mainly composed of fire mask along with latitude and longitude tables supplemented by confidence value in percent. Thirdly, our current status of fire product production is presented and the way to obtain them through FTP or HTTP is described. Finally, some caveats to bear in mind when using our fire product and our future works are described for the refinement of our algorithm.