In this study, we assessed the feasibility of road area extraction from LIDAR height and intensity data. We tested two methods. One is a maximum likelihood method using color image and LIDAR data. The other is the method using region segmentation. At first, a height based region segmentation method was applied and flat regions were extracted. Then, each region was distinguished by average intensity in the region and the road candidate regions were picked up. The results of two methods were compared with the result of a maximum likelihood method using color image. It was revealed that the extraction result of the proposed methods was better than that of the reference method. From this study, we concluded that the road extraction with LIDAR height and intensity data could be practicable, though the vectorisation process remains to be solved.

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We propose a new validation method for land cover maps. Land cover maps are used in the numerical models that estimate ecosystem behavior (such as carbon budget), water cycle (such as river runoff), and climate at the global scale. Because of this wide range of applications, accurate validation of these maps is of crucial importance. Currently, each of the existing land cover maps has been validated with its own validation method, but there is no validation method for land cover maps by using ground-truth with fairly uniform and accurate worldwide distribution. We propose a validation method that can address this shortcoming. Our method employs information gathered by “the Degree Confluence Project (DCP), ” a voluntary-based project that collects on site data from each of the degree confluence points (DCPoints) in the world. DCPoints are located at the intersections of integer level latitude and longitude grid lines. Volunteers with the project visit the DCPoints and collect data in the form of GPS readings, pictures and descriptions of the landscape. We propose to validate land cover maps using this DCP data. We choose Thailand as our test area and reclassified 30 DCPoints into the six landscape categories (classes) defined under the Kyoto Protocol's Land Use, Land Use Change and Forestry (LULUCF) guidelines. These DCP derived classifications were then compared to classifications derived from Landsat Thematic Mapper images. Through this method we were able to obtain validation information superior to that of visual interpretation. We also converted land cover classifications from Global Land Cover 2000 (GLC2000), MODIS Land Cover Dataset (MDO12), Global Land Cover Characterization Version 2.0 (USGS) and AARS Global 4-minute Land Cover Data Set (AARS) into the LULUCF framework. These four land cover maps were then compared in the same way with the DCP data to evaluate their agreements. Agreement of the GLC2000 is 76%, MOD12 is 70%, USGS is 73.3%, and AARS is 33.3%.

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Airborne hyperspectral sensor is increasingly being used for the precision agriculture and for the monitoring our environment. In general, data obtained by airborne hyperspectral sensor are affected by atmospheric conditions and solar illumination geometry. Therefore, airborne hyperspectral sensor data are commonly expressed as relative radiance value. For measuring and monitoring ground surface changes through time, it is important to calibrate hyperspectral sensor data to amount of reflectance. A number of calibration techniques have been developed ranging from empirical approaches to analytical radiative transfer approaches. These methods require a priori knowledge such as field reflectance observations or atmospheric conditions.
Several airborne hyperspectral sensor systems which are used for commercial purpose include a fiber optic probe on the aircraft roof. A fiber optic probe is able to monitor skylight reference data to ratio to hyperspectral raw data. This is a simple and practical calibration technique. However, there is a problem that small inaccuracies in skyright reference data calibrations may lead to unacceptable errors in calculated apparent reflectance. In this paper, simple calibration technique based on skylight reference data was discussed. The resultant reflectance estimates are compared with field reflectance observations of flat and homogeneous ground target and illustrate that proposed calibration technique is possible to derive reasonable reflectance from airborne hyperspectral raw data.

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According to appearance of low cost and high resolution consumer grade digital cameras, convenient 3D measurement using the cameras are enormously expected in various fields. However, there are still problems for efficient digital photogrammetry using the cameras. These problems include 3D measurement for ground control point or distance measurement for orientation and previous interior orientation. These restrictions should be removed for ideal convenient photogrammetry using consumer grade digital cameras.
In order to resolve the above problems, efficient camera calibration method which does not need ground control points nor previous interior orientation procedures are proposed in this paper.

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