Digital canopy models (DCMs) were extracted by multi-temporal aerial photographs (1964∼2004) and digital photogrammetric technique for forest canopy monitoring. In this study, ground contorol points (GCPs) were extracted by IKONOS image and digital elevation model (DEM) from LiDAR data. Bundle block adjustment with self-calibration was used for aerial triangulation, because camera callibration reports were not available. The generated DCM which was obtained in 2004 was compared with DCM from LiDAR data (obtained in 2004). Most of all pixel by aerial photographs were corresponded with DCM from LiDAR data. However, small gap areas were difficult to detect. Excluding gap areas, root mean square error of DCM from aerial photo was 3.78 meter. Using extracted multi-temporal DCMs, forest canopy changes, such as tree growth, clear-cutting and fallen damage, were figured out. Multi-temporal DCMs were found to be effective in monitoring forest canopy.
There are many areas where snow is main water resource and snowfall affects plant growth depending on moisture and water conditions. Therefore, it is important to understand snowfall fluctuations in snowfall areas for environmental monitoring. This paper describes a novel method for generating snow products by using SPOT/vegetation data. We engineered a discrete time-series model using a self-organizing map (SOM) and a hidden Markov Model (HMM) to reduce the influence of clouds in order to improve the accuracy of the snow products. The method developed in this research improved the processing accuracy and determined snowfall with an accuracy as high as 95% as well as reduced the calculation amount to less than one tenth of that required by continuous time-series model processing.
Many standardization organizations are working for syntactic level of interoperability, but in the same time, semantic interoperability of data must be considered for earth observation data. Remote sensing ontology is developed for not only integrating global observation data, but also knowledge sharing and transfer. Ontological information is used for data sharing and data service such as support of metadata design, structuring of data contents, and support of data mining. Semantic network dictionary is constructed based on Semantic MediaWiki. Ontological information are added to the dictionary by digitalizing text based dictionaries, developing “knowledge writing tool” for experts, and extracting semantic relations from authoritative documents with natural language processing technique.
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