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Classification of forest stand considering shapes and sizes of tree crown calculated from high spatial resolution satellite image

Forests play important role as environment for living things. There are many types of forest stand and the identification of forest type is available to conserve and to improve the forest. The field investigation on forest area is a hard work and needs many cost. The field investigation on wide area is almost impossible. The remote-sensing is useful for investigation of wide area of forest, especially on mountain area where field investigations are usually laborious. The spectral characteristics are different by the species. Many classifications of forest type based on spatial characteristics are operated. But, some kinds of forest types are difficult to be classified by using only spatial characteristics. Each type of forest stand has a spatial characteristic depending on the shape of trees. By using not only spatial characteristics but also tree shapes, number of classified types of forest can be increased. We developed the method for the delineation of tree crown with image processing using satellite images with high spatial resolution and aerial photographs. In this method, tree crowns that have similar color are delineated with circles. Densities, size and numbers of trees are different by species, and these information are available to classify the type of forest stand. In this study, forest stands were classified with not only spatial information but also the number and densities of trees. Multi-spectral images and pan image of IKONOS were used in this study. The NDVI (Normalized Difference Vegetation Index) was calculated from red and near-infrared images. Densities and numbers of trees on a forest stand were calculated using pan images with high spatial resolution by using the method for crown delineation with circler expression. The types of forest stand were classified using thresholds of NDVI and numbers of trees. A forest classification method using spatial information, densities and numbers of trees increased the number of classified type of forests than using only spatial information.