Identification of individual tree crowns from satellite image and image-to-map rectification

In forest area, there are few landmarks to be ground control points (GCPs) used for registration of satellite images or maps. Additionally, geographic information from the Global Positioning System (GPS) in field measurement survey is insufficient accuracy to identify individual tree crowns from satellite image. In this study, we propose the method of identifying individual tree crowns from satellite image using field measured data. First, in order to obtain the field measured data, we collected several information of individual trees in the test site. These are the tree stand locations, the distances between the tree trunk and outermost branch in eight directions, the diameter at breast height, and tree species. This survey was carried out on 20 September 2006. The area of this site is 160 meter by 80 meter, and there are about 60 canopy trees. Then, using the field measured data, we created the projected on-ground crown map which has the location and shape of individual trees. The each shape of tree crown is octagonal. Next, we detected the regions of tree crown from satellite image. In this study, we used an IKONOS panchromatic satellite image. The spatial resolution of analysis image is 1 meter per pixel. It can be recognized and identified an individual tree crown whose radius is more than 2 or 3 meter. Watershed algorithm was used for image segmentation, based on mathematical morphology considers gray-scale images to be sets of points in a three-dimensional space, the third dimension being the gray level. A gray scale landscape may be segmented according to the watersheds of the image. The segmented regions were classified to discriminate tree crown using the feature of spectral signature. Finally, we found out individual tree crowns related with field measured data from satellite image. Using a GCP by GPS equipment, we performed roughly registration of the satellite image to the projected on- ground crown map. For each tree crown in the map, we found out the - same tree, which has the highest corresponding possibility to the tree crown in the map, among segmented regions obtained from satellite image. This tree-to-tree matching algorithm was performed using the fitness value of the location and octagonal shape of both tree crowns in image and map. We could obtain the optimum registration by afflne transformation of highest fitness value without ground control points. Consequently, we could identify individual tree crowns from satellite image by image-to-map rectification.