The individual tree crown approach applied to Ikonos images of a coniferous plantation area.

摘要

In forestry, the availability of high spatial resolution (<1 m/pixel) imagery from new earth observation satellites like Ikonos favours a shift in the image analysis paradigm from a pixel-based approach towards one dealing directly with the essential structuring element of such images: the individual tree crown (ITC). This paper gives an initial assessment of the effects of 1 m and 4 m/pixel spatial resolutions (panchromatic and multispectral bands, respectively) on the detection, delineation, and classification of the individual tree crowns seen in Ikonos images. Winter and summer Ikonos images of the Hudson plantation of the Petawawa Research Forest, Ontario, Canada were analysed. The plantation comprise species such as Pinus resinosa, Pinus sylvestris, Picea rubens, Picea glauca, Picea abies, Pinus strobus, Populus spp., Betula papyrifera, and Acer spp. The panchromatic images were resampled to 0.5 m/pixel and then smoothed using a 3x3 kernel mean filter. A valley-following algorithm and rule-based isolation module were applied to delineate the individual tree crowns. Local maxima within a moving 3x3 window (i.e., Tree Tops) were also extracted from the smoothed images for comparison. Crown delineation and detection results were summarized and compared with field tree counts. Overall, the ITC delineation and the local maxima approaches led to tree counts that were on average 15% off for both seasons. Visual inspection reveals delineation of clusters of two or three crowns as a common source of error. Crown-based species spectral signatures were generated for six classes representing conifer species, plus a hardwood class and a shrub class. After the ITC-based classification, classification accuracy was ascertained using separate test areas of known species. The overall accuracy was 59%. Important confusion exists between red and white spruces, and mature versus immature white pines, but post-classification regroupings into single spruce and white pine classes led to an overall accuracy of 67%..