Individual tree recognition from multiple high spatial resolution image sources

The availability of high resolution (1 m or better) imagery from space opens up the possibility of automatic detection of coniferous trees. Our test site is located within the Greater Victoria Watershed (GVWD) on Vancouver Island, British Columbia, Canada. In previous research we have examined various filters for detecting trees over an area with mature and immature Douglas fir trees. We have obtained a 1-m spatial resolution digital orthophoto generated from aerial photography, MEIS 1-m multispectral imagery, and IKONOS panchromatic 1-m imagery over our test site. Within the test site, there are ground plots in which the location of each tree has been determined. These detailed plots are used to assess the accuracy of the methods used for tree detection for each of the high resolution image types. The characteristics of each tree are documented allowing for an assessment of the conditions leading to the identification, or lack of identification, of each tree. The comparison of three differing data sources, each with 1-m spatial resolution, indicates favorable results for the IKONOS satellite data. The highest proportion of the trees from the field stem plot data were identified with the IKONOS satellite panchromatic imagery. While the IKONOS results have a higher rate of false positives than the airborne multispectral data, a preference for the satellite data is due to characteristics such as ease of collection, large image extent, repeatability, and radiometric consistency over a larger area.