Extraction of forest attribute information using multisensor datafusion techniques: a case study for a test site on Vancouver Island,British Columbia

摘要

The use of multisensor and multitemporal remotely sensed data isimportant for the extraction of forest attribute information forCanada's 418 million km? of forests. Hyperspectral data can providevegetation signatures for forest attributes and canopy chemistry.Hyperspatial data can be used for individual tree recognition. Data fromP-band synthetic aperture radar (SAR) have yielded accurate timbervolume information in experiments involving managed plantations. Wepresent the results of a study undertaken to assess forest attributedetermination over the Greater Victoria Watershed District (GVWD) testsite on Vancouver Island, BC, Canada from the following airborne andsatellite sensors: Multi-detector Electro-optical Imaging Scanner(MEIS), Airborne Visible Infrared Imaging Spectrometer (AVIRIS), AirSARand LANDSAT-7. GIS information and field data from field spectrometersare used for validation and calibration of AVIRIS and LANDSAT-7 ETM+data. Inventory information (e.g. stem density, biomass) for our plotsis known as a result of both field sampling and data fusion of GIS andhigh spatial resolution MEIS data. Segmentation techniques are appliedto identify spatially homogeneous objects for quantification of forestattributes