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• W2006750359 abstract "Fine spatial resolution multispectral imagery and light detection and ranging (lidar) data capture differing, yet complementary characteristics of forest structure. Using a dataset consisting of fine spatial resolution multispectral imagery, discrete-return lidar data, and detailed ground-based measurements of individual tree attributes, we applied an automatic tree delineation routine (tree identification and delineation algorithm) to compare and contrast remotely sensed predictions with field observations. The results indicate the automatically extracted crowns derived from lidar data matched tree crown area (coefficient of determination r2 = 0.46, n = 36) and height (r2 = 0.88, n = 36) better than spatial clusters defined in the multispectral imagery (crown area r2 = 0.26, n = 36) for individual trees that were identifiable in both the lidar and multispectral imagery. Differences between crown delineation characteristics were related to the information content of the lidar and multispectral fine spatial resolution data. Investigation of the spectral characteristics of objects defined in the multispectral imagery revealed strong relationships between the vertical positions derived from the lidar data and the apparent multispectral reflectance, with low-reflectance spatial clusters occurring lower in the forest canopy. The application of lidar and multispectral datasets together, in the context of tree crown delineation, provides information not available from either data source independently." @default.
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• W2006750359 date "2004-12-01" @default.
• W2006750359 modified "2023-10-17" @default.
• W2006750359 title "Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data" @default.
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• W2006750359 doi "https://doi.org/10.5589/m04-045" @default.
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