0000000000586691
AUTHOR
Erkki Tomppo
Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification
We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0 % was obtained when all features were used. The highest classification accuracy (79.1 %) was obtained when the amount of features was reduced from the initial 328 to the 100 most imp…
Cost-effective forest conservation and criteria for potential conservation targets: a Finnish case study
International audience; Selecting reserves for forest biodiversity maintenance is often done by setting criteria for components of structural elements of biodiversity, such as a volume of decaying wood. We tested how the different threshold values for the components of structural elements affect the cost-effective site selection. Using Finnish National Forest Inventory information and remote sensing data, we determined a habitat quality index and economic value for each site in Satakunta region in Finland. Moreover, we defined several sets of potential conservation targets using alternative criteria for the habitat quality index developed for the Finnish case study. These figures were used …