Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland

90 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Non-parametric prediction of diameter distributions using airborne laser scanner data (2009, methodology): “It should also be noted as a general benefit in ALS-based forest inventories that the modelling accuracy of the derived stand attributes in ALSbased studies is usually close to the validation accuracy (e.g. Næsset, 2002; Maltamo et al., 2009).”
Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data (2020, methodology): “In the study by Maltamo et al. (2009), 69 independent validation stands with an average area of 1 ha and age ranging from 0 to 126 years were used, resulting in stand level RMSE of 18.3 years (36”
Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data (2010, methodology): “Maltamo et al. (2009) used MSN and variable-radius sample plots from the Finnish NFI to predict tree speciesspecific forest stand attributes.”