A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory
Two non-parametric estimation techniques were tested in two study areas in Ireland. For each area, plot level estimates of standing volume per hectare and basal area per hectare were computed from the National Forest Inventory field data and combined with SPOT 4 XS satellite imagery and a digital elevation model to form a set of observations. These observations were then used to predict variables across the satellite image using k-Nearest Neighbour (kNN) estimation and a Random Forest algorithm. Comparisons between the two techniques were assessed based on the estimation errors primarily using the Root Mean Square Error (RMSE) and relative mean deviation (bias). In both study areas it was found that the RMSE was lower for kNN than for RF. Overall, the RMSEs and mean deviations were lower in Study Area 1 when compared to Study Area 2, largely due to a difference in the number of available NFI reference plots.
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Document Type: Research Article
Affiliations: School of Biology and Environmental Science, College of Life Sciences, University College Dublin, Belfield, Dublin, Ireland
Publication date: 2009-10-01