Estimating wood volume and basal area in forest compartments by combining satellite image data with field data :

Abstract: Landsat TM satellite image data and field data from the National Forest Inventory (NFI) were combined using the kNN method. Wood volume and basal area estimations were done for an area in western Sweden. For each pixel in the inventory area, the distance in feature space was calculated to each reference (NFI) plot. The estimation for each pixel in the inventory area could be determined by choosing attribute values for the k closest neighbours in feature space and weighting them with the inverse squared distances to the estimation pixel. Two distance functions were used; Mahalanobis distance and prediction difference. The pixels were aggregated into 296 validation compartments and an average for wood volume and basal area was derived. Validation data for the compartments was acquired from a field inventory conducted by STORA Forest and Timber. The main tree species were Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula spp.). Volume and basal area could be estimated with a standard error of 21 % by combining satellite image and NFI data for compartments in the study area that had a volume between 100 and 300 m3 /ha. The standard error was high and wood volume was strongly overestimated for volumes less then 100 m3 /ha. Wood volume was underestimated by 33 % for volumes greater then 300m3 /ha. Neither correcting of reflectance for topography nor adding temperature sum improved wood volume estimation accuracy in the validation compartments. However, wood volume and basal area estimation accuracy improved if site index, age and mean tree height were included. The standard error was then 12% for compartments with a volume between 100 and 300 m3 /ha. Using Prediction difference reduced the RMSE, compared to using Mahalanobis distance, if mean tree height was included as predicting variable.