Simultaneous estimations of forest parameters using aerial photograph interpreted data and the k nearest neighbour method

Information about the state of the forest is of vital importance in forest management planning. To enable high-precision modelling. many forest planning systems demand input data at the single-tree level, The conventional strategy for collecting such data is a plot-wise field inventory. This is expensive and, thus, cost-efficient alternatives are of interest. During recent years, the focus has been on remote sensing techniques. The k nearest neighbour (kNN) estimation method is a way to assign plot-wise data to all stands in a forest area, using remotely sensed data in connection with a sparse sample of field reference plots. Plot-wise aerial photograph interpretations combined with information from a stand register were used in this study. Nearness to a reference plot was decided upon using a regression transform distance. Standing stem volume was estimated with a relative root mean square error (RMSE) equal to 20% at the stand level, while age could be estimated with a RMSE equal to 15%. A cost-efficient data-capturing strategy could be to assign plot data with the presented k-NN method to some types of forest, while using traditional field inventories in other, more valuable, stands.