Prediction of forest stem volume using kriging adapted to detected edges

Abstract

The modern techniques of the global positioning system and geographic information system enable many new approaches to forestry planning problems. Using these it is possible to efficiently geoposition, store, and analyze each field measurement in a spatial context. This work is directed towards the application of a dynamic forestry planning system based on a forest map with very high spatial resolution created from geopositioned field plot data, instead of the traditional forest stand map. The new dynamic system is dependent on accurate methods to create a high-resolution map from a set of field measurements. This problem may be solved using the kriging spatial prediction (interpolation) method. The aim of this paper is to present and empirically evaluate a new kriging method side-by-side with global and stratified kriging. The new method uses the output from an edge-detection algorithm, here applied to Landsat TM image data, to increase the prediction accuracy. Prediction evaluation was made in terms of mean forest stem volume per hectare measured on circular field plots of 10 m radius. The new method showed a prediction root mean square error of 41% of the mean volume, compared with corresponding results of global, 58%, and stratified kriging, 45%.