Using Decision Trees to Predict Forest Stand Height and Canopy Cover from LANDSAT and LIDAR Data

Abstract

The motivation for this study was to improve the consistency and accuracy, and increase the spatial resolution of some of the supporting information to the forest monitoring system in Slovenia by using data mining techniques. Specifically we aim to generate raster maps with 25 m horizontal resolution of forest stand height and canopy cover, for the Kras region of Slovenia. We used predictive models based on multi-temporal Landsat data and calibrated it with high resolution airborne laser scanning (ALS) data. The visual inspection by a forestry expert of the resulting maps showed that the generated maps corresponded to the actual forest cover in the Kras region, both in terms of forest stand height as well as canopy cover.