Acta Univ. Agric. Silvic. Mendelianae Brun. 2015, 63, 793-801
Published online 2015-06-28

Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment

Tomáš Mikita, Petr Balogh

Department of Geoinformation Technologies, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the first step, the growing stock on the planned clearcut determined by its location and area in feature class is calculated (by the method of individual tree detection). Subsequently tree heights from canopy height model (CHM) are extracted and then diameters at breast height (DBH) and wood volume using the regressions are calculated. Information about wood volume of each tree in the clearcut is exported and summarized in a table. In the next step, all trees in the clearcut are removed and a new DSM without trees in the clearcut is generated. This canopy model subsequently serves as an input for evaluation of wind risk damage by the MAXTOPEX tool (Mikita et al., 2012). In the final raster, predisposition of uncovered forest stand edges (around the clearcut) to wind risk is calculated based on this analysis. The entire tool works in the background of ArcGIS server as a spatial decision support system for foresters.


This article was prepared as a part of the research project of Faculty of Forestry and Wood Technology of Mendel University in Brno IGA 40/2013 “Geobiocoenological map of agricultural landscape – usage of multivariate statistical methods”.


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