Timo Melkas

Effects of individual tree detection error sources on forest management planning calculations

Abstract: The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees ( e td ), error in tree height prediction ( e h ) and error in tree diameter prediction ( e d ). The effects of errors were analyzed with Monte Carlo simulations. e td was modeled empirically based on a tree’s relative size. A total of five different tree detection scenarios were tested. Effect of e h was investigated using 5% and 0% and effect of e d using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for e td . In Scenario 1, 60.2% from stem number and 75.9% from total volume (V

Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data

Changes to stems caused by natural forces and timber harvesting constitute an essential input for many forestry-related applications and ecological studies, especially forestry inventories based on the use of permanent sample plots. Conventional field measurement is widely acknowledged as being time-consuming and labor-intensive. More automated and efficient alternatives or supportive methods are needed. Terrestrial laser scanning (TLS) has been demonstrated to be a promising method in forestry field inventories. Nevertheless, the applicability of TLS in recording changes in the structure of forest plots has not been studied in detail. This paper presents a fully automated method for detecting changes in forest structure over time using bi-temporal TLS data. The developed method was tested on five densely populated forest plots including 137 trees and 50 harvested trees in point clouds. The present study demonstrated that 90 percent of tree stem changes could be automatically located from single-scan TLS data. These changes accounted for 92 percent of the changed basal area. The results indicate that the processing of TLS data collected at different times to detect tree stem changes can be fully automated.

A method for estimating tree composition and volume using harvester data

This research article introduces a method that can be used to estimate tree composition and volume of arbitrary subdivisions of a logged stand. The method uses spatial data that is generated with a harvester to simulate individual tree locations. The simulation uses two probability density functions: the distance and the angle from the harvester at which the tree is cut. The average estimated volume root mean squared error varied from 4% for 0.4 ha subregions to 29% for 0.03 ha subregions. The stand subdivision method affected the accuracy of volume estimation only in the smallest subregions. Compared with the use of harvester data as such, i.e. without tree location simulation, the improvement in total and species-wise volume estimates varied between 5 and 35%. The data produced by the method can be used as a field data source for remote sensing methods as well as a verification data set for field inventories. However, a question remains over the generality of the model parameters used.

Airborne LiDAR-derived elevation data in terrain trafficability mapping

ABSTRACT Heavy off-road traffic causes soil compaction and rutting, which can significantly reduce the yield of forest stands. Reliable information on terrain trafficability, that is, the ability of terrain to support the passage of vehicles, would enable significant enhancement of wood procurement planning and reduction of soil damage. The objective here was to determine the feasibility of airborne scanning light detection and ranging (LiDAR)-derived digital terrain models (DTM) in terrain trafficability mapping. Soil damage was inventoried from a total of 13 km of forwarding trails, and a logistic regression model was fitted for predicting the event of soil damage. DTM-derived soil wetness indices performed well as predictor variables, and DTM-derived local binary patterns also proved useful in terrain trafficability mapping. A prediction accuracy of 83.6% (Cohen’s kappa of 0.38) was observed for soil damage probability modelling, using only DTM-derived predictors, and a corresponding accuracy of 85.0% (kappa of 0.45) was achieved when an existing soil map was used as well. In addition to the topography-related features, soil stoniness proved to be a critical factor affecting soil resistance to rutting. Our results indicate that the utilisation of LiDAR-derived elevation data for terrain trafficability mapping is a feasible method in sustainable forest management.