Abstract:A harvester enables detailed roundwood data to be collected during harvesting operations by means of the measurement apparatus integrated into its felling head. These data can be used to improve the efficiency of wood procurement and also replace some of the field measurements, and thus provide both less costly and more detailed ground truth for remote sensing based forest inventories. However, the positional accuracy of harvester-collected tree data is not sufficient currently to match the accuracy per individual trees achieved with remote sensing data. The aim in the present study was to test the accuracy of various instruments utilizing global satellite navigation systems (GNSS) in motion under forest canopies of varying densities to enable us to get an understanding of the current state-of-the-art in GNSS-based positioning under forest canopies. Tests were conducted using several different combinations of GNSS and inertial measurement unit (IMU) mounted OPEN ACCESSForests 2015, 6 3219 on an all-terrain vehicle (ATV) "simulating" a moving harvester. The positions of 224 trees along the driving route were measured using a total-station and real-time kinematic GPS. These trees were used as reference items. The position of the ATV was obtained using GNSS and IMU with an accuracy of 0.7 m (root mean squared error (RMSE) for 2D positions). For the single-frequency GNSS receivers, the RMSE of real-time 2D GNSS positions was 4.2-9.3 m. Based on these results, it seems that the accuracy of novel single-frequency GNSS devices is not so dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to the visibility of the satellites. When postprocessing can be applied, especially when combined with IMU data, the improvement in the accuracy of the dual-frequency receiver was significant.
Uncertainty factors related to inventory methodologies and forest-planning simulation computings in the estimation of logging outturn assortment volumes and values were examined. The uncertainty factors investigated were (1) forest inventory errors, (2) errors in generated stem distribution, (3) effects of generated stem distribution errors on the simulation of thinnings and (iv) errors related to the prediction of stem form and simulation of bucking. Regarding inventory errors, standwise field inventory (SWFI) was compared with area-based airborne laser scanning (ALS) and aerial photography inventorying. Our research area, Evo, is located in southern Finland. In all, 31 logging sites (12 clear-cutting and 19 thinning sites) measured by logging machine in winter 2008 were used as field reference data. The results showed that the most significant source of error in the prediction of clear-cutting assortment outturns was inventory error. Errors related to stem-form prediction and simulated bucking as well as generation of stem distributions also cause uncertainty. The bias and root-mean-squared error (RMSE) of inventory errors varied between -11.4 and 21.6 m 3 /ha and 6.8 and 40.5 m 3 /ha, respectively, depending on the assortment and inventory methodology. The effect of forest inventory errors on the value of logging outturn in clear-cuttings was 29.1% (SWFI) and 24.7% (ALS). The respective RMSE values related to thinnings were 41.1 and 42%. The generation of stem distribution series using mean characteristics led to an RMSE of 1.3 to 2.7 m 3 /ha and a bias of -1.2 to 0.6 m 3 /ha in the volume of all assortments. Prediction of stem form and simulation of bucking led to a relative bias of -0.28 to 0.00 m 3 in predicted sawtimber volume. Errors related to pulpwood volumes were -0.4 m 3 to 0.21 m 3 .
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 total ) were detected. When the only error source was e td (tree detection varying from 75.9% to 100% of V total ), root mean square errors (RMSEs) in stand characteristics ranged between the scenarios from 32.4% to 0.6%, 29.0% to 0.5%, 7.8% to OPEN ACCESSRemote Sens. 2011, 3 1615 0.2% and 5.4% to 0.1% in stand basal area (BA), V total , mean height (Hg) and mean diameter (Dg), respectively. Saw wood volume RMSE varied from 25.1% to 0.2%, as pulp wood volume respective varied from 37.8% to 1.0% when errors stemmed only from e td . The effect of e d was most significant for V total and BA and the decrease in RMSE was from 12.0% to 0.6% (BA) and from 10.9% to 0.5% (V total ) in the most accurate tree detection scenario when e d varied from 20% to 0%. The effect of increased accuracy in tree height prediction was minor for all the stand characteristics. The results show that the most important error source in ITD is tree detection. At stand level, unbiased predictions for tree height and diameter are enough, given the present tree detection accuracy.
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.
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