Uncertainty in timber assortment estimates predicted from forest inventory data

Abstract

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 .

Outlook for the Next Generation’s Precision Forestry in Finland

“…However, entirely different estimation and operation models are required for tasks that would extend our knowledge available from a forest, compared to the traditional, subjective forest mapping inventories. For example, in ABA, stem-quality attributes required by the forest industry, such as species-specific timber assortments, cannot be obtained accurately [5,[8][9][10]. Single tree-level information would be required to solve the above-mentioned limitations [11,12].…”

“…Forest attribute maps with spatial resolution of 10-20 m can be produced by current ABA inventory techniques [1][2][3][4][5][6]14,17]. In thinning stands, mapping and timing of forest management operations are highly important from a silvicultural point of view [24].…”

“…With ALS, the scale is at the stand-sub-stand levels. The total timber volume is obtained with high accuracy, while information about size-distribution, timber assortments or the number of trees has limited reliability (e.g., [5,6]). ALS data is suitable also for the estimation of other various forest characteristics [6,7].…”

Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning

“…Laser scanning-based applications have been widely studied (e.g., [5][6][7]) and are already in operational use [8][9][10][11][12]. Airborne laser scanning (ALS) has been used to estimate forest structural parameters from single trees (e.g., tree species, height (H), diameter-at-breast height (dbh) and volume (v)) to area levels (stand-level mean characteristics) [13][14][15][16][17]. Stand attribute information is conventionally estimated to a certain area level, based on large numbers of field sample plots, predictors derived from ALS data and statistical models linking field plots and ALS-derived predictors.…”

Uncertainty in Forest Net Present Value Estimations

“…Estimation of forest characteristics will be performed, using the nonparametric k-nearest neighbor (k-NN) or k-most similar neighbor (k-MSN) method [3]. With respect to the estimation of stand mean characteristics (e.g., [4][5][6][7]) and tree species-or timber assortment-specific characteristics [8][9][10][11], it has become possible to achieve at least the same level of accuracy using low-pulse ALS data as that found in traditional standwise forest inventory (SWFI). Overviews on the use of ALS in forest inventory can be found in [12][13][14][15].…”