Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims to explore these limits for various approaches: ordinary least squares regression (OLS), generalized additive models (GAM), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and Gaussian process regression (GPR). We modeled timber volume (m3·ha–1) for four boreal sites using ABA with 2–39 predictors and 20–500 training plots. OLS, GAM, LASSO, and SVM overfitted as the number of predictors approached the number of training plots. They required ≥15 plots per predictor to provide accurate predictions (RMSE ≤30%). GAM required ≥250 plots regardless of the number of predictors. The number of predictors only mildly affected RF and GPR, but they required ≥200 and ≥250 training plots, respectively. RF did not overfit in any circumstances, whereas GPR overfit even with 500 training plots. Overall, using up to 39 predictors did not generally result in overfit, and for most model types, it resulted in better accuracy for sufficiently large datasets (≥250 plots).
Lecture – Model-Assisted Estimation of Timber Volume by Means of Harvester and ALS Data
Janne Räty, Rasmus Astrup, Johannes Breidenbach
No abstract has been registered
Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM), and k nearest-neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.
Butt rot (BR) damage of a tree results from a decay caused by a pathogenic fungus. BR damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, maps of BR damages are typically lacking in forest information systems. Timber volume damaged by BR was predicted at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). This study utilized Random Forests models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). Our findings showed that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation resulted in an RMSE of 11.4 m3 · ha−1 (pseudo-R2: 0.66) whereas the mapping case resulted in a pseudo-R2 of 0.60. When spatially distinct clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo-R2 associated with the mapping case were 15.6 m3 · ha−1 and 0.37, respectively. The findings associated with the different cross-validation schemes indicated that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.
This study evaluated the suitability of different airborne laser scanning (ALS) datasets for the prediction of forest canopy fuel parameters in managed boreal forests in Finland. The ALS data alternatives were leaf-off and leaf-on unispectral and leaf-on multispectral data, alone and combined with aerial images. Canopy fuel weight, canopy base height, biomass of living and dead trees, and height and biomass of the understory tree layer were predicted using regression analysis. The considered categorical forest parameters were dominant tree species, site fertility and vertical forest structure layers. The canopy fuel weight was modeled based on crown biomass with an RMSE% value of 20–30%. The canopy base heights were predicted separately for pine and spruce stands with satisfactory results the RMSE% values being 9–10% and 15–17%, respectively. Following the initial classification of the existence of an understory layer (with kappa-values of 0.47–0.53), the prediction of understory height performed well (RMSE% 20–25%) but the understory biomass was predicted with larger RMSE% values (about 60–70%). Site fertility was classified with kappa-values of 0.5–0.6. The most accurate results were obtained using multispectral ALS data, although the differences between the datasets were minor.
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.