Building a high-resolution site index map using boosted regression trees: The Norwegian case

Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production ≥ 1 m3·ha−1·year−1) in a wall-to-wall high-resolution (16 m × 16 m) SI map for Norway. We investigate whether remotely sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine-learning algorithm, to create high-accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning metrics, as predictor variables. We use the SI observed at more than 10 000 National Forest Inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT has root mean squared error (RMSE) ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSEs are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation.publishedVersio

Depth-to-water maps as predictors of rut severity in fully mechanized harvesting operations

The preservation of the functionality of forest soil is a key aspect in planning mechanized harvesting operations. Therefore, knowledge and information about stand and soil characteristics are vital to the planning process. In this respect, depth-to-water (DTW) maps were reviewed with regard to their potential use as a prediction tool for wheel ruts. To test the applicability of open source DTW maps for prediction of rutting, the ground surface conditions of 20 clear-cut sites were recorded post harvesting, using an unmanned aerial vehicle (UAV). In total, 80 km of machine tracks were categorized by the severity of occurring rut-formations to investigate whether: i) operators intuitively avoid areas with low DTW values, ii) a correlation exists between decreasing DTW values and increasing rut severity, and iii) DTW maps can serve as reliable decision-making tool in minimizing the environmental effects of big machinery deployment. While the machine operators did not have access to these predictions (DTW maps) during the operations, there was no visual evidence that driving through these areas was actively avoided, resulting in a higher density of severe rutting within areas with DTW values <1 m. A logistic regression analysis confirmed that the probability of severe rutting rapidly increases with decreasing DTW values. However, significant differences between sites exist which might be attributed to a series of other factors such as soil type, weather conditions, number of passes and load capacity. Monitoring these factors is hence highly recommended in any further follow-up studies on soil trafficability.publishedVersio

A stand-level growth and yield model for thinned and unthinned managed Norway spruce forests in Norway

A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios.publishedVersio

A stand-level growth and yield model for thinned and unthinned managed Norway spruce forests in Norway

A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios

Depth-to-water maps as predictors of rut severity in fully mechanized harvesting operations

The preservation of the functionality of forest soil is a key aspect in planning mechanized harvesting operations. Therefore, knowledge and information about stand and soil characteristics are vital to the planning process. In this respect, depth-to-water (DTW) maps were reviewed with regard to their potential use as a prediction tool for wheel ruts. To test the applicability of open source DTW maps for prediction of rutting, the ground surface conditions of 20 clear-cut sites were recorded post harvesting, using an unmanned aerial vehicle (UAV). In total, 80 km of machine tracks were categorized by the severity of occurring rut-formations to investigate whether: i) operators intuitively avoid areas with low DTW values, ii) a correlation exists between decreasing DTW values and increasing rut severity, and iii) DTW maps can serve as reliable decision-making tool in minimizing the environmental effects of big machinery deployment. While the machine operators did not have access to these predictions (DTW maps) during the operations, there was no visual evidence that driving through these areas was actively avoided, resulting in a higher density of severe rutting within areas with DTW values <1 m. A logistic regression analysis confirmed that the probability of severe rutting rapidly increases with decreasing DTW values. However, significant differences between sites exist which might be attributed to a series of other factors such as soil type, weather conditions, number of passes and load capacity. Monitoring these factors is hence highly recommended in any further follow-up studies on soil trafficability