Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM

Enabling automated 3D mapping in forests is an important component of the future development of forest technology, and has been garnering interest in the scientific community, as can be seen from the many recent publications. Accordingly, the authors of the present paper propose the use of a Simultaneous Localisation and Mapping algorithm, called graph-SLAM, to generate local maps of forests. In their study, the 3D data required for the mapping process were collected using a custom-made, mobile platform equipped with a number of sensors, including Velodyne VLP-16 LiDAR, a stereo camera, an IMU, and a GPS. The 3D map was generated solely from laser scans, first by relying on laser odometry and then by improving it with robust graph optimisation after loop closures, which is the core of the graph-SLAM algorithm. The resulting map, in the form of a 3D point cloud, was then evaluated in terms of its accuracy and precision. Specifically, the accuracy of the fitted diameter at breast height (DBH) and the relative distance between the trees were evaluated. The results show that the DBH estimates using the Pratt circle fit method could enable a mean estimation error of approximately 2 cm (7–12%) and an RMSE of 2.38 cm (9%), whereas for tree positioning accuracy, the mean error was 0.0476 m. The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping. Moreover, such maps open up the possibility for precision localisation for forestry vehicles.publishedVersio

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

Climate change mitigation potential of biochar from forestry residues under boreal condition

Forest harvest residue is a low-competitive biomass feedstock that is usually left to decay on site after forestry operations. Its removal and pyrolytic conversion to biochar is seen as an opportunity to reduce terrestrial CO2 emissions and mitigate climate change. The mitigation effect of biochar is, however, ultimately dependent on the availability of the biomass feedstock, thus CO2 removal of biochar needs to be assessed in relation to the capacity to supply biochar systems with biomass feedstocks over prolonged time scales, relevant for climate mitigation. In the present study we used an assembly of empirical models to forecast the effects of harvest residue removal on soil C storage and the technical capacity of biochar to mitigate national-scale emissions over the century, using Norway as a case study for boreal conditions. We estimate the mitigation potential to vary between 0.41 and 0.78 Tg CO2 equivalents yr−1, of which 79% could be attributed to increased soil C stock, and 21% to the coproduction of bioenergy. These values correspond to 9–17% of the emissions of the Norwegian agricultural sector and to 0.8–1.5% of the total national emission. This illustrates that deployment of biochar from forest harvest residues in countries with a large forestry sector, relative to economy and population size, is likely to have a relatively small contribution to national emission reduction targets but may have a large effect on agricultural emission and commitments. Strategies for biochar deployment need to consider that biochar's mitigation effect is limited by the feedstock supply which needs to be critically assessed.acceptedVersio

An analysis of stand-level size distributions of decay-affected Norway spruce trees based on harvester data

We studied size distributions of decay-affected Norway spruce trees using cut-to-length harvester data. The harvester data comprised tree-level decay and decay severity recordings from 101 final felling stands, which enabled to analyze relationships between size distributions of all and decay-affected trees. Distribution matching technique was used to transfer the size distribution of all trees into the diameter at breast height (DBH) distribution of decay-affected trees.publishedVersio

A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway

Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.publishedVersio

Stand-level growth models for long-term projections of the main species groups in Norway

Stand-level growth and yield models are important tools that support forest managers and policymakers. We used recent data from the Norwegian National Forest Inventory to develop stand-level models, with components for dominant height, survival (number of survived trees), ingrowth (number of recruited trees), basal area, and total volume, that can predict long-term stand dynamics (i.e. 150 years) for the main species in Norway, namely Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and birch (Betula pubescens Ehrh. and Betula pendula Roth). The data used represent the structurally heterogeneous forests found throughout Norway with a wide range of ages, tree size mixtures, and management intensities. This represents an important alternative to the use of dedicated and closely monitored long-term experiments established in single species even-aged forests for the purpose of building these stand-level models. Model examination by means of various fit statistics indicated that the models were unbiased, performed well within the data range and extrapolated to biologically plausible patterns. The proposed models have great potential to form the foundation for more sophisticated models, in which the influence of other factors such as natural disturbances, stand structure including species mixtures, and management practices can be included.publishedVersio

Radiative forcing bias of simulated surface albedo modifications linked to forest cover changes at northern latitudes

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Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.publishedVersio

Skogtilstanden i verneområder og vurderinger av mulighetene for intensivert overvåking gjennom Landsskogtakseringen

Med grunnlag i Landsskogtakseringens prøveflater beskriver denne rapport skogtilstanden på vernet areal samt vurder mulighetene for økt overvåking av vernet areal gjennom Landsskogtakseringen. I henhold til Landsskogtakseringens utvalgskartlegging er 2,3% av den produktive skogen vernet, mens andelen er 5,5% for den uproduktive skogen. Dette betyr at 3,1% (343 000 ha) av det totale skogarealet er vernet. Størsteparten av den vernete produktive skogen er i naturreservater (134 000 ha), mens nasjonalparkene utgjør en relativt liten del av det vernete produktive skogarealet (55 000 ha). Den mest vanlige skogtypen i vernområdene er bjørkedominert skog, det vil si arealer hvor over 70% av det stående volum er bjørk (120 000 ha). Et nesten like stort areal er furudominert (106 000 ha), mens grandominert skog utgjør et noe mindre areal (60 000 ha). Fordelingen av den vernete skogen på produktivitetsklasser viser at høyproduktiv og middels produktiv skog er underrepresentert, mens lavproduktiv og uproduktiv skog er overrepresentert. […]publishedVersio