Visualisering – ett verktyg för att illustrera långsiktiga konsekvenser av slutavverkning och naturhänsyn

Forestry in Sweden is currently conducted in a manner aiming at equating the production objectives and the biodiversity. To ensure that biodiversity is maintained, patches that are considered to be valuable now or in the future is saved in final fellings. How the tree retention areas will grow into the future forest stands is yet unknown. The purpose of this study was to visualize how different scenarios of natural consideration taken at final felling will evolve in a long term perspective, for two rotation periods. Projections of the forest conditions were executed with the decision support system Heureka and its applications PlanStart and PlanWise. The information about the projected forests was then used for the visualizations which were created in the software Visual Nature Studio. For each scenario a number of visualizations were presented in images and related information of standing volume. A dialogue was also carried out with scientists working with forestry planning to discuss the benefits of visualization. They believed visualization should a part of the decision support systems that are used in forestry planning. Visualization has a future in Swedish forestry, but before it’s possible to make visualization more available a method making the dataflow simpler is needed as this is the most time consuming part today

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 - 2.11 m, and the relative RMSE was less than 10 % (7.68 - 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden

Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume

In this study, data from the satellite sensors TanDEM-X and Sentinel-2 were combined with national field inventory data to predict forest above-ground biomass (AGB) and stem volume (VOL) over a large area in Sweden. The data sources were evaluated both separately and in combination. The study area covers approximately 20,000,000 ha and corresponds to about 70% of the Swedish forest area. The study area was divided into tiles of 2.5 x 2.5 km(2), which were processed sequentially. The field plots were inventoried on 7 m and 10 m circular plots by the Swedish National Forest Inventory, and plot AGB and VOL at the year of the satellite data were estimated based on a 10-year period of field data. The AGB and VOL were modelled using the k nearest neighbor (kNN) algorithm, with k = 5 neighbors. The combined use of two data sources with different scene extents enabled the generation of seamless AGB and VOL maps. Moreover, the kNN algorithm provided the VOL divided per tree species, which was used for classification of the dominant tree species at stand-level. The overall accuracy for the dominant tree species classification was 77%. The predicted AGB and VOL rasters were evaluated using 549 field inventoried forest stands distributed over Sweden. The RMSE for the predictions based on both data sources were 31.4 t/ha (29.1%) for AGB, and 59.0 m(3)/ha (30.2%) for VOL. By estimating and removing the variance due to sampling (the stand values were estimated from sample plots), the RMSE was improved to 18.0 t/ ha (16.6%). The evaluated approach of using kNN was suitable for estimating forest variables from a combination of different satellite sensors, provided sufficient field reference data are available. The TanDEM-X data were most important for the AGB and VOL predictions, while Sentinel-2 data were essential to map the tree species