Most of our knowledge about wood production of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) and associated silvicultural guidelines are based on field experiments. These have been established in rather small, homogenous stands. In practical forestry there is probably a comparatively larger gradient in within-stand variation due to varying site conditions and less controlled silviculture than in experiments. The extent of the within-stand variation in coniferous production stands and how thinning guides are used in relation to the within-stand variation, is not well understood. Also, the freely available Forest resource maps (sv. Skogliga grunddata) and satellite data offer the possibility to accounts for the within-stand variation in forest management, but this is also poorly researched.

This thesis evaluates within-stand variation at first thinning: its extent, its effect on silviculture and its importance for future stand development. Additionally, optical satellite data from Sentinel-2 is used to detect thinning operations, estimate growth after thinning and classify tree species. The thesis is mainly based on a survey carried out in the fall of 2018 in planted conifer-dominated production stands planned for first commercial thinning in which the thinning method of the forest workers was observed. The survey was inventoried directly after thinning and three growing seasons later.

The survey showed an unprecedented within-stand variation before thinning in stem volume, stem density, dominant height, mean height quadratic mean diameter and basal area. The thinning operations did not reduce the within-stand variation in any of the attributes measured with the relative standard deviation. The stands were thinned heavily, and the harvested basal area increased with basal area before thinning at sample plot level, which suggest an ambition to reduce the variation. 

The stands were also monitored using Sentinel-2 satellite data. The thinning detection model separated unthinned, lightly thinned and heavily thinned sample plots with a moderate overall accuracy of 62% (Kappa of 0.34). A set of satellite images over the whole observation period was used estimate the periodical annual volume increment after thinning and did so with a root mean squared error (RMSE) of 1.8 m³ ha^-1 y^-1 (relative RMSE: 24%).

The long-term effects of optimizing the thinning regime on pixel level versus conventional stand-level thinning was evaluated using the Heureka system. No benefits in terms of stand economy or production was found, but the within-stand variation in basal area decreased over the rotation.

Tree-species classification, rendering maps with the dominant tree species at pixel level over a forest holding, were made using multi-temporal Sentinel-2 satellite data and the Random Forest classifier. The major tree species in the forest holding were Scots pine, Norway spruce, Pedunculate oak (Quercus robur), Birch (Betula spp.) and Hybrid larch (Larix × marschlinsii). These species were classified with a high overall accuracy of 88.2% (Kappa of 0.82). 

This thesis illustrates that considerable within-stand variation could be expected before and after first thinning for coniferous dominated stands in southern Sweden. The average stand basal area after thinning was consistently lower than the required basal area in the thinning guides from the Swedish Forest Agency, which means that reduced total production over the rotation may be a result. The increasing harvested basal area with basal area before thinning, suggests an ambition to reduce the within-stand variation in basal area. Thinning at the pixel level by adapting the thinning regime to the within-stand variation did not have any long-term effects on stand economy or volume production compared to conventional stand-level thinning. Despite the non-significant results, high-resolution maps are probably needed anyway to support forest workers in thinning operations to avoid heavy thinning. The Sentinel-2 satellite data proved its relevance for practical forestry for thinning detection, assessing growth after thinning, and classifying tree species. These methods can be used in combination the already existing Forest resource maps to reduce uncertainties for the management of planted forest.