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Skogliga grunddata är digitala raster-kartor som beskriver tillståndet i skog och mark. De skogliga variablerna är i huvudsak framtagna genom en sambearbetning av laserdata från Lantmäteriets nationella laserskanning och provytor från Riksskogstaxeringen. De digitala kartorna som beskriver skogsmarken är till stor hjälp för privata skogsägare, skogstjänstemän, myndigheter, m fl. Data är dels tillgängligt för gratis nedladdning, men det finns även möjligheter att titta på kartorna med hjälp av interaktiva webverktyg. Lantmäteriets laserskanning påbörjades 2009 och fortgår än idag. Endast delar av fjällkedjan återstår. Grundtanken med laserskanningen var att få en bra höjdmodell över landet. En bra höjdmodell ger stor nytta vid all samhällsplanering, men är även till hjälp vid klimatanpassning. Riksskogstaxeringen samlar årligen in data om Sveriges skogstillstånd med permanenta och tillfälliga provytor. Dessa provytor är koordinatsatta och är väl spridda över hela landet. I detta projekt har provytor från Riksskogstaxeringen kombinerats med laserdata för att ta fram rasterkartor med uppgifter om virkesvolym, grundyta, medelhöjd, medeldiameter och trädbiomassa.

3D information extracted by image matching of aerial images, so called image-based point clouds, have been found to provide accurate vegetation height measurements. This has led to an increased interest from the vegetation mapping community, since aerial images are an affordable alternative to airborne laser scanner (ALS) data. In Sweden, this is especially interesting due to the National Mapping Agency’s decision to derive 3D information from annually acquired aerial imagery, starting in 2016. Previous studies have shown that image-based point cloud data derived from standard stereo aerial images is of potential use for forest inventory and change detection. In this thesis, the focus is on exploring the utility of image-based point clouds, and surface models, for vegetation mapping; more specifically, it explores segmentation of vegetation patches based on height above ground, estimation of tree height, and estimation of vertical canopy cover. The studies were conducted in a study area located in the hemi-boreal zone of southern Sweden. Segmentation based on canopy height models (CHMs) derived by image matching combined with a digital elevation model (DEM) from ALS data was found to deliver polygons within which tree height varied with a few meters. Tree height was estimated using height percentiles derived from the CHM and the results were similar to previous studies using image-based point clouds. Estimation of vertical canopy cover resulted in low accuracy due to underestimation when the canopy cover was sparse, and overestimation when the canopy cover was dense, while behaving linearly at approximately 15 – 85 % canopy cover. Dominant tree species influenced the results of estimation of tree height, as well as vertical canopy cover. Vegetation mapping using image-based point cloud data holds great potential and further research is needed to gain knowledge of appropriate methods and limitations.

As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011.

Nitrogen (N) fertilisation increases tree production in boreal forests, but it is poorly known how long the fertiliser effect will remain after the end of the fertilisation. We studied a Scots pine stand during 17 years of annual fertilisation with N and other nutrients followed by a period of 19 years of no fertilisation. Fertilisation increased needle N concentrations, but once fertilisation was stopped the concentrations dropped to the level of the unfertilised trees within five years. Leaf area index was 10% higher in fertilised than in unfertilised plots 19 years after the end of fertilisation. Basal area and stem volume in fertilised plots were twice those in unfertilised plots at the end of the fertilisation period. The growth in the fertilised plots remained higher, even when the effects of tree size were accounted for. Fourteen years after the end of fertilisation, fertilised plots had higher soil carbon (C) pools, but similar annual C mineralisation as unfertilised plots. Fertilised plots had higher soil N pools and higher field net N mineralisation. The fertilisation resulted in a long-term increase in the forest production and probably caused a shift in N uptake from organic towards inorganic forms.