A century of National Forest Inventory in Norway–informing past,present,and future decisions

Past:In the early twentieth century,forestry was one of the most important sectors in Norway and an agitateddiscussion about the perceived decline of forest resources due to over-exploitation was ongoing.To base thediscussion on facts,the young state of Norway established Landsskogtakseringen–the world’s first National ForestInventory(NFI).Field work started in 1919 and was carried out by county.Trees were recorded on 10m wide stripswith 1–5 km interspaces.Site quality and land cover categories were recorded along each strip.Results for the firstcounty were published in 1920,and by 1930 most forests below the coniferous tree line were inventoried.The 2ndto 5th inventories followed in the years 1937–1986.As of 1954,temporary sample plot clusters on a 3 km×3 kmgrid were used as sampling units.Present:The current NFI grid was implemented in the 6th NFI from 1986 to 1993,when permanent plots ona 3 km×3 km grid were established below the coniferous tree line.As of the 7th inventory in 1994,the NFIis continuous,and 1/5 of the plots are measured annually.All trees with a diameter≥5 cm are recorded oncircular,250 m2 plots.The NFI grid was expanded in 2005 to cover alpine regions with 3 km×9 km and 9km×9 km grids.In 2012,the NFI grid within forest reserves was doubled along the cardinal directions.Clustered temporary plots are used periodically to facilitate county-level estimates.As of today,more than 120variables are recorded in the NFI including bilberry cover,drainage status,deadwood,and forest health.Landusechanges are monitored and trees outside forests are recorded.Future:Considerable research efforts towards the integration of remote sensing technologies enable thepublication of the Norwegian Forest Resource Map since 2015,which is also used for small area estimation atthe municipality level.On the analysis side,capacity and software for long term growth and yield prognosisare being developed.Furthermore,we foresee the inclusion of further variables for monitoring ecosystemservices,and an increasing demand for mapped information.The relatively simple NFI design has proven tobe a robust choice for satisfying steadily increasing information needs and concurrently providing consistenttime series.

Examining approaches for modeling individual tree growth response to thinning in Norway spruce

Using periodic measurements from permanent plots in non-thinned and thinned Norway spruce(Picea abies(L.)H.Karst.)stands in Norway,individual-tree growth models were developed to predict annual diameter increment,height increment,and height to crown base increment.Based on long-term data across a range of thinning regimes and stand conditions,alternative approaches for modeling response to treatment were assessed.Dynamic thinning response functions in the form of multiplicative modifiers that predict no effect at the time of thinning,a rapid increase followed by an early maximum before the effect gradually declines to zero could not be fitted to initially derived baseline models without thinning related predictors.However,alternative approaches were used and found to perform well.Specifically,indicator variables representing varying time periods after thinning were statistically significant and behaved in a robust manner as well as consistent with general expectations.In addition,they improved overall prediction accuracy when incorporated as fixed effects into the baseline models for diameter and height to crown base increment.Further,more simply,including exponentially decreasing multiplicative thinning response functions improved prediction accuracy for height increment and height to crown base increment.Irrespective of studied attribute and modelling approach,improvement in performance of these extended models was relatively limited when compared to the corresponding baseline models and more pronounced in trees from thinned stands.We conclude that the largely varying and often multi-year measurement intervals of the periodic data used in this study likely prevented the development of more sophisticated thinning response functions.However,based on the evaluation of the final models’overall performance such complex response functions may not to be necessary to reliably predict individual tree growth after thinning for certain conditions or species,which should be further considered in future analyses of similar nature.