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| 1 | Mapping forest age using National Forest Inventory,airborne laser scanning,and Sentinel-2 data显示文摘Background:The age of forest stands is critical information for forest management and conservation,for example for growth modelling,timing of management activities and harvesting,or decisions about protection areas.However,area-wide information about forest stand age often does not exist.In this study,we developed regression models for large-scale area-wide prediction of age in Norwegian forests.For model development we used more than 4800 plots of the Norwegian National Forest Inventory(NFI)distributed over Norway between latitudes 58°and 65°N in an 18.2 Mha study area.Predictor variables were based on airborne laser scanning(ALS),Sentinel-2,and existing public map data.We performed model validation on an independent data set consisting of 63 spruce stands with known age.Results:The best modelling strategy was to fit independent linear regression models to each observed site index(SI)level and using a SI prediction map in the application of the models.The most important predictor variable was an upper percentile of the ALS heights,and root mean squared errors(RMSEs)ranged between 3 and 31 years(6%to 26%)for SI-specific models,and 21 years(25%)on average.Mean deviance(MD)ranged between^(−1) and 3 years.The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years.Using a mapped SI,which is required for practical applications,RMSE and MD on plot level ranged from 19 to 56 years(29%to 53%),and 5 to 37 years(5%to 31%),respectively.For the validation stands,the RMSE and MD were 12(22%)and 2 years(3%),respectively.Conclusions:Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age.Overall,we obtained good results,especially for stands with high SI.The models could be considered for practical applications,although we see considerable potential for improvements if better SI maps were available. | Johannes Schumacher Marius Hauglin Rasmus Astrup Johannes Breidenbach | 2020 | Forest Ecosystems2020，7，4： | 3 |
| 2 | Longitudinal height-diameter curves for Norway spruce, Scots pine and silver birch in Norway based on shape constraint additive regression models显示文摘Background: Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce(Picea abies(L.) Karst.), Scots pine(Pinus sylvestris L.) and silver birch(Betula pendula Roth) in Norway are presented. The Norwegian National Forest Inventory(NFI) is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputation in forest inventories as well as future tree height predictions in growth and yield scenario simulations.Methods: Generalized additive mixed models(gamm) are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand age,as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests.Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models(scam) were fit to incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity.Results: Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. A two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatially correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive.Conclusions: In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scam may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge. | Matthias Schmidt Johannes Breidenbach Rasmus Astrup | 2018 | Forest Ecosystems2018，5，2： | 1 |
| 3 | Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features显示文摘 | Marius Hauglin Rasmus Astrup Terje Gobakken Erik N?sset | 2013 | Scandinavian Journal of Forest Research2013，，5： | 1 |
| 4 | A century of National Forest Inventory in Norway–informing past,present,and future decisions显示文摘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. | Johannes Breidenbach Aksel Granhus Gro Hylen Rune Eriksen Rasmus Astrup | 2020 | Forest Ecosystems2020，7，4： | 0 |
| 5 | Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data显示文摘Background:The Norwegian forest resource map(SR16)maps forest attributes by combining national forest inventory(NFI),airborne laser scanning(ALS)and other remotely sensed data.While the ALS data were acquired over a time interval of 10 years using various sensors and settings,the NFI data are continuously collected.Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data,and assess the accuracy overall and at the ALS project level.Materials and methods:The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects,covering 17 Mha and 2/3 of the productive forest in Norway.Mixed-effects regression models were used to account for differences among ALS projects.Two types of stratification were used to fit models:1)stratification by the three main tree species groups spruce,pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions,and 2)stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly.Stratified models were compared to general models that were fit without stratifying the data.Results:The species-specific models had relative root-mean-squared errors(RMSEs)of 35%,34%,31%,and 12% for volume,aboveground biomass,basal area,and Lorey’s height,respectively.These RMSEs were 2-7 percentage points(pp)smaller than those of general models.When validating using predicted species,RMSEs were 0-4 pp.smaller than those of general models.Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp.Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0-3.9 pp.,depending on the main tree species.RMSEs for timber volume ranged between 19%-59% among individual ALS projects.Conclusions:The stratification by tree species considerably improved models of forest structural variables.A further stratification by maturity class improved these models only moderately.The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories,but local variations are apparent. | Marius Hauglin Johannes Rahlf Johannes Schumacher Rasmus Astrup Johannes Breidenbach | 2021 | Forest Ecosystems2021，8，4： | 0 |
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