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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 | Estimating single-tree branch biomass of Norway spruce by airborne laser scanning显示文摘 | Marius Hauglin Janka Dibdiakova Terje Gobakken | 2013 | ISPRS Journal of Photogrammetry and Remote Sensing2013，79，： | 1 |
| 3 | Cyclic AMP induces apoptosis in multiple myeloma cells and inhibits tumor development in a mouse myeloma model显示文摘 | Follin-Arbelet V Hofgaard PO Hauglin H | 2011 | BMC Cancer2011，11，： | 1 |
| 4 | 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 |
| 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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