Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mec...Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.展开更多
Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree poi...Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.展开更多
Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories es...Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.展开更多
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.Ho...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.展开更多
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 thedi...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.展开更多
Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pe...Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa 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 imputatio 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 ag 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 （scare） were fit tc 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. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial 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, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.展开更多
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 t...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.展开更多
基金funded by the Norwegian Research Council(NFR project 302701 Climate Smart Forestry Norway).
文摘Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.
基金the Center for Research-based Innovation SmartForest:Bringing Industry 4.0 to the Norwegian forest sector (NFR SFI project no.309671,smartforest.no)。
文摘Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.
文摘Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.
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.
基金the Norwegian Institute of Bioeconomy Research(NIBIO).
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.
基金supported by the Norwegian Institute of Bioeconomy Research(NIBIO)
文摘Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa 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 imputatio 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 ag 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 （scare） were fit tc 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. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial 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, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.
基金supported by the Norwegian Institute of Bioeconomy Research(NIBIO).
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.
