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
Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exp...Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.展开更多
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supported in part by the Strategic Research Council at the Academy of Finland project“Competence Based Growth Through Integrated Disruptive Technologies of 3D Digitalization,Robotics,Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem(293389,314312),Academy of Finland projects“Estimating Forest Resources and Quality-related Attributes Using Automated Methods and Technologies”(334830,334829)”,“Monitoring and understanding forest ecosystem cycles”(334060)。
文摘Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.
