scholarly journals Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot data

2021 ◽  
Vol 502 ◽  
pp. 119737
Author(s):  
Inka Bohlin ◽  
Matti Maltamo ◽  
Henrik Hedenås ◽  
Tomas Lämås ◽  
Jonas Dahlgren ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Field Plot ◽  
Auxiliary Data ◽  
Airborne Laser

scholarly journals Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Johannes Schumacher ◽  
Marius Hauglin ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Forest Inventory ◽  
Regression Models ◽  
Laser Scanning ◽  
Site Index ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Practical Applications ◽  
Airborne Laser ◽  
Sentinel 2

Abstract 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.

scholarly journals Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data

2021 ◽  
Author(s):  
Janne Räty ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Systematic Sampling ◽  
National Forest ◽  
Prediction Errors ◽  
K Nearest Neighbor ◽  
Airborne Laser

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed- effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.

Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning

2012 ◽  
Vol 123 ◽  
pp. 443-456 ◽  
Author(s):  
Terje Gobakken ◽  
Erik Næsset ◽  
Ross Nelson ◽  
Ole Martin Bollandsås ◽  
Timothy G. Gregoire ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Airborne Laser ◽  
Field Plots

scholarly journals Timber volume estimation based on airborne laser scanning — comparing the use of national forest inventory and forest management inventory data

2021 ◽  
Vol 78 (2) ◽  
Author(s):  
Johannes Rahlf ◽  
Marius Hauglin ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Forest Management ◽  
Forest Inventory ◽  
Laser Scanning ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Volume Estimation ◽  
National Forest ◽  
Forest Resource ◽  
Timber Volume ◽  
Airborne Laser

Abstract Key message Large-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests. Context Traditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar. Aims We aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map. Methods Accuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level. Results Estimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map. Conclusion The comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.

scholarly journals Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Marius Hauglin ◽  
Johannes Rahlf ◽  
Johannes Schumacher ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
National Forest Inventory ◽  
Mixed Effects ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Airborne Laser ◽  
Maturity Class ◽  
Species Specific

Abstract 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.

A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory

2017 ◽  
Vol 194 ◽  
pp. 447-454 ◽  
Author(s):  
Mats Nilsson ◽  
Karin Nordkvist ◽  
Jonas Jonzén ◽  
Nils Lindgren ◽  
Peder Axensten ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Field Data ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Airborne Laser

scholarly journals The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning

2020 ◽  
Vol 12 (3) ◽  
pp. 413 ◽  
Author(s):  
Adrián Pascual ◽  
Juan Guerra-Hernández ◽  
Diogo N. Cosenza ◽  
Vicente Sandoval
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Mean Squared Error ◽  
Model Performance ◽  
Structural Complexity ◽  
Basal Area ◽  
Tree Height ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Airborne Laser

The level of spatial co-registration between airborne laser scanning (ALS) and ground data can determine the goodness of the statistical inference used in forest inventories. The importance of positioning methods in the field can increase, depending on the structural complexity of forests. An area-based approach was followed to conduct forest inventory over seven National Forest Inventory (NFI) forest strata in Spain. The benefit of improving the co-registration goodness was assessed through model transferability using low- and high-accuracy positioning methods. Through the inoptimality losses approach, we evaluated the value of good co-registered data, while assessing the influence of forest structural complexity. When using good co-registered data in the 4th NFI, the mean tree height (HTmean), stand basal area (G) and growing stock volume (V) models were 2.6%, 10.6% and 14.7% (in terms of root mean squared error, RMSE %), lower than when using the coordinates from the 3rd NFI. Transferring models built under poor co-registration conditions using more precise data improved the models, on average, 0.3%, 6.0% and 8.8%, while the worsening effect of using low-accuracy data with models built in optimal conditions reached 4.0%, 16.1% and 16.2%. The value of enhanced data co-registration varied between forests. The usability of current NFI data under modern forest inventory approaches can be restricted when combining with ALS data. As this research showed, investing in improving co-registration goodness over a set of samples in NFI projects enhanced model performance, depending on the type of forest and on the assessed forest attributes.

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