Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory, airborne laser scanning and harvester-derived data

2018 ◽  
Vol 33 (6) ◽  
pp. 603-612 ◽  
Author(s):  
Carolin Fischer ◽  
Olav A. Høibø ◽  
Geir I. Vestøl ◽  
Marius Hauglin ◽  
Endre H. Hansen ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Modulus Of Elasticity ◽  
Forest Inventory ◽  
Dynamic Modulus ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Structural Timber ◽  
Airborne Laser ◽  
Dynamic Modulus Of Elasticity ◽  
Derived Data

ANALYSIS OF SOUND SPEED IN LONGITUDINAL DIRECTION AT REFERENCE MOISTURE CONTENT (w = 12 %) IN SAWMILL ASSORTMENTS (FAGUS SYLVATICA, L.)

Akustika ◽  
2020 ◽  
pp. 45-50
Author(s):  
Alena Rohanová
Keyword(s):  
Moisture Content ◽  
Modulus Of Elasticity ◽  
Dynamic Modulus ◽  
Sound Speed ◽  
Longitudinal Direction ◽  
Structural Timber ◽  
Sound Speeds ◽  
Dynamic Modulus Of Elasticity ◽  
Round Timber

This paper explores the analysis of sound speeds in the longitudinal direction and their reduction to the reference moisture content w = 12 %. The sound speed cw was determined with Sylvatest Duo device. Moisture content of beech sawmill assortments (round timber: N = 16, logs: N = 2 × 16, structural boards: N = 54) in the range of 12 – 72 % was measured. For the analysis purposes, the sound speed was converted to reference conditions (c12, uref = 12%). A second-degree polynomial (parabola) with a regression equation of the form: c// = 5649 - 27,371 × w + 0.0735 × w2 was used to convert cw to c12, and correction of measured and calculated values was used as well. The sound speeds c12 in sawmill assortments (c12,round, c12,log, c12,board) were evaluated by linear dependences. Dependence was not confirmed for c12,round and c12,board1 (r = 0.168), in contrast for c12,round and c12,log2 the dependence is statistically very significant (r = 0.634). The results of testing showed that the most suitable procedure for predicting quality of structural timber is the first step round timber – log2, the second step: log2 - board2. More exact results of the construction boards were obtained from log2 than from log1. The sound speed is used in the calculation of dynamic modulus of elasticity (Edyn). EN 408 mentions the possibility of using dynamic modulus of elasticity as an alternative method in predicting the quality of structural timber.

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.

The shelf-life of airborne laser scanning data for enhancing forest inventory inferences

2018 ◽  
Vol 206 ◽  
pp. 254-259 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Qi Chen ◽  
Dale D. Gormanson ◽  
Brian F. Walters
Keyword(s):  
Shelf Life ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser

How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data?

2018 ◽  
Vol 72 ◽  
pp. 91-98 ◽  
Author(s):  
Mikael Kukkonen ◽  
Lauri Korhonen ◽  
Matti Maltamo ◽  
Aki Suvanto ◽  
Petteri Packalen
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Optical Data ◽  
Airborne Laser

Weather-sensitive height growth modelling of Norway spruce using repeated airborne laser scanning data

2021 ◽  
Vol 308-309 ◽  
pp. 108568
Author(s):  
Luiza Tymińska- Czabańska ◽  
Jarosław Socha ◽  
Paweł Hawryło ◽  
Radomir Bałazy ◽  
Mariusz Ciesielski ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Laser Scanning ◽  
Height Growth ◽  
Airborne Laser Scanning ◽  
Growth Modelling ◽  
Airborne Laser

scholarly journals Evaluating the accuracy of ALS-based removal estimates against actual logging data

2020 ◽  
Vol 77 (3) ◽  
Author(s):  
Ville Vähä-Konka ◽  
Matti Maltamo ◽  
Timo Pukkala ◽  
Kalle Kärhä
Keyword(s):  
Model Prediction ◽  
Forest Inventory ◽  
Boreal Forests ◽  
Laser Scanning ◽  
Growth Models ◽  
Airborne Laser Scanning ◽  
Inventory Data ◽  
Attribute Data ◽  
Forest Inventory Data ◽  
Airborne Laser

Abstract Key message We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester. Context Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data. Aims This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data. Methods We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient. Results In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals. Conclusion The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data

2015 ◽  
Vol 164 ◽  
pp. 36-42 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Erik Næsset ◽  
Terje Gobakken ◽  
Ole Martin Bollandsås
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Forest Biomass ◽  
Airborne Laser Scanning ◽  
Direct Estimation ◽  
Airborne Laser

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

2010 ◽  
Vol 114 (4) ◽  
pp. 911-924 ◽  
Author(s):  
Johannes Breidenbach ◽  
Erik Næsset ◽  
Vegard Lien ◽  
Terje Gobakken ◽  
Svein Solberg
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Crown ◽  
Individual Tree ◽  
Multispectral Data ◽  
Airborne Laser ◽  
Species Specific

scholarly journals The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

Forests ◽  
2013 ◽  
Vol 4 (3) ◽  
pp. 518-536 ◽  
Author(s):  
Joanne White ◽  
Michael Wulder ◽  
Mikko Vastaranta ◽  
Nicholas Coops ◽  
Doug Pitt ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Laser

scholarly journals Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Forests ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 72 ◽  
Author(s):  
Tuomo Kauranne ◽  
Sergey Pyankov ◽  
Virpi Junttila ◽  
Alexander Kedrov ◽  
Andrey Tarasov ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Experimental Results ◽  
Airborne Laser ◽  
Perm Region
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