scholarly journals Predicting factual sawlog volumes in Scots pine dominated forests using airborne laser scanning data

Silva Fennica ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Tomi Karjalainen ◽  
Petteri Packalen ◽  
Janne Räty ◽  
Matti Maltamo
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Mean Squared Error ◽  
Model Fitting ◽  
Mixed Effects ◽  
Airborne Laser Scanning ◽  
Linear Mixed Effects ◽  
Airborne Laser ◽  
Type Information ◽  
Relative Root

The aim in the study was to compare alternatives for the prediction of factual sawlog volumes using airborne laser scanning (ALS) data in Scots pine ( L.) dominated forests in eastern Finland. Accurate estimates of factual sawlog volume are desirable to ease the planning of harvesting operations. The factual sawlog volume of pines was derived from visual bucking, i.e. a procedure where the defects were located on each stem during sample plot measurements. For other species, the theoretical sawlog volume was considered also as the factual sawlog volume due to data restrictions. We predicted factual sawlog volume with eight alternatives that were based on either linear mixed-effects models or k-nearest neighbour imputations. An existing sawlog reduction model, commonly used in Finland, was also tested individually and combined with a number of the alternatives, and site type information was also utilised. Model fitting and prediction was implemented at the 15 × 15 m level, but accuracy was assessed at the 30 × 30 m level. The relative root mean squared error (RMSE%) values for the factual sawlog volume predictions varied between 20.9% and 33.5%, and the best accuracy was obtained with a linear mixed-effects model. These results indicate that factual sawlog volumes in Scots pine dominated forests can be predicted with reasonable accuracy with ALS data.Pinus sylvestris

Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning

2006 ◽  
Vol 102 (3-4) ◽  
pp. 364-376 ◽  
Author(s):  
Svein Solberg ◽  
Erik Næsset ◽  
Kjersti Holt Hanssen ◽  
Erik Christiansen
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Insect Attack

scholarly journals Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Forests ◽  
2013 ◽  
Vol 4 (2) ◽  
pp. 386-403 ◽  
Author(s):  
Tuula Kantola ◽  
Mikko Vastaranta ◽  
Päivi Lyytikäinen-Saarenmaa ◽  
Markus Holopainen ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Pine Trees ◽  
Needle Loss

scholarly journals Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data

Silva Fennica ◽  
2009 ◽  
Vol 43 (3) ◽  
Author(s):  
Matti Maltamo ◽  
Jussi Peuhkurinen ◽  
Jukka Malinen ◽  
Jari Vauhkonen ◽  
Petteri Packalén ◽  
...  
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Quality Characteristics ◽  
Airborne Laser Scanning ◽  
Airborne Laser

scholarly journals Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines

Silva Fennica ◽  
2019 ◽  
Vol 53 (3) ◽  
Author(s):  
Lauri Korhonen ◽  
Jaakko Repola ◽  
Tomi Karjalainen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Mixed Models ◽  
Laser Scanning ◽  
Systematic Errors ◽  
Mixed Effects ◽  
Airborne Laser Scanning ◽  
Mixed Effects Models ◽  
Individual Tree ◽  
Practical Applications ◽  
Airborne Laser ◽  
Crown Base Height

Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines ( L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.Pinus sylvestris

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.

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.

scholarly journals Integrating Detailed Timber Assortments into Airborne Laser Scanning (ALS)-Based Assessments of Logging Recoveries

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1221
Author(s):  
Blanca Sanz ◽  
Jukka Malinen ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
Vesa Leppänen ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Power Plants ◽  
Laser Scanning ◽  
Wood Quality ◽  
Business Practice ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Airborne Laser ◽  
Mean Square Errors

The methodology presented here can assist in making timber markets more efficient when assessing the value of harvestable timber stands and the amounts of timber assortments during the planning of harvesting operations. Information on wood quality and timber assortments is essential for wood valuation and procurement planning as varying wood dimensions and qualities may be utilized and refined in different places, including sawmills, plywood mills, pulp mills, heating plants or combined heat and power plants. We investigate here alternative approaches for generating detailed timber assortments for Norway spruce (Picea abies (L.) H.Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.) from airborne laser scanning (ALS) data, aerial images, harvester data and field data. For this purpose, we used 665 circular plots, and logging recovery information recorded from 249 clear-cut stands using cut-to-length harvesters. We estimated timber assortment volumes, economic values and wood paying capabilities (WPC) for each stand in different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The bucking scenarios were (1) bucking-to-value using maximum sawlog and pulpwood volumes excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Norway spruce and Scots pine and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch, either excluding quality (the usual business practice) or including quality (a novel business practice). The results showed that our procedure can assist in locating stands that are likely to be more valuable and have the desired timber assortment distributions. We conclude that the method can estimate WPC with root mean square errors of 28.7%, 66.0% and 45.7% in Norway spruce, Scots pine and birch, respectively, for sawlogs and 19.3%, 63.7% and 29.5% for pulpwood.

Sign in / Sign up

Export Citation Format

Share Document