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

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 1â3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2â6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4â6.2 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

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The transferability of airborne laser scanning based tree-level models between different inventory areas

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0128 ◽

2019 ◽

Vol 49 (3) ◽

pp. 228-236 ◽

Cited By ~ 4

Author(s):

Tomi Karjalainen ◽

Lauri Korhonen ◽

Petteri Packalen ◽

Matti Maltamo

Keyword(s):

Laser Scanning ◽

Nearest Neighbor ◽

Airborne Laser Scanning ◽

Tree Level ◽

Stem Volume ◽

K Nearest Neighbor ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser ◽

Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

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

Forest Ecosystems ◽

10.1186/s40663-020-00274-9 ◽

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.

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Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data

International Journal of Remote Sensing ◽

10.1080/01431160903380615 ◽

2010 ◽

Vol 31 (5) ◽

pp. 1213-1226 ◽

Cited By ~ 33

Author(s):

Jari Vauhkonen

Keyword(s):

Scots Pine ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

3D Geometry ◽

Crown Base Height

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Elimination of systematic errors from airborne laser scanning data

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. ◽

10.1109/igarss.2005.1526225 ◽

2005 ◽

Cited By ~ 3

Author(s):

S. Filin

Keyword(s):

Laser Scanning ◽

Systematic Errors ◽

Airborne Laser Scanning ◽

Airborne Laser

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International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2543225 ◽

2016 ◽

Vol 54 (9) ◽

pp. 5011-5027 ◽

Cited By ~ 61

Author(s):

Yunsheng Wang ◽

Juha Hyyppa ◽

Xinlian Liang ◽

Harri Kaartinen ◽

Xiaowei Yu ◽

...

Keyword(s):

Laser Scanning ◽

Forest Ecology ◽

Canopy Structure ◽

Airborne Laser Scanning ◽

Detection Methods ◽

Individual Tree ◽

Tree Detection ◽

International Benchmarking ◽

Airborne Laser ◽

The Individual

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Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

Frontiers in Plant Science ◽

10.3389/fpls.2020.596315 ◽

2021 ◽

Vol 11 ◽

Author(s):

David Pont ◽

Heidi S. Dungey ◽

Mari Suontama ◽

Grahame T. Stovold

Keyword(s):

Laser Scanning ◽

Management Practices ◽

Spatial Models ◽

Airborne Laser Scanning ◽

Tree Level ◽

Residual Variance ◽

Spatial Effects ◽

Individual Tree ◽

Airborne Laser ◽

Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

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