Building Pareto Frontiers under tree-level forest planning using airborne laser scanning, growth models and spatial optimization

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

Annals of Forest Science ◽

10.1007/s13595-020-00985-7 ◽

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.

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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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Burned forest characterization at single-tree level with airborne laser scanning for assessing wildlife habitat

Remote Sensing of Environment ◽

10.1016/j.rse.2015.12.044 ◽

2016 ◽

Vol 175 ◽

pp. 231-241 ◽

Cited By ~ 28

Author(s):

Ángeles Casas ◽

Mariano García ◽

Rodney B. Siegel ◽

Alexander Koltunov ◽

Carlos Ramírez ◽

...

Keyword(s):

Laser Scanning ◽

Wildlife Habitat ◽

Airborne Laser Scanning ◽

Tree Level ◽

Single Tree ◽

Airborne Laser ◽

Burned Forest

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Large-area inventory of species composition using airborne laser scanning and hyperspectral data

Silva Fennica ◽

10.14214/sf.10244 ◽

2021 ◽

Vol 55 (4) ◽

Author(s):

Hans Ørka ◽

Endre Hansen ◽

Michele Dalponte ◽

Terje Gobakken ◽

Erik Næsset

Keyword(s):

Forest Management ◽

Species Composition ◽

Laser Scanning ◽

Hyperspectral Data ◽

Airborne Laser Scanning ◽

Tree Level ◽

Small Scale ◽

Large Area ◽

Airborne Laser ◽

Species Specific

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

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Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Remote Sensing ◽

10.3390/rs11030261 ◽

2019 ◽

Vol 11 (3) ◽

pp. 261 ◽

Cited By ~ 10

Author(s):

Darío Domingo ◽

Rafael Alonso ◽

María Teresa Lamelas ◽

Antonio Luis Montealegre ◽

Francisco Rodríguez ◽

...

Keyword(s):

Laser Scanning ◽

Growth Models ◽

Model Performance ◽

Basal Area ◽

Stand Density ◽

Airborne Laser Scanning ◽

Support Vector ◽

Low Density ◽

Airborne Laser ◽

Forest Attributes

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

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