Combining tree height samples produced by airborne laser scanning and stand management records to estimate plot volume in Eucalyptus plantations

2011 ◽  
Vol 41 (8) ◽  
pp. 1649-1658 ◽  
Author(s):  
Jari Vauhkonen ◽  
Lauri MehtÄtalo ◽  
Petteri Packalén
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Small Sample ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Stem Volume ◽  
Data Set ◽  
Stand Management ◽  
Tree Detection ◽  
Airborne Laser

Regular stand structure and availability of precise silvicultural management data produce a special situation regarding remote sensing based assessments of plantation forests. This study tested the use of stand management records to improve single-tree detection in a Eucalyptus plantation. Combined airborne laser scanning (ALS) and planting distance data were used to detect trees and extract their heights. The extracted heights were used as an input for volume estimation using both existing plot-level functions and new tree-level models. The accuracies were evaluated in a test data set of 191 field reference plots in which the diameters of the Eucalyptus urograndis (E. grandis (Hill) Maiden × E. urophylla S.T. Blake) trees varied from 6 to 41 cm and tree heights varied from 12 to 41 m. The constructed mixed-effects model that predicted stem volume from tree height resulted in a root mean squared error (RMSE) of 68 dm3 (15%) in a cross validation of the modeling data. The tree detection produced estimates of stem number with low bias (i.e., average difference between measured and estimated) and an RMSE of 6% of the mean, whereas plot-level mean and dominant heights were estimated with RMSEs of 1.5 m (5%) and 2 m (6%), respectively, using ALS data alone. The difference of about 60 cm observed between the ALS-based and field-measured dominant height was most likely caused by the penetration of the laser pulses through the canopy. A system of plot-level models that employed a small sample of calibration field data gave RMSEs of 1 m (3%) and 2.2 m2/ha (9%) for site index and basal area, respectively. The plot volume was estimated with an RMSE of 44 m3/ha (12%) at best. A similar residual variation was observed in the volume estimates of an area-based method applied to the same data set. The combined results suggest the feasibility of the proposed methodology in a plantation inventory using ALS data with a density of only 1.5 pulses/m2.

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

2019 ◽  
Vol 49 (3) ◽  
pp. 228-236 ◽  
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.

Refining and evaluating a Horvitz– Thompson-like stand density estimator in individual tree detection based on airborne laser scanning

2021 ◽  
Author(s):  
Kasper Kansanen ◽  
Petteri Packalen ◽  
Timo Lähivaara ◽  
Aku Seppänen ◽  
Jari Vauhkonen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Tuning Parameter ◽  
Density Estimator ◽  
Validation Data ◽  
Data Set ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser

Horvitz--Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than the previous papers and present and discuss the methods for estimating the tuning parameter of the estimator using a functional $k$-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a $13$\% lower RMSE than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data to the results.

scholarly journals Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 268
Author(s):  
Jan Novotný ◽  
Barbora Navrátilová ◽  
Růžena Janoutová ◽  
Filip Oulehle ◽  
Lucie Homolová
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Airborne Laser Scanning ◽  
Allometric Equations ◽  
Spruce Forest ◽  
Individual Tree ◽  
Detection Rates ◽  
Site Specific ◽  
Tree Detection ◽  
Airborne Laser

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning

2016 ◽  
Vol 46 (6) ◽  
pp. 753-765 ◽  
Author(s):  
Zhengyang Hou ◽  
Qing Xu ◽  
Jari Vauhkonen ◽  
Matti Maltamo ◽  
Timo Tokola
Keyword(s):  
Laser Scanning ◽  
Economic Value ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Individual Tree ◽  
Tree Detection ◽  
Breast Height ◽  
Airborne Laser ◽  
Wood Procurement ◽  
Species Specific

The planning of wood procurement requires reliable information about the species-specific timber assortments on which the economic value of a production forest depends. The timber assortments refer to the stem volumes of the sawlog and pulpwood fractions, specified in terms of both timber quality and allowable log dimensions, e.g., the stem diameter at breast height (dbh). We propose here an airborne laser scanning based calibration framework for generating species-specific dbh distributions that combines the area-based approach (ABA) and individual-tree detection (ITD), two established and independent approaches for retrieving forest attributes from airborne laser scanning data. Both ABA- and ITD-derived dbh distributions were generated nonparametrically for pine, spruce, coniferous, deciduous, and all species and assessed with respect to the plot-level species-specific total stem volume (m3·ha–1) and approximations of volume of timber assortments. Although after calibration, the total volume of all species and the volume approximations of coniferous sawlog and spruce pulpwood decreased in accuracy by 4%–7%, the calibration improved the accuracy of the other 12 species-specific estimates by 2%–17%, testifying to the general effectiveness of the proposed calibration framework.

scholarly journals Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

2018 ◽  
Vol 53 (12) ◽  
pp. 1373-1382 ◽  
Author(s):  
Diogo Nepomuceno Cosenza ◽  
Vicente Paulo Soares ◽  
Helio Garcia Leite ◽  
José Marinaldo Gleriani ◽  
Cibele Hummel do Amaral ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Tree Height ◽  
Point Clouds ◽  
Equation System ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Individual Tree ◽  
Local Maxima ◽  
Airborne Laser

Abstract: The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.

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

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.

Sign in / Sign up

Export Citation Format

Share Document