Individual Tree Delineation in Windbreaks Using Airborne-Laser-Scanning Data and Unmanned Aerial Vehicle Stereo Images

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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Individual tree detection and crown segmentation based on metabolic theory from airborne laser scanning data

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.15.034504 ◽

2021 ◽

Vol 15 (03) ◽

Author(s):

Honglu Xin ◽

Yadvinder Malhi ◽

David A. Coomes ◽

Yi Lin ◽

Baoli Liu ◽

...

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Metabolic Theory ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser

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Individual tree detection from airborne laser scanning data based on supervoxels and local convexity

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2019.100242 ◽

2019 ◽

Vol 15 ◽

pp. 100242 ◽

Cited By ~ 1

Author(s):

Anandakumar M. Ramiya ◽

Rama Rao Nidamanuri ◽

Ramakrishnan Krishnan

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Local Convexity ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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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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