Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy

Sibona, Emanuele; Vitali, Alessandro; Meloni, Fabio; Caffo, Lucia; Dotta, Alberto; Lingua, Emanuele; Motta, Renzo; Garbarino, Matteo

doi:10.3390/f8010007

Cited by 59 publications

(65 citation statements)

References 33 publications

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“…Moreover, improving forest plantation productivity and efficiency are important for reducing harvest pressure on natural forests. To achieve efficiency gains in operational forest management, a wide range of forest inventory attributes are required to be measured accurately at high spatial resolution and landscape to regional extents [34,43]. More detailed inventory information can allow forest owners to make better decisions concerning the timing of timber sales, and allow forest companies to optimize their wood supply chain from forest to factory [44].…”

Section: Discussionmentioning

confidence: 99%

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Silva

Klauberg

Hudak

et al. 2017

Forests

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Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R 2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume.

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Section: Discussionmentioning

confidence: 99%

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Silva

Klauberg

Hudak

et al. 2017

Forests

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“…However, tree height growth can be demanding to estimate from the ground, because of the difficulty in determining the top of the crown [2,3], especially in species that do not display strong apical dominance. Crown growth is also difficult to determine when crowns overlap, as the edges of the crowns are often hidden by the branches of other trees [4].…”

Section: Introductionmentioning

confidence: 99%

Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands

Guerra-Hernández

González-Ferreiro

Monleón

et al. 2017

Forests

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High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R 2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86-87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015-2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.

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“…The accurate use of remote sensing technologies to estimate plant functional traits has been well documented [4,10,22]. Physiological traits, such as leaf pigment content, can be derived from spectroscopy, while morphological traits, such as plant height, can be measured from LiDAR [22,52]. This paper uses a combination of these two methodologies to derive 14 functional traits from 40 individual tress of the same species.…”

Section: Remote Sensing Of Intraspecific Trait Variationmentioning

confidence: 99%

“…The LiDAR data used to estimate morphological functional traits in this study was obtained freely from the Municipality of North Cowichan. With a density of 16 points/m 2 , this dataset provides excellent representation of tree crowns and allows for the accurate derivation of traits like tree height [52]. Due to the open-source nature of this LiDAR data considerable time and cost were avoided when compared to measuring these attributes in the field.…”

Section: Remote Sensing Of Intraspecific Trait Variationmentioning

confidence: 99%

Retrieving Foliar Traits of Quercus garryana var. garryana across a Modified Landscape Using Leaf Spectroscopy and LiDAR

et al. 2019

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Understanding the ecological effects of human activities on an ecosystem is integral to the implementation of conservation management plans. The plasticity of plant functional traits presents an opportunity to examine the capacity for intraspecific functional trait variations to be indicators of anthropogenic landscape modifications. The presence of intraspecific trait variation would indicate that plants of a single species could to be used to evaluate and map functional diversity, a common metric used to measure biodiversity. This study uses leaf spectroscopy, light detection and ranging (LiDAR) and partial least squares regression (PLSR) to examine the intraspecific variation of functional traits in a population of 40 Quercus garryana experiencing varying levels of anthropogenic influence at the site level (<0.3 km 2 ) in Duncan, B.C., Canada. These individuals vary in their spatial relationship to roads, agricultural land use change and an encroaching Coastal Douglas-fir forest. A total of 14 functional traits were estimated using pre-determined PLSR coefficients from a multi-species dataset. LiDAR data for each tree and were organized into functional categories based on their influence of plant lifeform, leaf growth or leaf structure. Principal components analysis was performed on each functional category to determine the relative influence of each trait. Results show that leaf growth and lifeform functional trait categories express significant variation in relation to three anthropogenic landscape modifications, while traits associated to leaf structure only varied between land use types (p = 0.05). Diameter at breast height (DBH), mass-based chlorophyll and leaf mass per area (LMA) showed the strongest variation across treatments. These findings support the hypothesis that trait variation exists in small populations of the same species and illustrate that spectroscopy can be used to indirectly sense land use via the leaf functional traits of a single tree species.

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Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy

Cited by 59 publications

References 33 publications

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands

Retrieving Foliar Traits of Quercus garryana var. garryana across a Modified Landscape Using Leaf Spectroscopy and LiDAR

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