Automatic tree species recognition with quantitative structure models

Åkerblom, Markku; Raumonen, Pasi; Mäkipää, Raisa; Kaasalainen, M.

doi:10.1016/j.rse.2016.12.002

Cited by 103 publications

(57 citation statements)

References 16 publications

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“…The quality of any TreeQSM output is a reflection of the quality of the point cloud on which it was based and the accuracy assessed for reconstruction. Several studies proved that TreeQSM was able to reconstruct smaller branches with high accuracy when these branches had sufficient point density for an proper reconstruction Calders et al 2015;Åkerblom et al 2017). In our study, we scanned the trees in dense tropical environments, which made it difficult to scan properly the smaller branches inside the crown.…”

Section: Discussionmentioning

confidence: 95%

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Lau

Bentley

Martius

et al. 2018

Trees

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122

134

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Key message A method using terrestrial laser scanning and 3D quantitative structure models opens up new possibilities to reconstruct tree architecture from tropical rainforest trees. Abstract Tree architecture is the three-dimensional arrangement of above ground parts of a tree. Ecologists hypothesize that the topology of tree branches represents optimized adaptations to tree's environment. Thus, an accurate description of tree architecture leads to a better understanding of how form is driven by function. Terrestrial laser scanning (TLS) has demonstrated its potential to characterize woody tree structure. However, most current TLS methods do not describe tree architecture. Here, we examined nine trees from a Guyanese tropical rainforest to evaluate the utility of TLS for measuring tree architecture. First, we scanned the trees and extracted individual tree point clouds. TreeQSM was used to reconstruct woody structure through 3D quantitative structure models (QSMs). From these QSMs, we calculated: (1) length and diameter of branches > 10 cm diameter, (2) branching order and (3) tree volume. To validate our method, we destructively harvested the trees and manually measured all branches over 10 cm (279). TreeQSM found and reconstructed 95% of the branches thicker than 30 cm. Comparing field and QSM data, QSM overestimated branch lengths thicker than 50 cm by 1% and underestimated diameter of branches between 20 and 60 cm by 8%. TreeQSM assigned the correct branching order in 99% of all cases and reconstructed 87% of branch lengths and 97% of tree volume. Although these results are based on nine trees, they validate a method that is an important step forward towards using tree architectural traits based on TLS and open up new possibilities to use QSMs for tree architecture.

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Section: Discussionmentioning

confidence: 95%

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Lau

Bentley

Martius

et al. 2018

Trees

Self Cite

122

134

View full text Add to dashboard Cite

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“…When combining multispectral images with LiDAR data, it is possible to achieve high accuracies (above 90%) in vegetation mapping [22]. In addition, terrestrial laser scanning is used for tree species classification and provides very detailed information about forest structure [23]. Although LiDAR and hyperspectral data possess high potential for species classification, their operational use is restricted owing to limited availability and high acquisition costs [3,24], and the applicability of these data in a regional or global scale is still limited [25].…”

Section: Introductionmentioning

confidence: 99%

Forest Stand Species Mapping Using the Sentinel-2 Time Series

et al. 2019

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Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

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“…In dense forest stands, the occlusion may limit the number of observations that are received from stems and crowns which decreases the forest characterization capacity [4,60]. In general, the number of scans that are required for automatic detection of each tree [23,49], tree species recognition [61], and systematic underestimation of tree height [21] are the major bottle-necks when mere TLS is used for forest characterization. Use of UAVs have mainly the same limitations as airborne remote sensing technologies, such as the lack of direct observation methods for single tree attributes.…”

Section: Discussionmentioning

confidence: 99%

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

Yrttimaa¹,

Saarinen²,

Kankare³

et al. 2020

Preprint

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Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably the root mean square error (RMSE) in Hg improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

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Automatic tree species recognition with quantitative structure models

Cited by 103 publications

References 16 publications

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Forest Stand Species Mapping Using the Sentinel-2 Time Series

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

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