Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning

Brede, Benjamin; Terryn, Louise; Barbier, Nicolas; Bartholomeus, Harm; Bartolo, Renée E.; Calders, Kim; Derroire, Géraldine; Moorthy, Sruthi M. Krishna; Lau, Alvaro; Levick, Shaun R.; Raumonen, Pasi; Verbeeck, Hans; Wang, Di; Whiteside, Timothy G.; Zee, Jens van der; Herold, Martin

doi:10.1016/j.rse.2022.113180

Cited by 57 publications

(43 citation statements)

References 70 publications

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“…The point cloud was then downsampled using a voxel size of 0.026 m and 0.02 m for Wytham and Sepilok respectively. At Wytham, scans were done within a larger 6 ha area to ensure the best possible data quality within our 1 ha study area (Calders et al 2022). TLS data were collected throughout December 2015 and January 2016 (in leaf-off conditions) on a 20 m x 20 m grid (46).…”

Section: Als Datamentioning

confidence: 99%

“…Understory trees are not visible in most remote sensing data, so can only be segmented using LiDAR data, which we show is currently inaccurate. This challenge may be addressed using extremely high pulse density LiDAR data collected using unoccupied Areial Vehicles (UAVs) (62). The processing power required to apply existing ALS segmentation algorithms to these massive data sets may be prohibitive, so we expect efficient algorithms to be adapted for this specific purpose.…”

Section: What Next For Tree Segmentation Algorithms?mentioning

confidence: 99%

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Tree segmentation in airborne laser scanning data is only accurate for canopy trees

Cao

Ball

Coomes

et al. 2022

Preprint

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Individual tree segmentation from airborne laser scanning data is a longstanding and important challenge in forest remote sensing. There are a number of segmentation algorithms but robust intercomparison studies are rare due to the difficulty of obtaining reliable reference data. Here we provide a benchmark data set for temperate and tropical broadleaf forests generated from labelled terrestrial laser scanning data. We compare the performance of four widely used tree segmentation algorithms against this benchmark data set. All algorithms achieved reasonable accuracy for the canopy trees, but very low accuracy for the understory trees. The point cloud based algorithm AMS3D (Adaptive Mean Shift 3D) had the highest overall accuracy, closely followed by the 2D raster based region growing algorithm Dalponte2016+. This result was consistent across both forest types. This study emphasises the need to assess tree segmentation algorithms directly using benchmark data. We provide the first openly available benchmark data set for tropical forests and we hope future studies will extend this work to other regions.

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Section: Als Datamentioning

confidence: 99%

Section: What Next For Tree Segmentation Algorithms?mentioning

confidence: 99%

Tree segmentation in airborne laser scanning data is only accurate for canopy trees

Cao

Ball

Coomes

et al. 2022

Preprint

Self Cite

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“…TLS is an active remote sensing technology that generates a detailed 3D point cloud of the surrounding area with centimetre to millimetre accuracy (Newnham et al 2015, Calders et al in Duncanson et al 2021). A TLS-based approach has been used to estimate AGB across a range of forest types (Demol et al, 2022a), for example; tropical forests (Momo Takoudjou et al, 2018, Gonzalez de Tanago et al, 2018, Beyene et al, 2020, Burt et al, 2021, Levick et al, 2021, Brede et al, 2022), temperate forests (Calders et al, 2015, Stovall et al, 2017, Disney et al, 2020, Calders et al), mangroves (Feliciano et al, 2014) and trees outside forests (Wilkes et al, 2018, Kükenbrink et al, 2021, Van Den Berge et al, 2021). Over the past decade, area scanned has increased from a few trees to systematic acquisition across multiple hectares that replicates forest inventory protocols (Wilkes et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…If P is of sufficient density and quality then S can be accurately segmented and an unbiased assessment of plot-level AGB, with associated uncertainties, can be produced (Calders et al, 2015, Momo Takoudjou et al, 2018, Burt et al, 2021). Currently, the most accurate method of S ⊂ P is to manually segment individual trees from their neighbours and other vegetation using interactive point cloud editing software (Momo Takoudjou et al, 2018, Gonzalez de Tanago et al, 2018, Disney et al, 2020, Brede et al, 2022). Manually segmenting trees increases tree- and plot-level estimation accuracy by a factor of 10 and 3 respectively, compared to existing automated TLS pipelines (Martin-Ducup et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

TLS2trees: a scalable tree segmentation pipeline for TLS data

Wilkes

Disney

Armston

et al. 2022

Preprint

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Above Ground Biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically greater or equal to 1 ha) using inventory measurements and allometry. In recent years, Terrestrial Laser Scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated across 10 plots of 7 forest types; from open savanna to dense tropical rainforest, where a total of 10,557 trees are segmented. TLS2trees segmented trees are compared to 1,281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is plus/minus 0.4 m3 and %RMSE is approximately 60%. To facilitate improvements to the presented methods as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software (FOSS).

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“…The rapid development of next-generation oblique photogrammetry technology makes it possible to rapidly extract 3D information from the surface and construct high-precision digital surface models (DSM). In particular, the technological breakthroughs in UAV and stereo image pairs have provided technical support for carrying out 3D landscape ecology work (Kang et terrestrial laser scanning (TLS) for assessing above-ground biomass, providing a new technical route for the topic of 3D green volume (Brede et al, 2022). However, most of the existing 3D landscape indices are based on theory, so the operability is low, and they lack a complete process from the acquisition of raw data to the construction of indices to the assessment and evaluation; meanwhile, the studied ones are primarily focused on urban areas, and less attention is paid to critical ecological regions such as islands and coastal zones.…”

Section: Introductionmentioning

confidence: 99%

Construction of 3D landscape index based on oblique photogrammetry and its application in islands

Guo

Wang

Liu

et al. 2022

Preprint

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The landscape index is quantitative indicator to reflect the composition and spatial configuration characteristics of landscape ecological structures. Following the "P-C-L" frameworks, eight 3D landscape indexes were constructed to quantitatively describe the spatial landscape features and two sensitivity indexes were built to identify the differences between 2D and 3D. Based on two kinds of oblique photogrammetry data of Sichang Island and Tianheng Island, the results show that: 1) The shape index (TPSI) indicates the spatial shape of the patch scale. The TPSI of vegetation was generally higher than that of buildings, with a reasonable correlation (R2 = 0.698). The classification index (TCI) shows remarkable performance in patch-type identification. When the value of TCI approaches values of 100 or 33, the patch-type is probably building or vegetation, respectively, with a classification accuracy rate of 95% after verification. 2) The sensitivity indexes, GSC and ESC, provide an evaluation criterion for the attribute transformation from 2D to 3D. The dimensional change significantly affected the buildings and arbor, with a GSC of 6.697 and 2.306, respectively. The changes in low-rise ground features were not significant. On class and landscape scales, the dynamic ranges of all six 3D indexes increased compared to 2D indexes. The highest was TLSI (3D Landscape Shape Index), and the lowest was TSHEI (3D Shannon Evenness Index), with a growth rate of 349.65% and 0.3%, respectively. 3) The 3D landscape indexes can better feature the biomass and the intensity of human development and construction activities on all scales. Its combination with modern remote sensing and mapping technology can provide a more sound ecological assessment method for spatial planning of different ecosystems.

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Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning

Cited by 57 publications

References 70 publications

Tree segmentation in airborne laser scanning data is only accurate for canopy trees

Tree segmentation in airborne laser scanning data is only accurate for canopy trees

TLS2trees: a scalable tree segmentation pipeline for TLS data

Construction of 3D landscape index based on oblique photogrammetry and its application in islands

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