Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in European broadleaf beech forests

Liu, Jing; Wang, Zhihui; Skidmore, A.K.; Jones, Simon; Heurich, Marco; Beudert, Burkhard; Premier, Joe

doi:10.1016/j.isprsjprs.2019.09.015

Cited by 17 publications

(10 citation statements)

References 75 publications

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“…Considerable research using HELIOS to validate or calibrate extracted parameters has been undertaken in multiple forestry applications. Liu et al (2019a) and Liu et al (2019b) estimated leaf angles on trees, where ground truth is practically impossible to obtain, because in real settings, leaves are continuously moving due to wind. VLS allows to validate their approach of leaf angle estimation and subsequently apply it to real data.…”

Section: Algorithm and Methods Evaluation: Validation And Calibrationmentioning

confidence: 99%

Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning

Winiwarter¹,

Pena²,

Weiser³

et al. 2021

Preprint

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Section: Algorithm and Methods Evaluation: Validation And Calibrationmentioning

confidence: 99%

Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning

Winiwarter¹,

Pena²,

Weiser³

et al. 2021

Preprint

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“…Although it is expected that the precise isolation of tree crowns is extremely changeling especially in complex forests where trees have their crowns interact with each other, we showed that a fully automatic method can produce a reasonable segmentation that leads to a relative RMSE at the magnitude of 20% for crown area estimates. It is, however, remains unknown how such a fully automatic tree extraction would impact the estimation of more advanced tree physiological attributes such as tree leaf angle distribution (Liu et al 2019) and above-ground biomass (AGB) (Calders et al 2020).…”

Section: Advantages Of Graph Pathingmentioning

confidence: 99%

“…A point cloud contains highly precised 3D coordinates in combination with radiometric information of objects (Pfeifer et al 2008). A range of applications including retrieval of conventional forest inventories parameters such as stem location (Liang et al 2014), diameter at the breast height (DBH) (Wang et al 2017), stem curve (Wang et al 2019), and estimate of tree physiological properties such as leaf angle distribution (Liu et al 2019), branching architecture (Lau et al 2018), and standing volume (Calders et al 2015) have shown the promising capability of TLS in quantifying ecosystem structure and function.…”

Section: Introductionmentioning

confidence: 99%

Individual tree extraction from terrestrial laser scanning data via graph pathing

et al. 2021

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Background Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

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“…Indirect non-contact methods based on the measurement of light transmission through canopies offer an alternative for LAI measurement. Portable optical instruments such as LAI-2000/2200 plant canopy analyzer and hemispherical photography systems are widely used to this end (Chen et al 1997;Hardwick et al 2015;Li et al 2017a;Liu et al 2019). In the last decades, indirect LAI measurement coupled with remote sensing techniques has provided a timely and labor-efficient solution for LAI estimation over large spatial and long temporal scales (Ogutu et al 2012;Zhang et al 2012;Li et al 2017b;Brown et al 2020;Dong et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong

Li¹,

Wong²,

Fung³

et al. 2022

Preprint

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Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the impact of the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in the Mai Po, Hong Kong. The LAI of overstory and understory mangroves was measured using digital hemispherical photography (DHP) in the field to calculate the OLe and ULe. LiDAR data was employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. Single red edge VI derived from HSI data delivered the low RMSE = 0.12 and high R2adj = 0.79 for OLe model fitting. The synergetic use LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. Apart from penetration attribute, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation.

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Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in European broadleaf beech forests

Cited by 17 publications

References 75 publications

Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning

Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning

Individual tree extraction from terrestrial laser scanning data via graph pathing

Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong

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