A Geometric Method for Wood-Leaf Separation Using Terrestrial and Simulated Lidar Data

Tao, Shengli; Guo, Qinghua; Su, Yanjun; Xu, Shiwu; Li, Yumei; Wu, Fangfang

doi:10.14358/pers.81.10.767

Cited by 85 publications

(75 citation statements)

References 32 publications

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“…Therefore, leaf and wood can be separated from the spectral information of each point as well. This is based on the fact that different components of a tree feature discriminatory optical properties at the operating wavelengths of the laser scanning system (Tao et al, 2015). In this study, the birch leaf and wood were separated with a hard normalized difference vegetation index (NDVI) threshold value of 0.2.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Assessment of canopy structure at tree or branch scale can be difficult with laser scanning data acquired from satellite and airborne platforms (Tao et al, 2015). Terrestrial Laser Scanning (TLS), on the other hand, has been established as an efficient tool for acquiring 3D data used for a range of fine-scale forest studies (Liang et al, 2016), including stem mapping (Liang et al, 2012), tree height measurement (Olofsson et al, 2014), diameter estimation (Wang et al, 2017), stem curve retrieval , biomass calculation (Kankare et al, 2013), and leaf area index (LAI) estimation (Zheng et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Terrestrial Laser Scanning (TLS), on the other hand, has been established as an efficient tool for acquiring 3D data used for a range of fine-scale forest studies (Liang et al, 2016), including stem mapping (Liang et al, 2012), tree height measurement (Olofsson et al, 2014), diameter estimation (Wang et al, 2017), stem curve retrieval , biomass calculation (Kankare et al, 2013), and leaf area index (LAI) estimation (Zheng et al, 2013). To better retrieve forest ecological attributes, it is often necessary to separate wood and leaf components of trees (Tao et al, 2015). For example, estimation of LAI requires to screen out wood points, otherwise the wood returns will artificially increase the apparent foliage content (Béland et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Intensity based methods (Pfennigbauer and Ullrich, 2010;Béland et al, 2014) use radiometric information of objects captured by a laser scanner. The assumption is that wood and leaf components have different optical properties at the operating wavelength of the laser scanner (Tao et al, 2015). By determining a proper intensity threshold, wood and leaf points can be separated.…”

Section: Introductionmentioning

confidence: 99%

“…Local structurerelated saliency information are derived from 3D points and supervised machine learning methods such as Support Vector Machine (SVM) (Yun et al, 2016) and Gaussian Mixture Model (GMM) (Ma et al, 2016) are often employed to classify wood and leaf points. Some direct geometric methods were also reported (Tao et al, 2015). Nevertheless, geometry based machine learning methods are rarely systematically examined for woodleaf classification, although it is a well-known and widely adapted technique for other classification tasks (Weinmann et al, 2013(Weinmann et al, , 2017Brodu and Lague, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Wang

Hollaus

Pfeifer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Terrestrial laser scanning‐derived canopy interception index for predicting rainfall interception

Gao

Zhu

et al. 2020

Ecohydrology

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Rainfall interception (RI) by forest canopies is an important process in hydrological cycling in forest ecosystems. However, accurately predicting RI is a challenging topic. In this study, a dimensionless descriptor, canopy interception index (CII), for predicting RI was defined. The terrestrial laser scanning was used to estimate CII in four temperate forest types, including Korean pine (Pinus koraiensis) plantation forest (KPF) stands, larch (Larix spp.) plantation forest (LPF) stands, mixed broadleaved forest (MBF) stands and Mongolian oak (Quercus mongolica) forest (MOF) stands. Using the measured RI values over the rainy seasons in 2017 and 2018, CII's performance for predicting RI was tested and also compared with several other indices (LAI: leaf area index, PAI: plant area index and ACH: average canopy height). The results indicated that CII was significantly and strongly related with RI for the four forest types together (R2 = 0.79), as well as for an individual forest type (R2 = 0.55–0.63). More importantly, its performance was better than those from LAI (R2 = 0.33–0.43), PAI (R2 = 0.40–0.53) and ACH (R2 = 0.35). All those results demonstrated that CII was an efficient index for accurately predicting RI. The potential applications of CII were also discussed.

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Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

2020

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Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach leads to significant improvement over the state-of-the-art methods in segmentation task.

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A Geometric Method for Wood-Leaf Separation Using Terrestrial and Simulated Lidar Data

Cited by 85 publications

References 32 publications

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Feasibility of Machine Learning Methods for Separating Wood And Leaf Points From Terrestrial Laser Scanning Data

Terrestrial laser scanning‐derived canopy interception index for predicting rainfall interception

Segmentation of unbalanced and in-homogeneous point clouds and its application to 3D scanned trees

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