Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Zhu, Xi; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Niemann, K. Olaf; Liu, Jing; Shi, Yifang

doi:10.1016/j.jag.2017.09.004

Cited by 82 publications

(129 citation statements)

References 46 publications

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“…Yun et al () used a support vector machine (SVM) model for four single trees, and the achieved overall accuracy was from 89.1% to 93.5%. Zhu et al () deployed a random forest classifier using both radiometric and geometric features. Their tests on 10 plots showed an overall accuracy of 0.84.…”

Section: Discussionmentioning

confidence: 99%

“…Overall, existing methods were either based on geometric (Wang et al, ) or radiometric intensity features (Béland et al, ) or a combination of both (Zhu et al, ). Methods based on intensity usually have limited application scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the distribution of training data greatly impacts the overall performance of machine learning methods (Wang, Hollaus, & Pfeifer, ). A combination of geometric and radiometric features may have advantage over using only one type (Zhu et al, ). Lastly, another preferable strategy is to deploy unsupervised geometric methods that are free of training data (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the distribution of training data greatly impacts the overall performance of machine learning methods (Wang, Hollaus, & Pfeifer, 2017). A combination of geometric and radiometric features may have advantage over using only one type (Zhu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for non‐destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open‐source tool with an end‐user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

2020

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“…In the past decade, there have been significant advancements in the methods for leaf and wood separation from TLS data. The methods developed were either based on the radiometric features [10] or geometric features [8], [11]- [16] or a combination of both [17]. Since the radiometric features mainly depend on the wavelength used by a particular sensor, methods based on radiometric features become sensorspecific.…”

mentioning

confidence: 99%

Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests

Moorthy

Calders

Vicari

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree-and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package.Index Terms-Leaf versus wood separation, LiDAR, machine learning, python package, tropical forests.

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Forest Structural Attribute Extraction

Su¹,

Hu²

2023

LiDAR Principles, Processing and Applications in Forest Ecology

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Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Cited by 82 publications

References 46 publications

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests

Forest Structural Attribute Extraction

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