Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds

Ayrey, Elias; Fraver, Shawn; Kershaw, John A.; Kenefic, Laura S.; Hayes, D. J.; Weiskittel, Aaron R.; Roth, Brian E.

doi:10.1080/07038992.2017.1252907

Cited by 126 publications

(90 citation statements)

References 21 publications

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“…Integration of the methods proposed with layered approaches (e.g. Ayrey et al, 2017) appears as a promising option, as well as exploitation of coordinated data, such as visible and IR imaging. Such additional techniques will be used both to improve initial guesses over the completely random, or regular-grid-based approach, and to select the most likely candidates emerging from RANSAC prior, or instead of, clustering.…”

Section: Discussionmentioning

confidence: 99%

“…Yao, Krull, Krzystek, and Heurich (2014) use the normalized cuts algorithm to build clusters, adding additional attributes to each point besides XYZ position, such as pulse width and intensity obtained from processing the full-wave response, in order to exploit similarity. Ayrey et al (2017) proposed to apply k-means clustering after dividing the point cloud into horizontal layers, looking for consistencies across layers, and starting to form top layers so that local maxima are used as seeds when building 3D clusters form 2D clusters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single-tree detection in high-density LiDAR data from UAV-based survey

Balsi

Esposito

Fallavollita

et al. 2018

European Journal of Remote Sensing

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Unmanned aerial vehicle-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers). Most authors proposed methods based totally or partially on search of local maxima in the canopy, which has poor performance for species that have flat or irregular upper canopy, and for mixed forests, especially where taller trees hide smaller ones. Such considerations apply in particular to Mediterranean hardwood forests. In such context, it is imperative to use the whole volume of the point cloud, however keeping computational load tractable. The authors propose the use of a methodology based on modelling the 3D-shape of the tree, which improves performance with respect to maxima-based models. A case study, performed on a hazel grove, is provided to document performance improvement on a relatively simple, but significant, case. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-tree detection in high-density LiDAR data from UAV-based survey

Balsi

Esposito

Fallavollita

et al. 2018

European Journal of Remote Sensing

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“…In addition to area-based forest inventories, we believe that CNNs may also be able to address the issue of individual tree segmentation. A wide array of algorithms has been put forward to segment the crowns of individual trees from a LiDAR point cloud for the purpose of developing a tree-list inventory [75][76][77]. Concurrently, CNNs have been enormously effective at segmenting objects from photographic and video imagery [78,79].…”

Section: Discussionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

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Abstract:As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.

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“…Usually, CHMs or DSMs, point clouds and spectral information are used in tree segmentation processes [49]. Depending on the type of data, there are several algorithms for tree detection, being the detection rate dependent on the forest type-usually higher in coniferous stands than broadleaves [50][51][52], tree density and clustering [52][53][54] and not in the algorithm used [53].…”

Section: Data Processing Vegetation Segmentation and Classificationmentioning

confidence: 99%

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds

Cited by 126 publications

References 21 publications

Single-tree detection in high-density LiDAR data from UAV-based survey

Single-tree detection in high-density LiDAR data from UAV-based survey

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

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