Deriving Merchantable Volume in Poplar through a Localized Tapering Function from Non-Destructive Terrestrial Laser Scanning

Sun, Yuan; Liang, Xinlian; Liang, Ziyu; Welham, Clive; Li, Weizheng

doi:10.3390/f7040087

Cited by 45 publications

(25 citation statements)

References 21 publications

(24 reference statements)

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“…LiDAR is an active remote sensing technology that emits laser pulses to the surface of vegetative elements and analyses the return signal [5]. The point cloud data obtained from LiDAR are valuable and convenient for estimating a variety of tree attributes, e.g., leaf area [6], phenotypic characteristics of leaf elements [7], tree structural attributes [8] and volume of timber [9]. In recent years, the efficiency of measurements has been greatly improved by mobile LiDAR, such as LiDAR loaded on ground vehicles [10], humans (hand-held [11] and backpack [12] modes) and manned aircraft.…”

Section: Introductionmentioning

confidence: 99%

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

et al. 2019

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Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury, were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height, and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimum number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR.

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Section: Introductionmentioning

confidence: 99%

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

et al. 2019

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“…A non-destructive and efficient measurement technique for single-tree stem profile estimation is needed. TLS is such a technique that could be used to develop locally-suitable taper functions [4]. Stem profile estimations from TLS could also be useful to predict between and within forest stand variations in order to plan forest operations and match demands from the forest industry.…”

Section: Introductionmentioning

confidence: 99%

Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties

Olofsson

Holmgren

2016

Forests

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Abstract:A method for automatic stem detection and stem profile estimation based on terrestrial laser scanning (TLS) was validated. The root-mean-square error was approximately 1 cm for stem diameter estimations. The method contains a new way of extracting the flatness saliency feature using the centroid of a subset of a point cloud within a voxel cell that approximates the point by point calculations. The loss of accuracy is outweighed by a much higher computational speed, making it possible to cover large datasets. The algorithm introduces a new way to connect surface patches belonging to a stem and investigates if they belong to curved surfaces. Thereby, cylindrical objects, like stems, are found in the pre-filtering stage. The algorithm uses a new cylinder fitting method that estimates the axis direction by transforming the TLS points into a radial-angular coordinate system and evaluates the deviations by a moving window convex hull algorithm. Once the axis direction is found, the cylinder center is chosen as the position with the smallest radial deviations. The cylinder fitting method works on a point cloud in both the single-scan setup, as well as a multiple scan setup of a TLS system.

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“…The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.Remote Sens. 2019, 11, 908 2 of 21 nearly 6.93 × 10 7 ha and accounts for 26.2% of the world's total planted forest area [4,5]. Ginkgo (Ginkgo biloba L.) is a characteristic planted forest species in China [6].…”

mentioning

confidence: 99%

“…Remote Sens. 2019, 11, 908 2 of 21 nearly 6.93 × 10 7 ha and accounts for 26.2% of the world's total planted forest area [4,5]. Ginkgo (Ginkgo biloba L.) is a characteristic planted forest species in China [6].…”

mentioning

confidence: 99%

Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

Shen

Cao

et al. 2019

Remote Sensing

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Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

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Deriving Merchantable Volume in Poplar through a Localized Tapering Function from Non-Destructive Terrestrial Laser Scanning

Cited by 45 publications

References 21 publications

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties

Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

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