Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests

Zhang, Zhengnan; Cao, Lin; Mulverhill, Christopher; Líu, Hao; Pang, Yong; Li, Zengyuan

doi:10.3390/f10020125

Cited by 18 publications

(7 citation statements)

References 100 publications

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“…Airborne lidar scanner (ALS) can support traditional forest inventories by providing precise measurements of forest attributes (e.g., stand density, mean basal area and dominant height) [4]. ALS has great potential to estimate and describe structural parameters in forest plantations [5]. Discret ALS data has been employed to describe stands forest structure, through two approaches according to the scale of analysis; single tree identification and data aggregation [1].…”

Section: Introductionmentioning

confidence: 99%

Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

et al. 2020

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Airborne lidar scanner (ALS) technology is used in a variety of applications, including forestry. ALS has enormous potential for the estimation of relevant biometric parameters in forest plantations. This study investigates the use of an object-oriented semi-automated segmentation algorithm for stands delineation, based on modeling ALS data, in plantations of Eucalyptus grandis and E. dunnii in Uruguay. The results show that non-parametric methods delivered more accurate and less biased results for total volume (TV) with R2 0.93, RMSE 20.04 m3 h−1 for E. grandis and R2 0.93, RMSE 18.43 m3 h−1 for E.dunnii; and above ground biomass (AGB) with R2 0.95, RMSE 70.2 kg h−1 for E. grandis and R2 0.96, RMSE: 71.2 Kg h−1 for E. dunnii. Parametric methods performed better for dominant height (Ho) with R2 0.98, RMSE 0.67 m and R2: 0.96, RMSE: 0.8 m for E. grandis and E. dunnii, respectively. The most informative ALS metrics for the estimation of AGB and TV were metrics related to the elevation in parametric models (Elev.70 and Elev.75), while for the non-parametric models (k-NN) they were Elev.75 and canopy density. For Ho, the ALS metrics selected were also related to elevation both in the parametric (Elev.90 and Elev.99) and random forest models (Elev.max and Elev.75). The segmentation methodology proposed here matched closely the segments delineated by human operators, and provides a low-cost, cost-effective, easy to apply and update model aimed at generating AGB or TV maps for harvest tasks, based on rasters derived from ALS metrics. The present research shows the capacity of ALS metrics to improve extensive strategic inventories; validating and promoting the adoption of ALS technology for inventory forest stands of Eucalyptus spp. in Uruguay.

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Section: Introductionmentioning

confidence: 99%

Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

et al. 2020

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“…LiDAR sensors loaded on ground vehicles and humans provide a bottom-up perspective of the high-density representation of tree trunk branches and vegetative elements at the low and middle parts of the forest canopy. Airborne LiDAR [13] provides a top-down measurement setup for the quantitative acquisition of features in the upper tree canopy, such as tree top locations and tree crown attributes; however, the tree properties at low heights are almost completely missed because the laser beam is intercepted by the foliage in the upper forest canopy. Hence, different scanning patterns have unique advantages in terms of providing useful tree characteristics from different scanning angles, which is suitable for different scale areas and forests composed of various tree species.…”

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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“…These studies recovered the Weibull-2P and the Johnson's SB distributions from LiDAR metrics. The Weibull function (2P and 3P) is the most commonly used distribution in this type of study [20,37,38]. However, in the present study, we used, for the first time within the framework of LiDAR-based research, three functions commonly used to describe and predict diameter distributions in forest stands, i.e., the beta [26], generalized beta [16] and gamma-2P [14] functions.…”

Section: Discussionmentioning

confidence: 99%

Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

Gorgoso-Varela

Ponce²,

Rodríguez-Puerta

2021

Remote Sensing

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The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.

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Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests

Cited by 18 publications

References 100 publications

Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

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

Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)

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