The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Qian, Ying; Li, Mingze; Zhen, Zhen; Hao, Yongmao; Wang, Bin

doi:10.3390/s20195555

Cited by 15 publications

(21 citation statements)

References 55 publications

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“…The miniaturized laser scanning sensors on UAV platforms have earned a good reputation in the field of forest inventory and surveying due to their various benefits, such as increased point densities, lower cost, flight route flexibility, and simple operation [13,14]. Compared with airborne laser scanning, UAVLS systems can obtain higher-density point cloud data at a local scale with a smaller bias and standard deviation when quantifying the crown structures, and thus have been increasingly applied for monitoring and estimating forest attributes [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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Section: Introductionmentioning

confidence: 99%

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

et al. 2020

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“…The tree factors used to establishment crown profile model should be selected according to the factors affecting the growth of trees. According to the relevant literature [2][3][4]6,11,34,35,37,38,44,47,51], the crown profile model is mainly affected by AGE, N, TH, DBH, LCL, CW, HCB, HLCR. In addition, in this study, we also defined the following composite tree factors: the tree crown length ratio, the HT to DBH ratio, and the tree crown fullness ratio.…”

Section: Datamentioning

confidence: 99%

“…Crown size and crown dimensions are important variables for imparting biological realism to individual-tree growth models [2]. The crown profile (crown width at any point in the crown [3]) affects the tree's physiological processes, principally photosynthesis, respiration, and transpiration, due to the utilization of light and precipitation, reflecting the crown size and crown dimensions [4][5][6]. Crown size is commonly used as both a predictor variable and a response variable in forest growth and yield models and biomass models [2,[7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of equations have been used to describe crowns, such as simple geometric shapes [31][32][33][34][35][36][37][38], segmented equations [39][40][41][42][43], variable-exponent equations [3,6,[44][45][46] and distribution functions [6,11,46,47]. These crown profile equations have shown their respective advantages under different regional conditions and with different tree species.…”

Section: Introductionmentioning

confidence: 99%

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Crown Profile Modeling and Prediction Based on Ensemble Learning

Chen

2022

Forests

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Improving prediction accuracy is a prominent modeling issue in relation to forest simulations, and ensemble learning is a new effective method for improving the precision of crown profile model simulations in order to overcome the disadvantages of statistical modeling. Background: Ensemble learning (a machine learning paradigm in which multiple learners are trained to achieve better performance) has strong nonlinear problem learning ability and flexibility in terms of analyzing longitudinal data, and it remains rarely explored so far in the field of crown profile modeling forest science. In this study, we explored the application of ensemble learning to the modeling and prediction of crown profiles. Methods: We evaluated the performance of ensemble learning procedures and marginal model in modeling crown profile using the crown profile database from China fir plantations in Fujian, in southern China. Results: The ensemble learning approach for the crown profile model appeared to have better performance and higher efficiency (R2 > 0.9). The crown equation model 18 showed an intermediate performance in its estimation, whereas GBDT (MAE = 0.3250, MSE = 0.2450) appeared to have the best performance and higher efficiency. Conclusions: The ensemble learning method can combine the advantages of multiple learners and has higher model accuracy, robustness and overall induction ability, and is thus an effective technique for crown profile modeling and prediction.

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“…The miniaturisation of sensors for UAV has contributed to the significant increase of applications in agriculture, such as thermal sensors [3,4], multispectral [5,6]; hyperspectral [7,8], and LiDAR sensors [9,10].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Soil Tillage Using UAV High-Resolution 3D Data

Rebelo

Nascimento

2021

Remote Sensing

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Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop’s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.

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The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Cited by 15 publications

References 55 publications

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Crown Profile Modeling and Prediction Based on Ensemble Learning

Measurement of Soil Tillage Using UAV High-Resolution 3D Data

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