Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Picos, Juan; Bastos, Guillermo; Míguez, Daniel Pedro; Alonso, Laura; Armesto, Julia

doi:10.3390/rs12050885

Cited by 55 publications

(38 citation statements)

References 53 publications

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“…In terms of the choice of sensor, light detection and ranging (LiDAR) is a promising technology to measure plant height and predict biomass [10]. LiDAR equipped UAVs have been demonstrated to work well in forestry [11,12], maize [13], and shorter annual crops such as wheat [14]. There is one study using tractor mounted LiDAR to characterize a bioenergy crop, Miscanthus giganteus, with good results (reported accuracy 92-98.2%) [15].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

et al. 2020

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Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

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Section: Introductionmentioning

confidence: 99%

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

et al. 2020

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“…They segmented the gradient map using watershed with the treetops as markers and improved the segmentation to yield the crown map. In addition, Picos et al [52] detected and measured the height of Eucalyptus trees in a plantation. They investigated two methods for detection: One based on constructing overlapping polygons around each point in the stem cloud, and another employing density estimation with an axis-aligned bivariate quartic kernel.…”

Section: Image Sourcesmentioning

confidence: 99%

Assessment of Tree Detection Methods in Multispectral Aerial Images

et al. 2020

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Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.

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“…As a new revolutionary measurement technology, it has attracted wide interest from industry and academia [ 1 ]. This technology has been widely used in 3D vegetation mapping [ 2 ], disaster management [ 3 , 4 ], forestry inventory [ 5 , 6 , 7 , 8 ], power line inspection [ 9 , 10 ], 3D city development [ 11 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area

Chen

Wang

2021

Sensors

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Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.

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Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Cited by 55 publications

References 53 publications

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

Assessment of Tree Detection Methods in Multispectral Aerial Images

A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area

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