A Lightweight Uav-Based Laser Scanning System for Forest Application

Torres, Fernanda Magri; Tommaselli, Antônio Maria Garcia

doi:10.1590/s1982-21702018000300021

Cited by 8 publications

(16 citation statements)

References 16 publications

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“…Ref. [41] estimated the DBH of urban trees by a linear regression model based on variables extracted from a CHM obtained from the UAV-based laser scanning system developed by [32].…”

Section: Introductionmentioning

confidence: 99%

“…Most recent studies were aimed to assess whether UAV-LS point clouds are qualified for individual tree mapping and modeling in various forest stands [42], how their accuracies are by comparison with terrestrial laser scanning [43], whether DBH and tree position estimation algorithms developed for ALS are suitable for UAV-LS data [44] and finally, how the level of processing automation impacts the tree mapping and modeling accuracy [15]. While the accuracy of UAV point clouds is overall high [32], the evaluation of further data processing algorithms for higher level products delivery in forestry is necessary for a comprehensive assessment of UAV-LS especially in complex forest structures. For example, Ref.…”

Section: Introductionmentioning

confidence: 99%

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Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

Torresan

Carotenuto

Chiavetta

et al. 2020

Drones

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In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r2 for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity.

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“…Ref. [41] estimated the DBH of urban trees by a linear regression model based on variables extracted from a CHM obtained from the UAV-based laser scanning system developed by [32].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

Torresan

Carotenuto

Chiavetta

et al. 2020

Drones

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“…The use of the Ibeo LUX laser unit was motivated by its compact structure (164.5 x 93 x 88 mm) with a weight less than 1 kg, and the measurement-range from 0.3 to 200 m. The nominal ranging resolution of this laser unit is 4 cm in terrestrial application. However, considering UAV platforms, an average altimetric accuracy of 10 cm is achieved, as presented by Jaakkola et al, (2010), Wallace et al, (2012) and Torres and Tommaselli (2018).…”

Section: Uav-lidar System and Mathematical Modelmentioning

confidence: 99%

“…The UAV-LiDAR devices were synchronized using a postprocessing synchronization (offline) with a precision of 0.7 ms. The process of point cloud generation was implemented in the C++ language, developed by Torres and Tommaselli (2018). The laser mathematical model used to generate the UAV-ALS point cloud is presented in Equation 1 (El-Sheimy et al, 2005, adapted by Torres and Tommaselli, 2018).…”

Section: Uav-lidar System and Mathematical Modelmentioning

confidence: 99%

Evaluation of Multiple Linear Regression Model to Obtain DBH of Trees Using Data From a Lightweight Laser Scanning System on-Board a Uav

Machado

Tommaselli

Tachibana

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R2 adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&thinsp;cm.

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“…The use of UAVs associated with image processing techniques has been the focus of many studies in the forestry field (Inoue et al, 2014;Torres and Tommaselli, 2018), especially regarding the difference vegetation index that are parameters obtained from mathematical manipulation of spectral reflectance measurements that can be applied to several purposes. These vegetative index, such as the normalized difference vegetation index (NDVI), are used constantly to measure the health of vegetation, but they also assist in other analyzes, such as growth, biomass and leaf area estimation, stress detection and variation of the canopy (size) of plants (Medauar et al, 2018a;Pereira et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle for identifying the vegetative vigor and quantify the area occupied by eucalyptus sprouts after chemical weeding in the state of Bahia, Brazil

et al. 2020

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Currently, the efficiency of chemical weeding for controlling eucalyptus sprouts is measured by field sampling, but the inefficiency of the sampling methods has led to the investigation of new technologies, such as using unmanned aerial vehicle (UAV) to help to identify the vegetative vigor of eucalyptus after chemical weeding. This study, therefore, used aerial images obtained by a UAV embedded with a sensor to identify the vegetative vigor and quantify the area occupied by eucalyptus sprouts 90 days after the chemical weeding. The study was conducted in three fields planted with eucalyptus whose sprouts had been previously controlled by the chemical weeding with the Scout® herbicide in November 2016. The vegetative vigor of the eucalyptus sprouts was evaluated from the aerial images obtained by the UAV with embedded sensor, during flights conducted in November 2016 and February 2017, that were used to calculate the normalized difference vegetation index and later, a random sample grid was constructed for each image by supervised classification of the area (m2) to determine the percentage occupied by the sprouts. The used chemical control method neither eradicated the sprouts nor reduced the sprout occupied area. The normalized difference vegetation index and supervised classification tools allowed determining with high precision sprout health status and size, generating interpretable data on the different evaluated fields and periods. The processing of the images obtained by the UAV provided a viable alternative of management to evaluate sprout status in reforestation areas.

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A Lightweight Uav-Based Laser Scanning System for Forest Application

Cited by 8 publications

References 16 publications

Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

Evaluation of Multiple Linear Regression Model to Obtain DBH of Trees Using Data From a Lightweight Laser Scanning System on-Board a Uav

Using unmanned aerial vehicle for identifying the vegetative vigor and quantify the area occupied by eucalyptus sprouts after chemical weeding in the state of Bahia, Brazil

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