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

Machado, Mônica Valéria dos Santos; Tommaselli, Antônio Maria Garcia; Tachibana, Vilma Mayumi; Martins-Neto, Rorai P.; Campos, Mariana

doi:10.5194/isprs-archives-xlii-2-w13-449-2019

Cited by 3 publications

(2 citation statements)

References 13 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%

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%

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

Torresan

Carotenuto

Chiavetta

et al. 2020

Drones

View full text Add to dashboard Cite

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“…Multiple linear regression is a statistical method used to predict response values and find the relationship between several independent (or exploratory) variables and the dependent (response) variables [17]. MLR is extensively applied in environmental and forest modeling to estimate dependent variables that are difficult to obtain with direct measurements, such as volume, biomass, and crown diameter, from independent variables such as total height, DBH, and their respective transformations [18,19].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

et al. 2023

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Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species.

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UAV-g 2019: Unmanned Aerial Vehicles in Geomatics

Nex¹

2019

Drones

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Unmanned aerial vehicle in geomatics (UAV-g) is a well-established scientific event dedicated to UAVs in geomatics and remote sensing. In the different editions of the journal, new scientific challenges have increased their synergy with adjacent domains, such as robotics and computer vision, thereby increasing the impact of this conference. The 2019 edition has been hosted by the University of Twente (The Netherlands) and has attracted about 300 participants for the full three-day program. Researchers from 36 different countries (from all continents) have presented 89 accepted papers in 17 oral and 2 poster sessions. The presented papers covered multi-disciplinary topics, such as photogrammetry, natural resources monitoring, autonomous navigation, and deep learning. All these contributions have in common the use of UAV platforms for the innovative acquisition and processing of the acquired data and information extracted from the surrounding environment.

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

Cited by 3 publications

References 13 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

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

UAV-g 2019: Unmanned Aerial Vehicles in Geomatics

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