Estimación del área basimétrica en masas maduras de Pinus sylvestris en base a una única medición del escáner láser terrestre (TLS)

Molina-Valero, Juan Alberto; Villamayor, María José Ginzo; Pérez, Manuel Antonio Novo; González, Juan Gabriel Álvarez; Pérez-Cruzado, César

doi:10.31167/csecfv0i45.19887

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Regarding the error obtained in each parameter, the combination of scans 0-5-6-7-8 for AGB obtained an error of 1.61 t•ha −1 , which was below the one presented by other authors [10] who used the same methodology, but in tropical environments. For BA (scan combination 0-5-6-7-8) the error obtained was 5.35 m 2 •ha −1 , improving those obtained by Molina-Valero et al [47], with an RMSE of 8.57 m 2 •ha −1 for pure plots of P. sylvestris L. with a radius of 7 m and performing a single TLS measurement from the plot center. The error obtained in the estimation of the CBH with the combination of three scans (5-0-7) was 0.83 m lower than 1.95 m obtained by Giannetti et al [48] in the CBH study performed on a set of individual trees of Cupressus sempervirens L. and P. pinaster with eight scanner positions.…”

Section: Discussionsupporting

confidence: 57%

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

Torralba¹,

Carbonell-Rivera²,

Ruiz³

et al. 2022

Forests

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In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.

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Section: Discussionsupporting

confidence: 57%

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

Torralba¹,

Carbonell-Rivera²,

Ruiz³

et al. 2022

Forests

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“…Strahler et al [91] obtained an error of 15.49% in the estimation of basal area in a P. ponderosa stand in New South Wales (Australia) with a single scan; in a study with 30 scan plots installed in six New England hardwood and conifer forest stands, Yao et al [92] explained 66% of the observed variability in G, with an RMSE% value of 15.24% and 85% of the variability observed in W, with an RMSE% of 10.69%; Skowronski et al [51] reported RMSE% values for CBD and CFL estimates of 18.48% and 15.68%, respectively, in wildfire-prone stands in the New Jersey Pinelands; Torralba et al [82] explained 70% of the observed variability in W with an RMSE% of 21% in Mediterranean forests using TLS-metrics obtained with a single scan similar to those used in this study; in a study of 40 plots in mature P. sylvestris forests in Spain, Molina-Valero et al [93] estimated G from a single scan with an RMSE% of 21.84%, and explained 75% of the observed variability.…”

Section: Models Based On Tls-derived Metricsmentioning

confidence: 65%

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

et al. 2021

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In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

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“…This is done by the normalize function ( Figure 1), which obtains coordinates relative to the plot centre and the digital terrain model. This function also applies the point cropping process as a criterion for reducing point density homogeneously in space and proportional to object size [10].…”

Section: Detection Of Trees and Estimation Of Dbhmentioning

confidence: 99%

FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories

Molina-Valero

Ginzo‐Villamayor

Pérez³

et al. 2020

The 1st International Electronic Conference on Forests—Forests for a Better Future: Sustainability, Innovation, Inter

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Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their diameter at breast height (dbh), (ii) estimation of some stand variables (e.g., density, basal area, mean, and dominant height), (iii) computation of metrics related to important tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches.

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Estimación del área basimétrica en masas maduras de Pinus sylvestris en base a una única medición del escáner láser terrestre (TLS)

Cited by 5 publications

References 18 publications

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories

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