An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests: a case study using the Nova Scotia permanent sample plot network

Kershaw, John A.; Weiskittel, Aaron R.; Lavigne, M. B.; McGarrigle, Elizabeth

doi:10.1186/s40663-017-0102-2

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Subsequently, stand volume was determined by extrapolating the mean plot-level volume to the total area (Equation ( 14)). In addition, the variance (Equation ( 15)), standard error of the mean (Equation ( 16)), sampling error (Equations ( 17)), and the confidence intervals (Equation ( 18) and ( 19)) were determined following the simple random sampling (SRS) protocol [38,39].…”

Section: Sampling Forest Inventory (Sfi)mentioning

confidence: 99%

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Corte

Neto

Rex

et al. 2022

Drones

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Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

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Section: Sampling Forest Inventory (Sfi)mentioning

confidence: 99%

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Corte

Neto

Rex

et al. 2022

Drones

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“…Within the forestry literature, we believe this is the first application to employ an autoregressive model (to account for correlation between successive segments), and also to address a zero-inflated distribution (many segments are empty, with no DWD intersections). Copula models are relatively new in the forestry literature; they have been used to describe relationships between continuous variables such as tree diameter and height (Wang et al 2008(Wang et al , 2010MacPhee et al 2017), to inform spatially-explicit stand simulations (Kershaw et al 2010), and for imputation-based growth and yield models (Kershaw et al 2017). Eskelson et al (2011) and Fortin et al (2013) extended copula models in forestry to account for spatial autocorrelation.…”

Section: Simulation Of Sampling Variabilitymentioning

confidence: 99%

Influence of transect length and downed woody debris abundance on precision of the line-intersect sampling method

et al. 2018

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Background: Accurate downed woody debris (DWD) volume or mass estimates are needed for numerous applications such as fuel loading, forest carbon, and biodiversity/habitat assessments. The line-intersect sampling (LIS) method of inventorying DWD is widely used in forest inventories and ecological studies because it is timeefficient and unbiased. Despite its widespread use, the appropriate transect length needed to achieve a desired precision at a particular location has received relatively little attention. Methods: We conducted intensive LIS sampling at 33 locations representing eight mature or old-growth forest types in northeastern USA, providing a range of forest conditions and DWD volumes (from 17 to 323 m 3 •ha − 1). We used these empirical field data to test, through simulations, the effect of increasing transect length (up to 340 m at each location) on precision of associated LIS volume estimates. Importantly, we used a novel application of copula models to account for within-transect spatial autocorrelation of DWD volumes during our simulations, thereby properly addressing variance estimates. Results: As expected, precision consistently improved with increasing cumulative transect length, and locations with lower DWD volumes required longer transects to achieve a given level of precision. We developed models relating precision, transect length, and DWD volume that allows us to gauge a suitable LIS transect length for desired precision levels. Conclusions: LIS provides an attractive method for estimating DWD volume for a given localized area of interest. For the forest types sampled here, and for the particular copula model framework employed, transect lengths of ca. 120 m provide a reasonable level of precision, ranging from 18% to 60% coefficients of variation.

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Development in Copula Applications in Forestry and Environmental Sciences

Bhatti

Quang

2020

Forum for Interdisciplinary Mathematics

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An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests: a case study using the Nova Scotia permanent sample plot network

Abstract: Background: A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system.

Cited by 6 publications

References 34 publications

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Influence of transect length and downed woody debris abundance on precision of the line-intersect sampling method

Development in Copula Applications in Forestry and Environmental Sciences

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