Influence of sample selection method and estimation technique on sample size requirements for wall-to-wall estimation of volume using airborne LiDAR

Yang, Ting-Ru; Kershaw, John A.; Weiskittel, Aaron R.; Lam, Tzeng Yih; McGarrigle, Elizabeth

doi:10.1093/forestry/cpz014

Cited by 17 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown here, estimates derived from ALS provide suitable lists of covariates for LIST and 3P. Besides the EFI estimated volume used in this study, covariates extracted directly from ALS point cloud data (e.g., LiDAR metrics) also can be used (Yang et al 2019). Similar to Yang et al's (2019) work, Parker andEvans (2004, 2009) showed that double sampling could be an effective approach to derive LiDAR estimates for forest and individual tree attributes, but they only used systematic selection.…”

Section: Discussionmentioning

confidence: 99%

Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots

Hsu

Chen

Yang

et al. 2020

Annals of Forest Science

Self Cite

View full text Add to dashboard Cite

& Key message This study presents an easy-to-apply variable probability sample design that is an efficient and costeffective method to correct for local bias in regional LiDAR-assisted forest inventory estimates. This design is especially useful for small woodlot owners. & Context Light detection and ranging (LiDAR)-derived forest inventory estimates are generally unbiased at landscape levels but may be biased locally. One solution to correct local bias is to use ground-based double sampling with ratio estimation where the LiDAR estimates form the large sample covariate and the ground plots are used to estimate a correction or calibration ratio. & Aims Our objectives were to test the performance of different sample strategies, to correct for local bias, and to determine the most efficient and cost-effective sampling design. & Methods We compared five sample selection methods and four plot types using simulation. Sample sizes and inventory costs required to achieve 5% standard error were calculated to assess sampling efficiency. & Results The results showed that bias can be corrected successfully using a doubling sampling approach with ratio estimation, and that variable probability selection methods were more efficient than equal probability selection methods. A big basal area factor (BAF) plot was the most cost-effective on-the-ground plot type. & Conclusion The most efficient and cost-effective sampling design was list sampling with big BAF plots. This combination can be used to calibrate LiDAR-derived forest inventory estimates for a variety of forest attributes.

show abstract

Section: Discussionmentioning

confidence: 99%

Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots

Hsu

Chen

Yang

et al. 2020

Annals of Forest Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the last few years, Big BAF has a steadily increasing number of research studies in the literature, for example (Chen et al, 2019;Iles, 2012;Kershaw Jr. et al, 2016b;Lei et al, 2019;McTague, 2010;Rice et al, 2014;Samiotis & Stamatellos, 2011;Yang & Burkhart, 2018;Yang, Kershaw, Weiskittel, Lam, & McGarrigle, 2019). Grosenbaugh (1963) introduced a sampling method in which trees are selected for measurement with a probability proportional to some predicted tree value (e.g.…”

Section: The Big Baf Sampling Methodsmentioning

confidence: 99%

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Georgakis¹,

Stamatellos²

2019

OJF

View full text Add to dashboard Cite

Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs' ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands' volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade.

show abstract

“…The performance of ALS-enhanced forest inventories may depend on several factors, such as the sampling design, sample size, estimator, PC parameters, and variation in the target variable within the population (Köhl et al 2006;Yang et al 2019). There are many theoretical studies concerning the estimation of the required sample size, which chiefly depends on the goal and means of analysis.…”

Section: Sampling Intensitymentioning

confidence: 99%

“…Therefore, the obtained distributions can only be considered expected results, although the entire dataset was large. The above-listed issues deserve to be further investigated in greater detail, for example, by testing non-parametric estimators such as random forest or regression trees, especially considering the notable results obtained by Yang et al (2019), who found random forest imputations to be efficient for small sample sizes (e.g., below 50). The stratification of the area according to the stand structure should also be investigated, as we have observed its potential influence, which would be perhaps more relevant if one had to evaluate more species-diversified forest districts.…”

Section: Constraintsmentioning

confidence: 99%

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

et al. 2020

View full text Add to dashboard Cite

Background Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns, and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning (ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded. Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALS-enhanced stand-level inventory. Results Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/− 5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used. Conclusions ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

show abstract

Influence of sample selection method and estimation technique on sample size requirements for wall-to-wall estimation of volume using airborne LiDAR

Cited by 17 publications

References 37 publications

Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots

Sample strategies for bias correction of regional LiDAR-assisted forest inventory Estimates on small woodlots

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Contact Info

Product

Resources

About