A modification of balanced acceptance sampling

Robertson, B. L.; McDonald, Trent L.; Price, C. J.; Brown, Jennifer

doi:10.1016/j.spl.2017.05.004

Cited by 24 publications

(15 citation statements)

References 8 publications

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“…In addition to reducing costs compared to traditional LPI methods, Cagney et al () highlighted that images provide a permanent record, which can be reevaluated if data are questionable, if software improves, or if management objectives change. Here, we add a spatially balanced sampling design, called balanced acceptance sampling (BAS) (Robertson et al , ), along with geo‐tagged imagery, to sample reclaimed natural gas well pads and reference sites. BAS is a representative sample of the population from which it is drawn (Robertson et al ).…”

Section: Introductionmentioning

confidence: 99%

Spatially balanced sampling and ground‐level imagery for vegetation monitoring on reclaimed well pads

Curran

Cox

Robinson

et al. 2019

Restoration Ecology

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Land reclamation associated with natural gas development has become increasingly important to mitigate land surface disturbance in western North America. Since well pads occur on sites with multiple land use and ownership, the progress and outcomes of these efforts are of interest to multiple stakeholders including industry, practitioners and consultants, regulatory agents, private landowners, and the scientific community. Reclamation success criteria often vary within, and among, government agencies and across land ownership type. Typically, reclamation success of a well pad is judged by comparing vegetation cover from a single transect on the pad to a single transect in an adjacent reference site and data are collected by a large number of technicians with various field monitoring skills. We utilized “SamplePoint” image analysis software and a spatially balanced sampling design, called balanced acceptance sampling, to demonstrate how spatially explicit quantitative data can be used to determine if sites are meeting various reclamation success criteria and used chi‐square tests to show how sites in vegetation percent cover differ from a statistical standpoint. This method collects field data faster than traditional methods. We demonstrate how quantitative and spatially explicit data can be utilized by multiple stakeholders, how it can improve upon current reference site selection, how it can satisfy reclamation monitoring requirements for multiple regulatory agencies, how it may help improve future seed mix selection, and discuss how it may reduce costs for operations responsible for reclamation and how it may reduce observer bias.

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Section: Introductionmentioning

confidence: 99%

Spatially balanced sampling and ground‐level imagery for vegetation monitoring on reclaimed well pads

Curran

Cox

Robinson

et al. 2019

Restoration Ecology

View full text Add to dashboard Cite

show abstract

“…The i th coordinate of each point in the sequence has an associated base

b_{i}

, with

b_{1} = 2

and

b_{2} = 3

. The i th coordinate of the k th point in this sequence is (Robertson, McDonald, Price, & Brown, )

x_{k}^{(i)} = \sum_{j = 0}^{\infty} \{(⌋⌊, \frac{u_{i} + k}{b_{i}^{j}}) mod b_{i}\} \frac{1}{b_{i}^{j + 1}},

where

u_{i}

is a random non‐negative integer and ⌊ x ⌋ is the floor function — the largest integer that is less than or equal to x . For example, the first coordinate of the second point with

u_{1} = 1

and

b_{1} = 2

\begin{matrix} x_{2}^{false(1 false)} & = ((⌋⌊, \frac{3}{1}) mod 2) \frac{1}{2} + ((⌋⌊, \frac{3}{2}) mod 2) \frac{1}{4} + ((⌋⌊, \frac{3}{4}) mod 2) \frac{1}{8} \\ = \frac{1}{2} + \frac{1}{4} + 0 \\ = \frac{3}{4} . \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…If

x_{1}

is not in the study area, new sequences are considered until one with

x_{1}

in the study area is found (Robertson et al., ). Starting from

x_{1}

, the first n scaled points in the study area define the BAS sample (Robertson et al., ). The BAS points are kept in the same order as they appear in Equation and will have good spatial spread over the study area.…”

Section: Methodsmentioning

confidence: 99%

“…The ith coordinate of each point in the sequence has an associated base b i , with b 1 = 2 and b 2 = 3. The ith coordinate of the kth point in this sequence is (Robertson, McDonald, Price, & Brown, 2017)…”

Section: Point Selectionmentioning

confidence: 99%

“…The points from Equation 1 are then scaled to a minimal bounding box enclosing the study area. If x 1 is not in the study area, new sequences are considered until one with x 1 in the study area is found (Robertson et al, 2017). Starting from x 1 , the first n scaled points in the study area define the BAS sample (Robertson et al, 2017).…”

Section: Point Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Using balanced acceptance sampling as a master sample for environmental surveys

Dam‐Bates¹,

Gansell²,

Robertson

2018

Methods Ecol Evol

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Well‐designed environmental monitoring programmes for management organisations are important for evidence‐based decision making. However, many environmental problems are not single agency issues that require intervention or monitoring at one spatial scale. A master sample can be used to coordinate and scale monitoring designs to ensure consistency in information gathered and robustness of estimators at the different spatial scales. We propose using balanced acceptance sampling (BAS) to generate a master sample. In this context, practical applications and justification of BAS as a master sample are addressed. These include sample generation, stratification, unequal probability sampling, rotating panel designs, and regional intensification. A method for incorporating legacy sites is also provided. Using BAS as a master sample is conceptually simple, gives good spatial balance over different spatial scales, and is computationally efficient to generate. An example for terrestrial biodiversity monitoring in New Zealand is provided. Environmental monitoring can benefit from increased coordination between agencies. A master sample is an excellent way to incorporate coordination directly into the sample design. BAS improves on methods previously described and provides an effective method to monitor populations at multiple spatial scales.

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Achieving spatial balance in environmental surveys under constant inclusion probabilities or inclusion density functions

Di Biase,

Marcheselli,

Pisani

2024

Environmetrics

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In environmental and ecological surveys, well spread samples can be easily obtained via widely adopted tessellation schemes, which yield equal first‐order inclusion probabilities in the case of finite populations of areas or constant inclusion density functions in the case of continuous populations. In the literature, many alternative schemes that are explicitly tailored to select well spread samples have been proposed, but owing to their complexity, their use should be preferred only if they allow us to achieve a valuable gain in precision with respect to the tessellation schemes. Therefore, by means of an extensive simulation study, the performances of tessellation schemes and several specifically tailored schemes are compared under constant first‐order inclusion probabilities or constant inclusion density functions.

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A modification of balanced acceptance sampling

Abstract: This article presents a modification of balanced acceptance sampling (BAS) that causes inclusion probabilities to better approximate targeted inclusion probabilities. A new sample frame constructor for BAS is also introduced from which equi-probable spatially balanced samples are drawn.

Cited by 24 publications

References 8 publications

Spatially balanced sampling and ground‐level imagery for vegetation monitoring on reclaimed well pads

Spatially balanced sampling and ground‐level imagery for vegetation monitoring on reclaimed well pads

Using balanced acceptance sampling as a master sample for environmental surveys

Achieving spatial balance in environmental surveys under constant inclusion probabilities or inclusion density functions

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