Efficient sampling for ecosystem service supply assessment at a landscape scale

Ancin-Murguzer, Franfisco Javier; Muñoz, Lorena; Monz, Christopher; Fauchald, Per; Hausner, Vera Helene

doi:10.1080/26395908.2018.1541329

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…Additionally, determining the optimal amount of pixels to sample within an image may need further consideration. Here, we used 50 pixels, though recent studies suggest 20–30 pixels per image is adequate to estimate vegetation at a landscape scale when multiple images are acquired (Ancin‐Murguzur et al ). Finally, although using BAS points in a buffer around a well pad as a reference site improves spatial coverage and statistical analyses compared to a single LPI transect, reclaimed areas in this study are limited to being compared against their own unique reference site, which may limit efficiency.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…This study used a 6 x 6 grid to select a total of 36 pixels, though more or fewer pixels per image can be classified depending on the desired measurement precision for classification. A recent study found 20-30 pixels per image is adequate for sampling large areas 31 .…”

Section: In Samplepoint Click Options | Database Wizard |mentioning

confidence: 99%

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Curran

Hodza

Cox

et al. 2020

JoVE

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Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the 'travelling salesperson problem' (TSP) to increase time and cost efficiency. While UAS images can be acquired 2-3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided.

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“…Also, ecological data is often subjected to further process (e.g. data input and classification, and image analysis for pixel data (Booth and Cox 2008, Ancin‐Murguzur et al 2019)) to utilize as input data in various research questions. These factors may reduce the available sampling time.…”

Section: Discussionmentioning

confidence: 99%

Developing generalized sampling schemes with known error properties: the case of a moving observer

Takashina

Economo

2020

Ecography

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Pattern in space and time is central to ecology, and adequately designed ecological sampling is needed to resolve those patterns, pursue ecological questions and design conservation strategies. Recently, there has been an explosion of various ecological data due to the proliferation of online data‐sharing platforms, citizen science programs and new technology such as unmanned aerial vehicles (UAVs), but data reliability, consistency and the error properties of the sampling method are usually uncertain. While there are a number of standard survey protocols for different taxa, they often subjectively designed and standardization is meant to facilitate repeatability rather than produce a quantitative evaluation of the data (e.g. error properties). Here, we describe an ecological survey scheme consisting of an ‘algorithm' to be followed in the field that will result in a standard set of data as well as the error properties of the data. While many such sampling schemes could be developed that target different types of organisms, we focus on one case of a moving observer attempting to detect a species in the field (e.g. a birder, UAV, etc.) with the goal of producing a presence–absence map. The multiscale model developed is spatially explicit and accommodates inherent survey tradeoffs such as sampling speed, detectability and map resolution. Given a set of sampling parameters, the model provides estimates of the total sampling time and map accuracy translated into the probability of false negative. Additionally it also provides an actual and sampled occupancy–area curve across mapping resolutions that can be utilized to discuss sampling effects. While the proposed sampling framework is simple, the same general approach could be adapted for other conditions to meet the needs of a particular taxon. If a set of ‘canonical' sampling algorithms could be developed with known mathematical properties, it would enhance reliability and usage of ecological datasets.

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Efficient sampling for ecosystem service supply assessment at a landscape scale

Cited by 11 publications

References 28 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

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Developing generalized sampling schemes with known error properties: the case of a moving observer

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