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

Abstract

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.

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

“…This can be done on a handwritten sheet or on a digital form to aid in photo identification later. It may be beneficial to include species that are likely to be expected in the area in the inventory even if they are not observed in the field (e.g., species within reclamation seed mixes) 18 .…”

“…The height of the monopod coupled with the camera specifications will determine the ground sample distance (GSD). In this study, a 12.1-megapixel camera was used and the monopod was set at a constant 1.3 m above the ground to obtain Nadir images at ~0.3 mm GSD 18 .…”

“…These methods are often applied based on convenience rather than stochastically, with observers choosing where they collect data. Additionally, reported and actual sample locations frequently differ, causing confusion for land managers and other stakeholders reliant upon vegetation monitoring data 18 . Recent research has demonstrated that image-based vegetation monitoring is time-and cost-effective 6 , 19 , 20 .…”

Use of 3-Dimensional Videography as a Non-Lethal Way to Improve Visual Insect Sampling

“…In this instance, the group of observers could potentially look at the video together and determine errors (e.g., why observer 1 always seemed to have higher counts than others and observers 3, 6 and 7 seemed to have lower counts than others). Other studies have shown that the use of cameras recording individuals collecting insects have been useful to correct observer bias [36] and that images provide permanent records which can be reviewed by multiple observers and stakeholder groups in vegetation surveys [37]. While our study was limited to insects, the technique presented could also be used as a non-lethal sampling strategy to study behavior in other animals (e.g., monitoring migratory bird behavior at stopover grounds).…”

Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images

“…The log ratio difference and singular decomposition were combined using neighbor constraints. Finally, residual U-Net was applied to detect the changes [22,23].…”