A Comparison of Two Herbaceous Cover Sampling Methods to Assess Ecosystem Services in High-Shrub Rangelands: Photography-Based Grid Point Intercept (GPI) Versus Quadrat Sampling

Hulvey, Kristin B.; Thomas, Katherine K.; Thacker, Eric

doi:10.1016/j.rala.2018.08.004

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…Both height and canopy gap may be metrics which need only be monitored when the reclaimed area is nearing bond release. It has also been reported that image analysis may result in difficulties capturing vegetation under dense canopy cover (Hulvey et al ). Our study areas were not prone to this issue and it is unlikely for reclaimed sites in semiarid sagebrush‐steppe ecosystems to have dense canopy cover during the interim reclamation phase.…”

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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show abstract

“…To investigate the accurate CC estimation using the RGB-based sensor, which is equivalent to CC estimation using NDVI, a pixel-wise classification method was implemented; this is presented in Figure 4. As found in the literature, pixel classification methods are considered highly accurate for separating the canopy and non-canopy classes, and they are mainly used to calibrate RGB-based methods [35,38,45]. A pixel classification method based on K-means clustering was used to compare the RGB-based methods that use vegetation indices to separate canopy areas from non-canopy areas.…”

Section: Ndvi = mentioning

confidence: 99%

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

et al. 2019

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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

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“…The plant species were selected based on their medicinal and cultural potential mentioned by informants, citation frequency, occurrence in alpine meadows, and the palatability as described by local inhabitants ( Table 1 ). In each 1 x 1 m subplot, the percent cover for each plant species was estimated to the nearest 10% for each species rooted inside the plot (1 x 1 m%%, 10cm grid points) using the Daubenmire method [ 57 ]. Implementing this method, we measured the cover; defined as vertical projections of vegetation that include the area of a quadrat.…”

Section: Methodsmentioning

confidence: 99%

The response of culturally important plants to experimental warming and clipping in Pakistan Himalayas

et al. 2021

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The relative effects of climate warming with grazing on medicinally important plants are not fully understood in Hindukush-Himalaya (HKH) region. Therefore, we combined the indigenous knowledge about culturally important therapeutic plants and climate change with experimental warming (open-top chambers) and manual clipping (simulated grazing effect) and compared the relative difference on aboveground biomass and percent cover of plant species at five alpine meadow sites on an elevation gradient (4696 m-3346 m) from 2016–2018. Experimental warming increased biomass and percent cover throughout the experiment. However, the interactive treatment effect (warming x clipping) was significant on biomass but not on percent cover. These responses were taxa specific. Warming induced an increase of 1 ± 0.6% in Bistorta officinalis percent cover while for Poa alpina it was 18.7 ± 4.9%. Contrastingly, clipping had a marginally significant effect in reducing the biomass and cover of all plant species. Clipping treatment reduced vegetation cover & biomass by 2.3% and 6.26%, respectively, but that was not significant due to the high variability among taxa response at different sites. It was found that clipping decreased the effects of warming in interactive plots. Thus, warming may increase the availability of therapeutic plants for indigenous people while overgrazing would have deteriorating effects locally. The findings of this research illustrate that vegetation sensitivity to warming and overgrazing is likely to affect man–environment relationships, and traditional knowledge on a regional scale.

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A Comparison of Two Herbaceous Cover Sampling Methods to Assess Ecosystem Services in High-Shrub Rangelands: Photography-Based Grid Point Intercept (GPI) Versus Quadrat Sampling

Cited by 12 publications

References 13 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

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

The response of culturally important plants to experimental warming and clipping in Pakistan Himalayas

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