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Dispersion of Nanoparticles in Polymers
Published in Frank Abdi, Mohit Garg, Characterization of Nanocomposites, 2017
Ambrose C. Taylor, David J. Bray
Perhaps the simplest statistical approach that is typically used is the quadrat method [105]. The method originates from geographical methods developed during the Second World War to quantify crop production [103]. The method splits a micrograph into an array of equal-sized rectangular regions, i.e., quadrats. The quadrat size is chosen to be equal to the scale of the smallest object of interest. The number of, or the area occupied by, the objects of interest within each quadrat is determined. This is repeated for conglomerates of quadrats, which may be square areas or lines of neighbouring quadrats along orthogonal axes. The coefficient of variation of particle number is compared against the quadrat size. Deviations away from 1 (which is expected if particles are randomly dispersed) at given quadrat sizes indicate the presence of structure at particular length scales. This method is well known to be influenced by the placement and size increment of the quadrats [106], and hence is not ideal for characterising dispersion.
The hydrological controls on vegetation dynamics and wildlife in the mayas wetlands of the Dinder National Park
Published in Khalid Elnour Ali Hassaballah, Land Degradation in the Dinder and Rahad Basins, 2021
In this study, a systematic-random quadrat (SRQ) method was used by a Quadrate (40 cm2) for collecting information regarding flora inside the mayas through four transects. The position of the first quadrat was chosen randomly which automatically determined the positions of all other quadrats in the sampling (25 m spacing). For instance, a quadrat is placed on the ground at random to count the vegetation within the sample. This was done to identify a) the type and category of maya (productive or not, young, mature, old), and b) the type and status of flora including distribution and density.
Estimating methane emissions using vegetation mapping in the taiga–tundra boundary of a north-eastern Siberian lowland
Published in Tellus B: Chemical and Physical Meteorology, 2019
T. Morozumi, R. Shingubara, R. Suzuki, H. Kobayashi, S. Tei, S. Takano, R. Fan, M. Liang, T. C. Maximov, A. Sugimoto
The spatial distribution of plant species was investigated at the 231 points sampled for relative elevation at site K on 14 July 2013. During sampling, a quadrat (0.5 × 0.5 m) was placed on fairly homogeneous and wide patches of vegetation, and dominant and rare plant species – including trees, shrubs, grasses, forbs and mosses – were recorded. Moss species were categorised into five groups (green-moss, green-moss/Sphagnum-dry mixed, Sphagnum-dry, Sphagnum-wet and moss-wet) according to the soil moisture condition, and contained several typical species and/or genera (Table S1). Close-range aerial photographs were taken by a commercially available radio-controlled unmanned aerial vehicle to confirm the extent of vegetation patches and to aid in training pixel selection (AR Drone 2.0, Parrot, Paris, France).