Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.

Simultaneous continuous recording of accommodation and pupil size using the modified Shin-Nippon SRW-5000 autorefractor

The internal optics of the recent models of the Shin-Nippon SRW-5000 autorefractor (also marketed as the Grand Seiko WV-500) have been modified by the manufacturer so that the infrared measurement ring has been replaced by pairs of horizontal and vertical infrared bars, on either side of fixation. The binocular, open field-of-view, allowing the accommodative state to be objectively monitored while a natural environment is viewed, has made the SRW-5000 a valuable tool in further understanding the nature of the oculomotor response. It is shown that the root-mean-square of model eye measures was least (0.017 ± 0.002D) when the separation of the horizontal measurement bars were averaged twice. The separation of the horizontal bars changes by 3.59 pixels/dioptre (r2 = 0.99), allowing continuous on-line analysis of the refractive state at up to 60 Hz temporal resolution to an accuracy of <0.001D, with pupils >3 mm. The pupil edge is not obscured in the diagonal axis by the measurement bars, unlike the ring of the original optics, so in the newer model pupil size can be measured simultaneously at the same rate with a resolution of <0.001 mm. The measurements of accommodation and pupil size are relatively unaffected by eccentricity of viewing up to ±10° from the visual axis and instrument focusing inaccuracies over a range of 10 mm towards the eye and 5 mm away from the eye. The resolution and temporal properties of the analysis are therefore ideal for the simultaneous measurement of dynamic accommodation and pupil responses. © 2004 The College of Optometrists.

Continuous recording of accommodation and pupil size using the Shin-Nippon SRW-5000 autorefractor

A newly released commercial autorefractor, the Shin-Nippon SRW-5000 (Japan), has been found to be valid compared to subjective refraction and repeatable over a wide prescription range. Its binocular open field-of-view allows the accommodative state to be monitored while a natural environment is viewed. In conventional static mode, the device can take up to 45 readings in 1min using digital image analysis of the reflected retinal image of a measurement ring. Continuous on-line analysis of the ring provides high (up to 60Hz) temporal resolution of the refractive state to an accuracy of <0.001D. Pupil size can also be analysed to a resolution of <0.001mm. The measurement of accommodation and pupil size was relatively unaffected by eccentricity of viewing up to ±10° and instrument focusing inaccuracies of ±5mm. The resolution properties of the analysis are shown to be ideal for measurement of dynamic accommodation and pupil responses. Copyright © 2001 The College of Optometrists.

Accuracy and optimal sampling in Monte Carlo solution of population balance equations

Implementation of a Monte Carlo simulation for the solution of population balance equations (PBEs) requires choice of initial sample number (N0), number of replicates (M), and number of bins for probability distribution reconstruction (n). It is found that Squared Hellinger Distance, H2, is a useful measurement of the accuracy of Monte Carlo (MC) simulation, and can be related directly to N0, M, and n. Asymptotic approximations of H2 are deduced and tested for both one-dimensional (1-D) and 2-D PBEs with coalescence. The central processing unit (CPU) cost, C, is found in a power-law relationship, C= aMNb0, with the CPU cost index, b, indicating the weighting of N0 in the total CPU cost. n must be chosen to balance accuracy and resolution. For fixed n, M × N0 determines the accuracy of MC prediction; if b > 1, then the optimal solution strategy uses multiple replications and small sample size. Conversely, if 0 < b < 1, one replicate and a large initial sample size is preferred. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2394–2402, 2015