The 51- and 103-km window sizes were used to generate 36 and 14 data points, respectively, that enabled the production of depth contour maps.
A smoothed terrain was obtained by applying the Gaussian filter to the 1″ × 1″ grid using an 18-km window size.
In the recent paper by Abraham et al. ([2014]) 'Spectral analysis of aeromagnetic data for geothermal energy investigation of Ikogosi Warm Spring - Ekiti State, southwestern Nigeria', 22 randomly selected blocks of dimension 55 × 55 km (data window) were utilized for CPD evaluation using spectral centroid method.
The highest annual increase rate for a 5-km moving window was about 6.8%.
This is demonstrated for all three radars by comparing the debris flux versus diameter over 200 km altitude windows for 3 nonconsecutive years from 1998 to 2003.
The validation was conducted at four spatial length scales from 25too 150 km using windows to represent potential dust source areas centered on and positioned around eight meteorological stations within the study area.
We applied the code along 2-km moving windows, which are sufficiently long to capture multiple streamlined landforms when applied across flow, yet small enough to capture roughness variability within each of the survey patches.
The 55-km dimension windows will neither be adequate to analyze 11.48- nor 21.91-km 21.91-kmths as reported basebraham et al. ([2014]).
Neighbourhood statistics of local mean, local standard deviation and local coefficient of variation were calculated using window sizes of 3 km × 3 km, 6 km × 6 km, 9 km × 9 km, 12 km × 12 km, 24 km × 24 km and 48 km × 48 km and visualized using GIS mapping techniques.
We developed a resampling algorithm that uses the raw data set (30-m NLCD-2001), calculathe the areas of all the LCLU classes within each coarse 1-km or 3-km grid cell (filter window), and assigns the dominant LCLU class to that coarse grid cell in a GIS, as demonstrated in Figure 2 for the city of Atlanta.
