Ai Feedback
Exact(4)
We measured spatial patterns in floodplain topography for pool 9 of the Upper Mississippi River, USA, using moving window analyses of eight surface metrics applied to a 1 × 1 m2 DEM over multiple scales.
The method involves a series of moving window analyses and union and intersection operations which determine the edge width, connectivity and holes of data in a binary forest/non forest image [52, 53].
The surface character and variability of each floodplain were measured using four surface metrics; namely, standard deviation, skewness, coefficient of variation, and standard deviation of curvature from a series of moving window analyses ranging from 50 to 1000 m in radius.
Using program FRAGSTATS, version 3.3 (McGarigal and Marks 1995), we performed moving window analyses using both 60-m and 120-m circular windows around each research plot (corresponding to an area of 1.89 and 6.21 ha, respectively, see McGarigal and Marks 1995).
Similar(56)
Maps showing the level of provision of ES were analysed using a moving window to analyse scale dependence in the spatial distribution of ES provision.
c Exposure time-line analysed using a 60-minute moving window at one second increments.
The temporal variability and the uncertainty of the rating curve and its parameters were analysed through a Monte Carlo (MC) analysis on a moving window of data using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology.
Here, we analyse overlapping segments selected using a 500 sec moving window, shifted 10 sec at a time.
As an alternative, a moving window technique is applied.
where w is the moving window.
(4) Moving Window Integration.
Related(1)
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com