GoogleEarth_README.txt
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Date: 19 December 2019

Dataset creator and contact person:
Roeland C. van de Vijsel
roeland.van.de.vijsel[at]nioz.nl
NIOZ Royal Netherlands Institute for Sea Research (www.nioz.nl)
Yerseke, The Netherlands

This data belongs to the dataset that can be downloaded from the NIOZ Data Repository (https://dataverse.nioz.nl) and/or can be found as DOI: 10.25850/nioz/7b.b.m (https://doi.org/10.25850/nioz/7b.b.m). 

This dataset is part of the following publication:
van de Vijsel, R.C., van Belzen, J., Bouma, T.J., van der Wal, D., Cusseddu, V., Purkis, S.J., Rietkerk, M. & van de Koppel, J. (2019). Estuarine biofilm patterns: Modern analogues for Precambrian self-organization. Earth Surface Processes and Landforms. DOI: 10.1002/esp.4783 (https://doi.org/10.1002/esp.4783).

Whenever any of these data (scripts, figures, raw and processed data) are used elsewhere, please refer correctly to this publication and its authors and please cite the dataset. Options for citing the dataset can be found on https://dataverse.nioz.nl. Dataset and publication published under the Creative Commons Attribution License.

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LoadGoogleEarthImages.m
MATLAB script that:
-loads Google Earth images (obtained with Google Earth Pro, (c) 2016 Google Inc.) from folder “input”
-selects the 44x49.5 focus plot and georeferences it (i.e. register to the orthogonal DEMs)
-saves output in folder “output”

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output
This folder contains:

YYYYRegistered.jpg (.png, .pdf)
The selected 44x49.5m focus area, cropped from the Google Earth aerial images in “input” and georeferenced with the Matlab script “LoadGoogleEarthImages.m”.

VDVIYYYY.txt
The selected 44x49.5m focus area, converted to (absolute) VDVI-values with the Matlab script.

VDVI_TrendYYYY.txt
The background profile (the “spatial trend”): a 4th-order, two-dimensional polynomial was fitted through the absolute VDVI data to calculate this background profile.

VDVIdetYYYY.txt
The “spatially detrended” VDVI-profile, i.e. absolute minus background profile. This is done to retain the local anomalies in greenness (“relative VDVI”).

VDVIdetFillMissingYYYY.txt
As VDVIdetYYYY.txt, but the (very few) NaN’s in this matrix were filled in (with their nearest non-NaN value).

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Please see the explanations in the Matlab script for more info.

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