10.5061/DRYAD.5D19R
Mander, Luke
Universidade Aberta
Dekker, Stefan C.
Utrecht University
Li, Mao
Florida State University
Mio, Washington
Florida State University
Punyasena, Surangi W.
University of Illinois System
Lenton, Timothy M.
University of Exeter
Data from: A morphometric analysis of vegetation patterns in dryland ecosystems
Dryad
dataset
2017
computational vision
vegetation patterns
2017-01-20T18:37:28Z
2017-01-20T18:37:28Z
en
https://doi.org/10.1098/rsos.160443
829442 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Vegetation in dryland ecosystems often forms remarkable spatial patterns.
These range from regular bands of vegetation alternating with bare ground,
to vegetated spots and labyrinths, to regular gaps of bare ground within
an otherwise continuous expanse of vegetation. It has been suggested that
spotted vegetation patterns could indicate that collapse into a bare
ground state is imminent, and the morphology of spatial vegetation
patterns, therefore, represents a potentially valuable source of
information on the proximity of regime shifts in dryland ecosystems. In
this paper, we have developed quantitative methods to characterize the
morphology of spatial patterns in dryland vegetation. Our approach is
based on algorithmic techniques that have been used to classify pollen
grains on the basis of textural patterning, and involves constructing
feature vectors to quantify the shapes formed by vegetation patterns. We
have analysed images of patterned vegetation produced by a computational
model and a small set of satellite images from South Kordofan (South
Sudan), which illustrates that our methods are applicable to both
simulated and real-world data. Our approach provides a means of
quantifying patterns that are frequently described using qualitative
terminology, and could be used to classify vegetation patterns in
large-scale satellite surveys of dryland ecosystems.
Model images of patterned vegetationThis zip file contains a dataset of
images of patterned vegetation produced by a computational model. These
images are: (1) raw model output images, (2) images that have been cropped
to 50x50 pixels, and (3) binary images derived from these cropped images.
These images are all in .tif format. The images show patterns produced at
0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3 and 1.4 mm per day.
South Sudan
South Kordofan