Spatial and spectral analysis of fairy circles in Namibia on a landscape scale using satellite image processing and machine learning analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00367078" target="_blank" >RIV/68407700:21110/23:00367078 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.jag.2023.103377" target="_blank" >https://doi.org/10.1016/j.jag.2023.103377</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jag.2023.103377" target="_blank" >10.1016/j.jag.2023.103377</a>
Alternative languages
Result language
angličtina
Original language name
Spatial and spectral analysis of fairy circles in Namibia on a landscape scale using satellite image processing and machine learning analysis
Original language description
Fairy circles (FCs) are a unique phenomenon characterized by circular patches, 4-10 m in diameter, of bare soil within a vegetated matrix. This project aimed to study the spatial and spectral characteristics of FCs on a landscape scale in Namibia. The specific objectives of this research are (1) processing satellite observations to explore the FCs distributions by applying statistical analysis and deep machine learning algorithms; (2) analyzing the FCs' geometric attributes to retrieve their spatial patterns regarding topographic features nearby. The FCs were classified within 25 km2 by processing 15 input layers through a convolutional neural network (CNN) model. The layers include four WorldView2 spectral bands, derived vegetation, biocrust, and mineral indices, and textural characteristics. The FCs' geometry was extracted, and spatial autocorrelation was performed. By labeling 1600 FCs and using the CNN model, 14,536 FCs were mapped with 0.97% accuracy and a binary cross -entropy loss function value of only 0.01. Field measurements and laboratory analysis justified the need to use spectral indices for the model. Unique elongated FCs, clustered by hotspot analysis, were quantified and mapped along watercourses in alluvial fans with notable connectivity. On a landscape scale that has not yet been studied, spatial and spectral analyses became possible only with valuable remote sensing retrievals, deep statistical analysis, and machine learning algorithms.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10500 - Earth and related environmental sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Applied Earth Observation and Geoinformation
ISSN
1569-8432
e-ISSN
1872-826X
Volume of the periodical
121
Issue of the periodical within the volume
103377
Country of publishing house
AT - AUSTRIA
Number of pages
14
Pages from-to
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UT code for WoS article
001015894400001
EID of the result in the Scopus database
2-s2.0-85163511428