Remote sensing based mapping of Tillandsia fields: a semi-automatic detection approach in the hyperarid coastal Atacama Desert, northern Chile
Unique fog ecosystems that occur inland along the Chilean coastal desert are dominated by Tillandsia landbeckii. The average annual precipitation in this hyperarid area lies below 1 mm per year. Tillandsia are specialized in the foliar uptake of fog as a main source of water. The detailed mapping of...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
6 July 2022
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| In: |
Journal of arid environments
Year: 2022, Jahrgang: 205, Pages: 1-11 |
| ISSN: | 1095-922X |
| DOI: | 10.1016/j.jaridenv.2022.104821 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1016/j.jaridenv.2022.104821 Verlag, Volltext: https://www.sciencedirect.com/science/article/pii/S0140196322001161 
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| Verfasserangaben: | Signe Mikulane, Alexander Siegmund, Camilo del Río, Marcus A. Koch, Pablo Osses, Juan-Luis García |
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| 520 | |a Unique fog ecosystems that occur inland along the Chilean coastal desert are dominated by Tillandsia landbeckii. The average annual precipitation in this hyperarid area lies below 1 mm per year. Tillandsia are specialized in the foliar uptake of fog as a main source of water. The detailed mapping of the distribution of Tillandsia is lacking, making it difficult to understand their geo-ecological niche and to determine the impacts that climate change may have on this species. The objective of this study is to create a detailed spatial distribution of Tillandsia in the Atacama Desert in northern Chile based on remote sensing semi-automatic detection process. For this purpose, high-resolution WorldView-3 optical satellite data has been acquired. The extraction of Tillandsia was done with ENVI Deep Learning tools. As a result, a map of Tillandsia has been created. Several fields were found between Cerro Huantajaya in the north and Cerro Soronal in the south in the study area between 800 and 1300 m a.s.l. For validation purposes ground truth data has been used. The overall accuracy of this classification is 92.02%. The results can be used as a basis for geo-ecological niche modeling, further monitoring and for the development of conservation strategies. | ||
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