Universität zu Köln

Reddig, Fabian ORCID: 0000-0003-4958-1208, Hütt, Christoph ORCID: 0000-0001-8381-9676, Jenal, Alexander ORCID: 0000-0002-1890-4839, Wolf, Jan ORCID: 0009-0005-7732-5879 and Bareth, Georg ORCID: 0000-0003-3692-8655 (2025). The Invisible Plant: Estimating Fractional Vegetation Cover of Tillandsia landbeckii in the Atacama Desert using Hyperspectral EnMAP and High-Resolution Validation Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 93 (6). pp. 583-609. Springer Nature. ISSN 2512-2789

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Abstract

Fractional vegetation cover (FVC) is a critical canopy structural variable essential for understanding vegetation dynamics. Estimating FVC in arid environments often remains difficult due to sparse vegetation, weak spectral signals, and high spectral confusion with the background. This study investigates the potential of spaceborne imaging spectroscopy to overcome these limitations by estimating the FVC of Tillandsia landbeckii, a fog-dependent bromeliad endemic to the Atacama Desert. Owing to its low reflectance and lack of chlorophyll absorption features, Tillandsia remains largely undetectable using conventional multispectral sensors. We used hyperspectral data from the EnMAP satellite and applied six semi-supervised sparse spectral unmixing algorithms at both local and regional scales. Unlike traditional supervised approaches, our method does not rely on labeled training data. Instead, it uses a limited set of field- and image-based endmember spectra to perform subpixel unmixing. Validation was conducted using a high-resolution reference dataset combining a UAV orthomosaic (1.7 cm) and Pléiades Neo imagery (30 cm). While the best local model reached a mean absolute error (MAE) of 3.1%, restricting the regional regression to confirmed Tillandsia pixels further reduced the MAE to 1.8%. This study presents the first operational demonstration of EnMAP for subpixel mapping of Tillandsia landbeckii cover, highlighting the value of semi-supervised unmixing techniques for vegetation analysis in hyper-arid environments with limited reference data and strong spectral background similarity. The approach establishes a transferable framework for estimating low-signal vegetation in hyper-arid regions, advancing the state of the art in fractional cover mapping.

Item Type:	Article
Creators:	Creators Email ORCID ORCID Put Code Reddig, Fabian UNSPECIFIED https://orcid.org/0000-0003-4958-1208 UNSPECIFIED Hütt, Christoph UNSPECIFIED https://orcid.org/0000-0001-8381-9676 UNSPECIFIED Jenal, Alexander UNSPECIFIED https://orcid.org/0000-0002-1890-4839 UNSPECIFIED Wolf, Jan UNSPECIFIED https://orcid.org/0009-0005-7732-5879 UNSPECIFIED Bareth, Georg UNSPECIFIED https://orcid.org/0000-0003-3692-8655 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-806679
Identification Number:	10.1007/s41064-025-00358-7
Journal or Publication Title:	PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science
Volume:	93
Number:	6
Page Range:	pp. 583-609
Number of Pages:	27
Date:	15 December 2025
Publisher:	Springer Nature
ISSN:	2512-2789
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Geosciences > Geographisches Institut
Subjects:	Earth sciences Geography and travel
Uncontrolled Keywords:	Keywords Language Remote sensing ; Spectral unmixing ; Hyperspectral ; Tillandsia landbeckii ; EnMAP English
['eprint_fieldname_oa_funders' not defined]:	Publikationsfonds UzK
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/80667