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A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations

Marshall Ingram, David und Kühl, Kevin und Heiden, Uta und Bachmann, Martin und Schmidt, Thomas (2023) A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations. WHISPERS 2023, 2023-10-30 - 2023-11-03, Athens, Greece.

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Offizielle URL: https://www.ieee-whispers.com/

Kurzfassung

Fractional vegetation cover (FVC) is an important measure for the conservation, restoration and maintenance of biodiverse environments, giving the spatial patterns and distributions of photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation as well as bare soil (BS) in a given region. Using hyperspectral remote sensing observations from DESIS and EnMAP (Environmental Mapping and Analysis Program), we derive FVC for Camarena, Spain, a semi-arid region southwest of Madrid and an important test site for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and compare the results from both sensors. DESIS and EnMAP are both hyperspectral remote sensing instruments with spatial resolutions of 30 m but they differ in other key aspects. DESIS has a spectral range of 400-1000 nm and a maximum spectral resolution of 2.55 nm whilst EnMAP has a range of 400-2500 nm and a resolution of 6.5-10 nm. The SWIR bands of EnMAP make it far more useful for the derivation of FVC than DESIS due to characteristic absorption features above 1500 nm which help to disentangle NPV and BS spectra. Nevertheless, abundances can still be derived from the FVC processing, accepting that the RMSEs are higher for the DESIS results (13% for PV, 18% for NPV, 9% for BS) than for the EnMAP results (12% for PV, 14% for NPV, 4% for BS). The FVC processing of the DESIS and EnMAP images consists of three steps. After some pre-processing (band removal and smoothing), pure spectra are retrieved from the image using the spatial-spectral endmember extraction method developed by Rogge. This method creates a global set of endmembers from the image after the masking of pixels which are not vegetation or soil. Secondly, the extracted endmembers are classified with a Logistic Regression (for DESIS) or a Random Forest (for EnMAP) classifier which were trained from a spectral library containing 631 samples. Three classes are used for the classification: PV, NPV amd BS. Unmixing is the final stage which uses a MESMA approach where each pixel is considered to be a linear combination of one PV spectrum, one NPV spectrum and one BS spectrum from the labelled endmember library. The class abundance are the weights found in the linear unmixing and an extra shade component is considered. In this work, we will present FVC maps derived from EnMAP and DESIS of Camarena which is a semi-arid region covering approximately 75 km2 in the Province of Toledo, Spain, where the land is mainly used for rainfed agriculture. It has an undulating topography with vegetation growing on sloping areas that were either not considered good enough for farming or later abandoned. Since June 2019, 60 cloud free images were acquired by DESIS over the region and EnMAP has so far acquired 8 cloud free images in this area since launch in April 2022. Several EnMAP images in July-August 2022 coincide closely with a DESIS observation which will enable quantifiable comparisons to be made between the two sensors and allow for an evaluation of the results considering the different wavelength ranges of each sensor.

elib-URL des Eintrags:https://elib.dlr.de/199224/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Marshall Ingram, DavidDavid.Marshall (at) dlr.dehttps://orcid.org/0000-0002-4765-8198NICHT SPEZIFIZIERT
Kühl, Kevinkevin.kuehl (at) dlr.dehttps://orcid.org/0009-0005-5069-5570146681629
Heiden, Utauta.heiden (at) dlr.dehttps://orcid.org/0000-0002-3865-1912NICHT SPEZIFIZIERT
Bachmann, MartinMartin.Bachmann (at) dlr.dehttps://orcid.org/0000-0001-8381-7662146681630
Schmidt, ThomasCentro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT)NICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2023
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:DESIS, EnMAP, fractional vegetation cover, live cover, endmembers, classification, unmixing
Veranstaltungstitel:WHISPERS 2023
Veranstaltungsort:Athens, Greece
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:30 Oktober 2023
Veranstaltungsende:3 November 2023
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Optische Fernerkundung, R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren, R - DESIS Betrieb, R - Projekt EnMAP Phase E
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Hinterlegt von: Kühl, Kevin
Hinterlegt am:15 Nov 2023 14:16
Letzte Änderung:24 Apr 2024 20:59

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