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ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS

Maier, Philipp; Keller, Sina

Abstract:
Water is a key component of life, the natural environment and human health. For monitoring the conditions of a water body, the chlorophyll a concentration can serve as a proxy for nutrients and oxygen supply. In situ measurements of water quality parameters are often time-consuming, expensive and limited in areal validity. Therefore, we apply remote sensing techniques. During field campaigns, we collected hyperspectral data with a spectrometer and in situ measured chlorophyll a concentrations of 13 inland water bodies with different spectral characteristics. One objective of this study is to estimate chlorophyll a concentrations of these inland waters by applying three machine learning regression models: Random Forest, Support Vector Machine and an Artificial Neural Network. Additionally, we simulate four different hyperspectral resolutions of the spectrometer data to investigate the effects on the estimation performance. Furthermore, the application of first order derivatives of the spectra is evaluated in turn to the regression performance. This study reveals the potential of combining machine learning approaches and remote sensing data for inland waters. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000095971
Veröffentlicht am 24.06.2019
Originalveröffentlichung
DOI: 10.5194/isprs-annals-IV-2-W5-609-2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator ISSN: 2194-9050
KITopen-ID: 1000095971
Erschienen in ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands. Ed.: G. Vosselman
Veranstaltung ISPRS Geospatial Week (2019), Enschede, Niederlande, 10.06.2019 – 14.06.2019
Verlag ISPRS
Seiten 609–614
Serie ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; IV-2/W5
Vorab online veröffentlicht am 29.05.2019
Schlagworte Spectral Resolution, Inland Waters, Supervised Learning, Support Vector Machine, Random Forest, Neural Network, Remote Sensing, Phytoplankton
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