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DOI: 10.5445/IR/1000085641
Veröffentlicht am 31.08.2018
DOI: 10.3390/ijerph15091881

Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

Keller, Sina; Maier, Philipp; Riese, Felix; Norra, Stefan; Holbach, Andreas; Börsig, Nicolas; Wilhelms, Andre; Moldaenke, Christian; Zaake, André; Hinz, Stefan

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preproces ... mehr

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Institut für Angewandte Geowissenschaften (AGW)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1660-4601
URN: urn:nbn:de:swb:90-856417
KITopen ID: 1000085641
Erschienen in International journal of environmental research and public health
Band 15
Heft 9
Seiten 1881/1-15
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 30.08.2018
Schlagworte machine learning; regression; water quality parameters; hyperspectral data; spectral features; algae; chlorophyll a; multi-sensor system; fluorometer; field campaign
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