Mapping Specific Constituents of an Ochre-Coloured Watercourse Based on In Situ and Airborne Hyperspectral Remote Sensing Data
Author:
Ulrich Christoph1, Hupfer Michael23, Schwefel Robert2ORCID, Bannehr Lutz1, Lausch Angela145ORCID
Affiliation:
1. Department of Architecture, Facility Management and Geoinformation, Institute for Geoinformation and Surveying, Bauhausstraße 8, D-06846 Dessau, Germany 2. Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 301, D-12587 Berlin, Germany 3. Department of Aquatic Ecology, Brandenburg Technical University Cottbus-Senftenberg, Seestr. 45, D-15526 Bad Saarow, Germany 4. Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, D-04318 Leipzig, Germany 5. Department of Geography and Geoecology, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 4, D-06120 Halle, Germany
Abstract
It is a well-known fact that water bodies are crucial for human life, ecosystems and biodiversity. Therefore, they are subject to regulatory monitoring in terms of water quality. However, land-use intensification, such as open-cast mining activities, can have a direct impact on water quality. Unfortunately, in situ measurements of water quality parameters are spatially limited, costly and time-consuming, which is why we proposed a combination of hyperspectral data, in situ data and simple regression models in this study to estimate and thus monitor various water quality parameters. We focused on the variables of total iron, ferrous iron, ferric iron, sulphate and chlorophyll-a. Unlike other studies, we used a combination of airborne hyperspectral and RGB data to ensure a very high spatial resolution of the data. To investigate the potential of our approach, we conducted simultaneous in situ measurements and airborne hyperspectral/RGB aircraft campaigns at different sites of the Spree River in Germany to monitor the impact of pyrite weathering on water bodies after open-cast mining activities. Appropriate regression models were developed to estimate the five variables mentioned above. The model with the best performance for each variable gave a coefficient of determination R2 of 64% to 79%. This clearly shows the potential of airborne hyperspectral/RGB data for water quality monitoring. In further investigations, we focused on the use of machine learning techniques, as well as transferability to other water bodies. The approach presented here has great potential for the development of a monitoring method for the continuous monitoring of still waters and large watercourses, especially given the freely available space-based hyperspectral missions via EnMAP.
Funder
Institute of Geoinformation and Surveying of the Anhalt University of Applied Science Leibniz Institute of Freshwater Ecology and Inland Fisheries
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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