Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.scitotenv.2024.178347 |
Lizenz  |
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| Titel (primär) | Monitoring and modelling landscape structure, land use intensity and landscape change as drivers of water quality using remote sensing |
| Autor | Lausch, A.; Selsam, P.; Heege, T.; von Trentini, F.; Almeroth, A.; Borg, E.; Klenke, R.; Bumberger, J.
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| Quelle | Science of the Total Environment |
| Erscheinungsjahr | 2025 |
| Department | CLE; NSF; MET; iDiv |
| Band/Volume | 960 |
| Seite von | art. 178347 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | https://ars.els-cdn.com/content/image/1-s2.0-S004896972408505X-mmc1.docx |
| Keywords | Remote sensing; Water quality; Landscape structure; Land use intensity; Landscape change; Machine learning |
| Abstract | The interactions between landscape structure, land use intensity (LUI), climate change, and ecological processes significantly impact hydrological processes, affecting water quality. Monitoring these factors is crucial for understanding their influence on water quality. Remote sensing (RS) provides a continuous, standardized approach to capture landscape structures, LUI, and landscape changes over long-term time series. In this study, RS-based indicators from Landsat data (2018–2021) were used to assess landscape structure, LUI, and land use change for a study area in northern Germany, applying the ESIS/Imalys tool. These indicators were then used to model and predict water quality (Chla) in 119 standing waters. Various machine learning methods, including Generalised Linear Models, Support Vector Machines, Deep Learning, Decision Trees, Random Forest, and Gradient Boosted Trees, were tested. The Random Forest model performed best, with a correlation of 0.744 ± 0.11. Indicators related to landscape structure, such as diversity_mean (0.376) and relation_mean (0.292), had the highest global correlation weights, while LUI and land use change indicators like NirV2_mean (0.369) and NirV_regme (0.284) were also significant. All indicators and their effects on water quality (Chla) are discussed in detail. The study highlights the potential of the ESIS/Imalys tool for quantifying landscape structure, LUI, and land use change with RS to model and predict water quality and suggests directions for future model improvements by incorporating additional influencing factors. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30245 |
| Lausch, A., Selsam, P., Heege, T., von Trentini, F., Almeroth, A., Borg, E., Klenke, R., Bumberger, J. (2025): Monitoring and modelling landscape structure, land use intensity and landscape change as drivers of water quality using remote sensing Sci. Total Environ. 960 , art. 178347 10.1016/j.scitotenv.2024.178347 |
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