Estimating Reed Bed Cover in Hungarian Fish Ponds Using NDVI-Based Remote Sensing Technique
In the EU, aquaculture ponds cover an area of 360,000 ha and are a crucial part of the rural landscape. As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environm...
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| Veröffentlicht in: | Water (Basel) 2023-04, Vol.15 (8), p.1554 |
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| creator | Sharma, Priya Varga, Monika Kerezsi, György Kajári, Balázs Halasi-Kovács, Béla Békefi, Emese Gaál, Márta Gyalog, Gergő |
| description | In the EU, aquaculture ponds cover an area of 360,000 ha and are a crucial part of the rural landscape. As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environmental studies to be able to accurately estimate the extent and the temporal dynamics of reed cover. Here, we propose a method for mapping reed cover in fish ponds from freely available Sentinel-2 imagery using the normalized difference vegetation index (NDVI), which we applied to Hungary, the third largest carp producer in the EU. The dynamics of reed cover in Hungarian fish ponds mapped using satellite imagery show a high degree of agreement with the ground-truth points, and when compared with data reported in the annual aquaculture reports for Hungary, it was found that the calculation of reed cover based on the NDVI-based approach was more consistent than the estimates provided in the report. We discuss possible applications of this remote sensing technique in estimating reed-like vegetation cover in fish ponds and the possible use of the results for climate change studies and ecosystem services assessment. |
| doi_str_mv | 10.3390/w15081554 |
| format | Article |
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As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environmental studies to be able to accurately estimate the extent and the temporal dynamics of reed cover. Here, we propose a method for mapping reed cover in fish ponds from freely available Sentinel-2 imagery using the normalized difference vegetation index (NDVI), which we applied to Hungary, the third largest carp producer in the EU. The dynamics of reed cover in Hungarian fish ponds mapped using satellite imagery show a high degree of agreement with the ground-truth points, and when compared with data reported in the annual aquaculture reports for Hungary, it was found that the calculation of reed cover based on the NDVI-based approach was more consistent than the estimates provided in the report. We discuss possible applications of this remote sensing technique in estimating reed-like vegetation cover in fish ponds and the possible use of the results for climate change studies and ecosystem services assessment.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w15081554</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agriculture ; Algorithms ; Aquaculture ; Aquaculture industry ; Aquatic plants ; Automation ; Carbon sequestration ; Carp ; Classification ; Climate change ; Climate studies ; Climatic changes ; Ecosystem services ; Ecosystems ; Environmental studies ; Farmers ; Farms ; Fish ponds ; Fish production ; Fishes ; Infrastructure ; Machine learning ; Methods ; Normalized difference vegetative index ; Nutrient cycles ; Phenology ; Ponds ; Reed beds ; Regions ; Remote sensing ; Satellite imagery ; Unmanned aerial vehicles ; Vegetation ; Vegetation cover ; Wildlife ; Wildlife conservation ; Wildlife habitats</subject><ispartof>Water (Basel), 2023-04, Vol.15 (8), p.1554</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Agriculture Algorithms Aquaculture Aquaculture industry Aquatic plants Automation Carbon sequestration Carp Classification Climate change Climate studies Climatic changes Ecosystem services Ecosystems Environmental studies Farmers Farms Fish ponds Fish production Fishes Infrastructure Machine learning Methods Normalized difference vegetative index Nutrient cycles Phenology Ponds Reed beds Regions Remote sensing Satellite imagery Unmanned aerial vehicles Vegetation Vegetation cover Wildlife Wildlife conservation Wildlife habitats |
| title | Estimating Reed Bed Cover in Hungarian Fish Ponds Using NDVI-Based Remote Sensing Technique |
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