Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Description
2.2. Water-Quality Parameters
2.3. Collection and Processing of Hydroacoustic Data
2.4. Satellite Data Mining and Analysis
2.4.1. Preprocessing of Satellite Data
2.4.2. Development of Water-Quality Models
2.4.3. Fish Spatial Distribution Modeling
3. Results
3.1. Development of Water-Quality Models
3.2. Spatiotemporal Patterns of Water Quality Parameters
3.3. Fish Spatial Distribution Modelling Results
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Fox, J.; Nino-Murcia, A. Status of species conservation banking in the United States. Conserv. Biol. 2005, 19, 996–1007. [Google Scholar] [CrossRef]
- Champ, W.; Kelly, F.L.; King, J.J. The Water Framework Directive: Using fish as a management tool. Biol. Environ. Proc. R. Ir. Acad. 2009, 109, 191–206. [Google Scholar] [CrossRef]
- Council of the European Parliament. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Parliam. 2000, 1–72. Available online: https://eur-lex.europa.eu/resource.html?uri=cellar:5c835afb-2ec6-4577-bdf8-756d3d694eeb.0004.02/DOC_1&format=PDF (accessed on 24 October 2019).
- Sandström, A.; Philipson, P.; Asp, A.; Axenrot, T.; Kinnerbäck, A.; Ragnarsson-Stabo, H.; Holmgren, K. Assessing the potential of remote sensing-derived water quality data to explain variations in fish assemblages and to support fish status assessments in large lakes. Hydrobiologia 2016, 780, 71–84. [Google Scholar] [CrossRef]
- Stow, D.A.; Hope, A.; McGuire, D.; Verbyla, D.; Gamon, J.; Huemmrich, F.; Houston, S.; Racinef, C.; Sturmg, M.; Tapeh, K.; et al. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sens. Environ. 2004, 89, 281–308. [Google Scholar] [CrossRef]
- Lathrop, R.; Lillesand, T.M. Use of Thematic Mapper data to assess water quality in Green Bay and central Lake Michigan. Photogramm. Eng. Remote Sens. 1986, 5, 671–680. [Google Scholar]
- Noss, R.F. Ecosystems as conservation targets. Trends Ecol. Evol. 1996, 11, 351. [Google Scholar] [CrossRef]
- Muška, M.; Tušer, M.; Frouzová, J.; Mrkvička, T.; Ricard, D.; Sed’a, J.; Morelli, F.; Kubečka, J. Real-time distribution of pelagic fish: Combining hydroacoustics, GIS and spatial modelling at a fine spatial scale. Sci. Rep. 2018, 8, 5381. [Google Scholar] [CrossRef]
- Rychtecky, P.; Znachor, P. Spatial heterogeneity and seasonal succession of phytoplankton along the longitudinal gradient in a eutrophic reservoir. Hydrobiologia 2011, 663, 175–186. [Google Scholar] [CrossRef]
- Znachor, P.; Visocká, V.; Nedoma, J.; Rychtecký, P. Spatial heterogeneity of diatom silicification and growth in a eutrophic reservoir. Freshw. Biol. 2013, 58, 1889–1902. [Google Scholar] [CrossRef]
- Znachor, P.; Nedoma, J.; Hejzlar, J.; Sed’a, J.; Kopáček, J.; Boukal, D.; Mrkvička, T. Multiple long-term trends and trend reversals dominate environmental conditions in a man-made freshwater reservoir. Sci. Total Environ. 2018, 624, 24–33. [Google Scholar] [CrossRef]
- Simek, K.; Hornák, K.; Jezbera, J.; Nedoma, J.; Znachor, P.; Hejzlar, J.; Sed’a, J. Spatio-temporal patterns of bacterioplankton production and community composition related to phytoplankton composition and protistan bacterivory in a dam reservoir. Aquat. Microb. Ecol. 2008, 51, 249–262. [Google Scholar] [CrossRef] [Green Version]
- Vašek, M.; Prchalová, M.; Říha, M.; Blabolil, P.; Čech, M.; Draštík, V.; Peterka, J. Fish community response to the longitudinal environmental gradient in Czech deep-valley reservoirs: Implications for ecological monitoring and management. Ecol. Indic. 2016, 63, 219–230. [Google Scholar] [CrossRef]
- Znachor, P.; Hejzlar, J.; Vrba, J.; Nedoma, J.; Seďa, J.; Šimek, K.; Komárková, J.; Kopáček, J.; Šorf, M.; Kubečka, J.; et al. Brief History of Long-Term Ecological Research into Aquatic Ecosystems and Their Catchments in the Czech Republic. Part I: Manmade Reservoirs; Institute of Hydrobiology, BC CAS: České Budějovice, Czech Republic, 2016; p. 80. ISBN 978-80-86668-38-3. [Google Scholar]
- Beutler, M.; Wiltshire, K.H.; Meyer, B.; Moldaenke, C.; Lüring, C.; Meyerhöfer, M.; Hansen, U.-P.; Dau, H. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynth. Res. 2002, 72, 39–53. [Google Scholar] [CrossRef] [PubMed]
- Foote, K.G.; Knudsen, H.P.; Korneliussen, R.J.; Nordbø, P.E.; Røang, K. Postprocessing system for echo sounder data. J. Acoust. Soc. Am. 1991, 90, 37–47. [Google Scholar] [CrossRef]
- Frouzova, J.; Kubecka, J.; Balk, H.; Frouz, J. Target strength of some European fish species and its dependence on fish body parameters. Fish. Res. 2005, 75, 86–96. [Google Scholar] [CrossRef]
- Olmanson, L.G.; Bauer, M.E.; Brezonik, P.L. A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sens. Environ. 2008, 112, 4086–4097. [Google Scholar] [CrossRef]
- Chander, G.; Markham, B. Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2674–2677. [Google Scholar] [CrossRef]
- Zanter, K. Landsat 8 (L8) Data Users Handbook. U.S. Geological Survey, Department of the Interior; Version 1; USGS: Reston, VA, USA, 2015.
- Jiang, H.; Feng, M.; Zhu, Y.; Lu, N.; Huang, J.; Xiao, T. An automated method for extracting rivers and lakes from Landsat imagery. Remote Sens. 2014, 6, 5067–5089. [Google Scholar] [CrossRef]
- Tan, W.; Liu, P.; Liu, Y.; Yang, S.; Feng, S. A 30-year assessment of phytoplankton blooms in Erhai Lake using Landsat imagery: 1987 to 2016. Remote Sens. 2017, 9, 1265. [Google Scholar] [CrossRef]
- Lathrop, R.G.; Lillesand, T.M.; Yandell, B.S. Testing the utility of simple multi-date Thematic Mapper calibration algorithms for monitoring turbid inland waters. Int. J. Remote Sens. 1991, 12, 2045–2063. [Google Scholar] [CrossRef]
- Matthews, M.W.; Bernard, S.; Winter, K. Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote Sens. Environ. 2010, 114, 2070–2087. [Google Scholar] [CrossRef]
- Davies-Colley, R.J.; Vant, W.N. Absorption of light by yellow substance in freshwater lakes. Limnol. Oceanogr. 1987, 32, 416–425. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Říha, M.; Kubečka, J.; Vašek, M.; Seda, A.J.; Mrkvička, T.; Prchalová, M.; Matēna, J.; Hladík, M.; Čech, M.; Draštík, V.; et al. Long-term development of fish populations in the Římov Reservoir. Fish. Manag. Ecol. 2009, 16, 121–129. [Google Scholar] [CrossRef]
- Mishra, N.; Helder, D.; Barsi, J.; Markham, B. Continuous calibration improvement in solar reflective bands: Landsat 5 through Landsat 8. Remote Sens. Environ. 2016, 185, 7–15. [Google Scholar] [CrossRef] [Green Version]
Source Dataset (N = 162) | Mean | Median | Q25%–Q75% | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|
Biomass (kg/pixel) | 2.227 | 0.17 | 0.20–3.01 | 2.933 | 0 | 17.874 |
Temperature (°C) | 21.95 | 21.94 | 21.50–22.38 | 0.4284 | 21.06 | 23.25 |
Green algae (μg/L) | 1.32 | 1.31 | 1.284–1.1.345 | 0.057 | 1.013 | 1.479 |
Cyanobacteria (μg/L) | 0.087 | 0.106 | 0.027–0.116 | 0.056 | 0.014 | 0.0242 |
Yellow Substance (μg/L) | 2.012 | 2.02 | 1.961–2.0669 | 0.066 | 1.84 | 2.174 |
Chl-a (°C) | 4.087 | 4.147 | 3.918–4.231 | 0.21 | 3.682 | 5.32 |
Source Dataset (N = 351) | Mean | Median | Q25%–Q75% | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|
Biomass (kg/pixel) | 2.875 | 1.368 | 0.0–1.915 | 6.355 | 0 | 44.477 |
Temperature (°C) | 19.10 | 19.11 | 18.96–19.25 | 0.28 | 18.4 | 21.03 |
Green algae (μg/L) | 4.146 | 3.726 | 2.721–5.128 | 1.819 | 1.9 | 14.847 |
Cyanobacteria (μg/L) | 6.604 | 5.516 | 4.199–8.666 | 3.66 | 1.479 | 34.404 |
Yellow Substance (μg/L) | 1.72 | 1.739 | 1.601–1.864 | 0.191 | 1.11 | 2.11 |
Chl-a (°C) | 11.65 | 10.592 | 7.918–14.431 | 4.692 | 5.497 | 38.225 |
© 2019 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Perivolioti, T.-M.; Tušer, M.; Frouzova, J.; Znachor, P.; Rychtecký, P.; Mouratidis, A.; Terzopoulos, D.; Bobori, D. Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing. Water 2019, 11, 2226. https://doi.org/10.3390/w11112226
Perivolioti T-M, Tušer M, Frouzova J, Znachor P, Rychtecký P, Mouratidis A, Terzopoulos D, Bobori D. Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing. Water. 2019; 11(11):2226. https://doi.org/10.3390/w11112226
Chicago/Turabian StylePerivolioti, Triantafyllia-Maria, Michal Tušer, Jaroslava Frouzova, Petr Znachor, Pavel Rychtecký, Antonios Mouratidis, Dimitrios Terzopoulos, and Dimitra Bobori. 2019. "Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing" Water 11, no. 11: 2226. https://doi.org/10.3390/w11112226