Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Sampling Methods
2.3. Remote Sensing Data
2.4. Vegetation Indices
- -
- NDVI (Normalised Difference Vegetation Index) [37]: this is a vegetation index that uses the difference in reflectance between the near infrared and red parts of the electromagnetic spectrum to quantify vegetation density and health;
- -
- SAVI (Soil-Adjusted Vegetation Index) [52]: This vegetation index is similar to NDVI but considers the reflectivity of the soil. It was developed to correct the influence of soil on vegetation measurements;
- -
- MNDWI (Modified Normalised Difference Water Index) [33]: This index focuses on the detection of water. It uses the near infrared and green bands to distinguish between water and other types of land cover;
- -
- LAI (Leaf Area Index) [53]: A key indicator of vegetation structure that represents the total leaf area per unit of ground area. It provides information on the density and distribution of leaves in a vegetation;
- -
- FAPAR (Fraction of Absorbed Photosynthetically Active Radiation): This index measures the proportion of photosynthetically active radiation absorbed by vegetation, which is an indicator of the photosynthetic efficiency of an ecosystem;
- -
- FCOVER (Fraction of Ground Cover): This index quantifies the proportion of the ground covered by vegetation, providing information on the density and cover of vegetation in a given area.
2.5. Data Analysis
3. Results
3.1. Radiometric Vegetation Indices Analysis
3.2. Field and Remote Sensing Data
3.3. Data Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
- Scheffer, M.; Hosper, S.H.; Meijer, M.-L.; Moss, B.; Jeppesen, E. Alternative equlibria in shallow lakes. Trends Ecol. Evol. 1993, 8, 275–279. [Google Scholar] [CrossRef]
- Scheffer, M. Ecology of Shallow Lakes; Chapman and Hall: London, UK, 1998. [Google Scholar] [CrossRef]
- Scheffer, M.; van Nes, E.H. Shallow lakes theory revisited: Various alternative regimes driven by climate, nutrients, depth and lake size. Hydrobiologia 2007, 584, 455–466. [Google Scholar] [CrossRef]
- Cao, T.; Ni, L.; Xie, P.; Xu, J.; Zhang, M. Effects of moderate ammonium enrichment on three submersed macrophytes under contrasting light availability. Freshw. Biol. 2011, 56, 1620–1629. [Google Scholar] [CrossRef]
- Rodrigo, M.A. Wetland Restoration with Hydrophytes: A Review. Plants 2021, 10, 1035. [Google Scholar] [CrossRef]
- James, W.F.; Barko, J.W.; Butler, M.G. Shear stress and sediment resuspension in relation to submersed macrophyte biomass. Hydrobiologia 2004, 515, 181–191. [Google Scholar] [CrossRef]
- Søndergaard, M.; Moss, B. Impact of submerged macrophytes on phytoplankton in shallow freshwater lakes. In The Structuring Role of Submerged Macrophytes in Lakes; Springer: New York, NY, USA, 1998; pp. 115–132. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhou, X.; Han, R.; Xu, X.; Wang, G.; Liu, X.; Bi, F.; Feng, D. Reproduction capacity of Potamogeton crispus fragments and its role in water purification and algae inhibition in eutrophic lakes. Sci. Total Environ. 2016, 580, 1421–1428. [Google Scholar] [CrossRef] [PubMed]
- Chao, C.; Wang, L.; Li, Y.; Yan, Z.; Liu, H.; Yu, D.; Liu, C. Response of sediment and water microbial communities to submerged vegetations restoration in a shallow eutrophic lake. Sci. Total Environ. 2021, 801, 149701. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Liu, Z.S.; Zhang, Y.; Liu, B.Y.; Zhou, Q.H.; Zeng, L.; He, F.; Wu, Z.B. Synergistic removal effect of P in sediment of all fractions by combining the modified bentonite granules and submerged macrophyte. Sci. Total Environ. 2018, 626, 458–467. [Google Scholar] [CrossRef] [PubMed]
- RejmánkováJ, E. The role of macrophytes in wetland ecosystems. Ecol. Field Biol. 2011, 34, 333–345. [Google Scholar] [CrossRef]
- Le Fur, I.; De Wit, R.; Plus, M.; Oheix, J.; Simier, M.; Ouisse, V. Submerged benthic macrophytes in Mediterranean lagoons: Distribution patterns in relation to water chemistry and depth. Hydrobiologia 2018, 808, 175–200. [Google Scholar] [CrossRef]
- Timms, R.M.; Moss, B. Prevention of growth of potentially dense phytoplankton populations by zooplankton grazing, in the presence of zooplanktivorous fish, in a shallow wetland ecosystem. Limnol. Oceanogr. 1984, 29, 472–486. [Google Scholar] [CrossRef]
- Dvořák, J. An example of relationships between macrophytes, macroinvertebrates and their food resources in a shallow euthrophic lake. Hydrobiologia 1996, 339, 27–36. [Google Scholar] [CrossRef]
- Mitchell, S.F. Primary production in a shallow eutrophic lake dominated alternately by phytoplankton and by submerged macrophytes. Aquat. Bot. 1989, 33, 101–110. [Google Scholar] [CrossRef]
- Romo, S.; García-Murcia, A.; Villena, M.J.; Sánchez, V.; Ballester, A. Tendencias del fitoplancton en el lago de la Albufera de Valencia e implicaciones para su ecología, gestión y recuperación. Limnetica 2008, 27, 11–28. [Google Scholar] [CrossRef]
- Official State Gazette (BOE). Royal Legislative Decree 1/2001, of 20 July 2001, approving the revised text of the Water Law. BOE 2001, 176, 26791–26817. [Google Scholar]
- Official State Gazette (BOE). Royal Decree 2090/2008, of 22 December 2008, approving the Regulations for the partial development of Law 26/2007, of 23 October 2007, on Environmental Responsibility. BOE 2008, 308, 51626–51646. [Google Scholar]
- Xue, J.; Su, B. Significant remote sensing vegetation indices: A review of developments and application. J. Sens. 2017, 2017, 1353691. [Google Scholar] [CrossRef]
- Samboni, N.E.; Carvajal, Y.; Escobar, J. Revisión de parámetros fisicoquímicos como indicadores de calidad y contaminación del agua. Ing. E Investig. 2007, 27, 172–181. Available online: https://repositorio.unal.edu.co/handle/unal/28869 (accessed on 12 November 2023).
- Paredes-Arquiola, J.; Andreu-Álvarez, J.; Martín-Monerris, M.; Solera, A. Water quantity and quality models applied to the Jucar River Basin, Spain. Water Resour. Manag. 2010, 24, 2759–2779. [Google Scholar] [CrossRef]
- Matthews, M.W. A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int. J. Remote Sens. 2011, 32, 6855–6899. [Google Scholar] [CrossRef]
- Hestir, E.L.; Brando, V.E.; Bresciani, M.; Giardino, C.; Matta, E.; Villa, P.; Dekker, A.G. Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission. Remote Sens. Environ. 2015, 167, 181–195. [Google Scholar] [CrossRef]
- Huang, C.; Chen, Y.; Shiqiang, Z.; Jianping, W. Detecting, extracting and monitoring surface water from space using optical sensors: A review. Rev. Geophys. 2018, 56, 333–360. [Google Scholar] [CrossRef]
- Pompêo, M.; Moschini-Carlos, V.; Bitencourt, M.D.; Sòria-Perpinyà, X.; Vicente, E.; Delegido, J. Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll-a, Sechhi disk depth and cyanobacteria cell number: The Cantareira system reservoirs (São Paulo, Brazil). Environ. Sci. Pollut. Res. 2021, 28, 34990–35011. [Google Scholar] [CrossRef] [PubMed]
- Schmidt, K.S.; Skidmore, A.K. Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 2003, 85, 92–108. [Google Scholar] [CrossRef]
- Foley, W.J.; McIlwee, A.; Lawler, I.; Aragones, L.; Woolnough, A.P.; Berding, N. Ecological Applications of near Infrared Reflectance Spectroscopy—A Tool for Rapid, Cost-Effective Prediction of the Composition of Plant and Animal Tissues and Aspects of Animal Performance. Oecologia 1998, 116, 293–305. [Google Scholar] [CrossRef] [PubMed]
- Malthus, T.J. Bio-optical Modeling and Remote Sensing of Aquatic Macrophytes. In Bio-Optical Modeling and Remote Sensing of Inland Waters; Elsevier: Amsterdam, The Netherlands, 2017; pp. 263–308. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Bresciani, M.; Giardino, C.; Longhi, D.; Pinardi, M.; Bartoli, M.; Vascellari, M. Imaging spectrometry of productive inland waters. Application to the lakes of Mantua. Ital. J. Remote Sens. 2009, 41, 147–156. [Google Scholar] [CrossRef]
- Tian, Y.Q.; Yu, Q.; Zimmerman, M.J.; Flint, S.; Waldron, M.C. Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing. Freshw. Biol. 2010, 55, 1658–1673. [Google Scholar] [CrossRef]
- Villa, P.; Laini, A.; Bresciani, M.; Bolpagni, R. A Remote Sensing Approach to Monitor the Conservation Status of Lacustrine Phragmites Australis Beds. Wetl. Ecol. Manag. 2013, 21, 399–416. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Hu, C. A novel ocean color index to detect floating algae in the global oceans. Remote Sens. Environ. 2009, 113, 2118–2129. [Google Scholar] [CrossRef]
- Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Villa, P.; Bresciani, M.; Bolpagni, R.; Pinardi, M.; Giardino, C. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sens. Environ. 2015, 171, 218–233. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Huete, A.R.; Liu, H.Q.; Batchily, K.; van Leeuwen, W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 1997, 59, 440–451. [Google Scholar] [CrossRef]
- Villa, P.; Mousivand, A.; Bresciani, M. Aquatic vegetation indices assessment through radiative transfer modeling and linear mixture simulation. Int. J. Appl. Earth Obs. Geoinf. 2014, 30, 113–127. [Google Scholar] [CrossRef]
- Espel, D.; Courty, S.; Auda, Y.; Sheeren, D.; Elger, A. Submerged macrophyte assessment in rivers: An automatic mapping method using Pleiades imagery. Water Res. 2020, 186, 116353. [Google Scholar] [CrossRef]
- Soria, J.M.; Vera-Herrera, L.; Calvo, S.; Romo, S.; Vicente, E.; Sahuquillo, M.; Sòria-Perpinyà, X. Residence time analysis in the Albufera of Valencia, a Mediterranean Coastal Lagoon, Spain. Hydrology 2021, 8, 37. [Google Scholar] [CrossRef]
- Xiao, C.; Wang, X.; Xia, J.; Liu, G. The effect of temperature, water level and burial depth on seed germination of Myriophyllum spicatum and Potamogeton malaianus. Aquat. Bot. 2010, 92, 28–32. [Google Scholar] [CrossRef]
- Directorate General for the Natural Environment and Environmental Assessment. Distribution and Conservation Status of Aquatic Macrophytes in Albufera de Valencia Lake (Report); Generalitat Valenciana: Valencia, Spain, 2017; (In Spanish). Available online: https://parquesnaturales.gva.es/documents/80302883/165126079/Distribucion+y+estado+de+conservacion+de+macrofitos+acuaticos+en+el+lago+de+l_Albufera+de+Valencia.pdf/040fb483-35d4-428d-8609-8126e6c6cbba?t=1514550696577 (accessed on 14 November 2023).
- American Public Health Association. Standard Methods for the Examination of Water and Wastewater, 23rd ed.; Bridgewater, L.L., Baird, R.B., Eaton, A.D., Rice, E.W., American Public Health Association, American Water Works Association, Water Environment Federation, Eds.; American Public Health Association: Washington, DC, USA, 2017; ISBN 978-0-87553-287-5. [Google Scholar]
- Crumpton, W.G.; Isenhart, T.M.; Mitchell, P.D. Nitrate and Organic N Analyses with Second-Derivative Spectroscopy. Limnol. Oceanogr. 1992, 37, 907–913. [Google Scholar] [CrossRef]
- Shoaf, W.T.; Lium, B.W. Improved extraction of chlorophyll a and b from algae using dimethyl sulfoxide. Limnol. Oceanogr. 1976, 21, 926–928. [Google Scholar] [CrossRef]
- Jeffrey, S.T.; Humphrey, G.F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem. Physiol. Pflanz. 1975, 167, 191–194. [Google Scholar] [CrossRef]
- Louis, J.; Debaecker, V.; Pflug, B.; Main-Knorn, M.; Bieniarz, J.; Mueller-Wilm, U.; Cadau, E.; Gascon, F. Sentinel-2 Sen2Cor: L2A processor for users. In Proceedings of the Living Planet Symposium, Prague, Czech Republic, 9–13 May 2016. [Google Scholar]
- Soria, X.; Delegido, J.; Urrego, E.P.; Pereira-Sandoval, M.; Vicente, E.; Ruíz-Verdú, A.; Moreno, J. Validación de algoritmos para la estimación de la clorofila-a con Sentinel-2 en la Albufera de València. In Proceedings of the XVII Congreso de la Asociación Española de Teledetección, Murcia, Spain, 3–7 October 2017; pp. 289–292. [Google Scholar]
- Ruescas, A.B.; Pereira-Sandoval, M.; Tenjo, C.; Rúiz-Verdú, A.; Steinmetz, F.; De Keukelaere, L. Sentinel-2 Atmospheric Correction Inter-comparison over two lakes in Spain and Peru-Bolivia. In Proceedings of the Colour and Light in the Ocean from Earth Observation (CLEO) Workshop, Frascati, Italy, 6–8 September 2016. [Google Scholar]
- Bernstein, L.S. Quick atmospheric correction code: Algorithm description and recent upgrades. Opt. Eng. 2012, 51, 111719. [Google Scholar] [CrossRef]
- Huete, A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Chen, J.M.; Black, T.A. Defining leaf-area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421–429. [Google Scholar] [CrossRef]
- European Spacial Agency (ESA). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services; Fletcher, K., Ed.; ESA Communications: Noordwijk, The Netherlands, 2012; p. 77. [Google Scholar]
- National Aeronautics and Space Administration (NASA). Landsat 8 Bands. Available online: https://landsat.gsfc.nasa.gov/satellites/landsat-8/landsat-8-bands/ (accessed on 23 December 2023).
- Jiang, Z.Y.; Huete, A.R.; Chen, J.; Chen, Y.H.; Li, J.; Yan, G.J.; Zhang, X.Y. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 2006, 101, 366–378. [Google Scholar] [CrossRef]
- Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Measurement of Turbidity and Total Suspended Matter in the Albufera of Valencia Lagoon (Spain) Using Sentinel-2 Images. J. Mar. Sci. Eng. 2023, 11, 1894. [Google Scholar] [CrossRef]
- Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain). Water 2023, 15, 3669. [Google Scholar] [CrossRef]
- Lodge, D.M. Herbivory on freshwater macrophytes. Aquat. Bot. 1991, 41, 195–224. [Google Scholar] [CrossRef]
- Wetzel, R.G. Limnology. In Lake and River Ecosystems; Academic Press: San Diego, CA, USA, 2001; p. 1006. [Google Scholar]
- Feldmann, T. The Structuring Role of Lake Conditions for Aquatic Macrophytes. Ph.D. Thesis, Estonian University of Life Sciences, Tartu, Estonia, 2012; p. 182. [Google Scholar]
- Bini, L.M.; Thomaz, S.M.; Murphy, K.J.; Camargo, A.F.M. Aquatic macrophyte distribution in relation to water and sediment conditions in the Itaipu Reservoir, Brazil. Hydrobiologia 1999, 415, 147–154. [Google Scholar] [CrossRef]
- Lougheed, V.L.; Crosbie, B.; Chow-Fraser, P. Primary determinants of macrophyte community structure in 62 marshes across the Great Lakes basin: Latitude, land use, and water quality effects. Can. J. Fish. Aquat. Sci. 2001, 5, 1603–1612. [Google Scholar] [CrossRef]
- Pandit, A.K. Freshwater biological resources of Kashmir Himalaya. In Natural Resources of Western Himalaya; Pandit, A.K., Ed.; Valley Book House: Srinagar, India, 2002; pp. 123–174. [Google Scholar]
- Sastroutomo, S.S. Environmental control of turion formation in curly pondweed, Potamogeton crispus L. Physiol. Plant. 1980, 49, 261–264. [Google Scholar] [CrossRef]
- Robledo, D.; Freile-Pelegrin, Y. Seasonal variation in photosynthesis and biochemical composition of Caulerpa spp. Bryopsidales, Chlorophyta) from the Gulf of Mexico. Phycologia 2005, 44, 312–319. [Google Scholar] [CrossRef]
- Barko, J.W.; Smart, R.N. Sediment related mechanism of growth limitation in submerged macrophytes. Ecology 1986, 67, 1328–1340. [Google Scholar] [CrossRef]
- Kaul, V.; Tristal, C.L.; Handoo, J.K. Distribution and production of macrophytes in some aquatic bodies of Kashmir. In Glimpses of Ecology; Singh, J.S., Gopal, B., Eds.; Prakash Publishers: Jaipur, India, 1978; pp. 313–334. [Google Scholar]
- Pandit, A.K. Biodiversity of wetlands in Kashmir Himalaya. Proc. Nat. Acad. Sci. USA 2008, 78, 29–51. [Google Scholar]
- Drew, M.C.; Lynch, J.M. Soil anaerobiosis, microorganisms, and root function. Annu. Rev. Phytopathol. 1980, 18, 37–66. [Google Scholar] [CrossRef]
- Barko, J.W.; Gunnison, D.; Carpenter, S.R. Sediment interactions with aquatic macrophytes in freshwater. Hydrobiologia 1991, 595, 9–26. [Google Scholar] [CrossRef]
- Boedeltje, G.; Smolders, A.J.P.; Roelofs, J.G.M.; Groenendael, J.M.V. Constructed shallow zonesalong navigation canals: Vegetation establishment and change in relation to environmental characteristics. Aquat. Conserv. Mar. Freshw. Ecosyst. 2001, 11, 453–0471. [Google Scholar] [CrossRef]
- Toivonen, H.; Huttunen, P. Aquatic macrophytes and ecological gradients in 57 small lakes in southern Finland. Aquat. Bot. 1995, 51, 197–221. [Google Scholar] [CrossRef]
- Alahuhta, J. Patterns of Aquatic Macrophytes in the Boreal Region: Implications for Spatial Scale Issues and Ecological Assessment. Ph.D. Thesis, University of Oulu, Oulu, Finland, 2011. [Google Scholar]
- Dennison, W.C. Effects of light on seagrass photosynthesis, growth and depth distribution. Aquat. Bot. 1987, 27, 15–26. [Google Scholar] [CrossRef]
- Sfriso, A.; Facca, C.; Ghetti, P.F. Temporal and spatial changes of macroalgae and phytoplankton in a Mediterranean coastal area: The Venice lagoon as a case study. Mar. Environ. Res. 2003, 56, 617–636. [Google Scholar] [CrossRef]
- Moore, K.A.; Wetzel, R.L. Seasonal variations in eelgrass (Zostera marina L.) responses to nutrient enrichment and reduced light availability in experimental ecosystems. J. Exp. Mar. Biol. Ecol. 2000, 244, 1–28. [Google Scholar] [CrossRef]
- Zharova, N.; Sfriso, A.; Voinov, A.; Pavoni, B. A simulation model for the annual fluctuation of Zostera marina biomass in the Venice lagoon. Aquat. Bot. 2001, 70, 135–150. [Google Scholar] [CrossRef]
- Christian, D.; Sheng, Y. Relative influence of various water quality parameters on light attenuation in Indian River Lagoon. Estuar. Coast. Shelf Sci. 2003, 57, 961–971. [Google Scholar] [CrossRef]
- Generalitat Valenciana (GVA). Evolución de los Macrófitos Acuáticos en el Lago de l’Albufera de Valencia y Relación con Variables Ambientales, Período 2015–2020; Informe técnico 04/2021; Generalitat Valenciana: Valencia, Spain, 2021.
- Lloret, J.; Marin, A.; Marin-Guirao, L.; Velasco, J. Changes in macrophytes distribution in a hypersaline coastal lagoon associated with the development of intensively irrigated agriculture. Ocean Coast. Manag. 2005, 48, 828–842. [Google Scholar] [CrossRef]
- Carpenter, S.R.; Adams, M.S. Effects of nutrients and temperature on decomposition of Myriophyllum spicatum L. in a hard-water eutrophic lake. Limnol. Oceanogr. 1979, 24, 520–528. [Google Scholar] [CrossRef]
- Silva, T.S.F. Imagens EOS-MODIS E Landsat 5 TM No Estudo Da Dinâmica Das Comunidades De Macrófitas Na Várzea Amazônica. Master’s Thesis, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil, 2004. [Google Scholar]
- Jensen, J.R.; Narumalani, S.; Weatherbee, O.; Mackey, J.H.E. Measurement of seasonal and yearly cattail and waterlily changes using multidate SPOT panchromatic data. Photogramm. Eng. Remote Sens. 1993, 59, 519–525. [Google Scholar]
- Powell, S.J.; Jakeman, A.; Croke, B. Can NDVI response indicate the effective flood extent in macrophyte dominated floodplain wetlands? Ecol. Indic. 2014, 45, 486–493. [Google Scholar] [CrossRef]
Landsat 8 | Sentinel-2 | ||||
---|---|---|---|---|---|
Bands | Objective | λc (nm) | Bands | Objective | λc (nm) |
B1 | Coastal aerosol | 443 | B1 | Coastal aerosol | 442 |
B2 | Blue | 482 | B2 | Blue | 492 |
B3 | Green | 562 | B3 | Green | 560 |
B4 | Red | 655 | B4 | Red | 665 |
B5 | NIR | 865 | B5 | Vegetation red edge | 705 |
B6 | SWIR | 1610 | B6 | 740 | |
B7 | 2200 | B7 | 783 | ||
B8 | Pan | 590 | B8 | NIR | 842 |
B9 | Cirrus | 1375 | B8A | 865 | |
B10 | TIR | 10,900 | B9 | 942 | |
B11 | 12,000 | B10 | SWIR | 1380 | |
B11 | 1610 | ||||
B12 | 2190 |
NDVI Range | Correspondence |
---|---|
<0.1 | Bare soil, water, and snow |
0.2–0.3 | Scrub and grassland (sparse vegetation) |
0.4–0.5 | Relatively thick and healthy vegetation |
0.6–0.8 | Dense temperate and tropical forests |
Date | Remote Sensing Estimated Data | Field Data (Plants Zone) | ||||||
---|---|---|---|---|---|---|---|---|
Satellite | M. spicatum Area (m2) | ZSD (m) | Turbidity (NTU) | TSS (mg/L) | Temperature | Nitrates (mg/L) | Chlorophyll-a (mg/m3) | |
12 March 2018 | S2A | 600 | 0.30 | 36.08 | 137.61 | |||
22 March 2018 | S2A | 1800 | 0.43 | 9.00 | 34.32 | |||
27 March 2018 | S2B | 6800 | 0.41 | 9.53 | 36.36 | 16.8 | 73.5 | |
5 April 2018 | L8 | 12,600 | ||||||
12 April 2018 | L8 | 13,500 | ||||||
28 April 2018 | L8 | 20,700 | ||||||
11 May 2018 | S2A | 28,300 | 0.27 | 23.67 | 90.29 | |||
21 May 2018 | S2A | 29,000 | 0.25 | 28.27 | 107.82 | 24.8 | 331.0 | |
8 June 2018 | L8 | 52,200 | ||||||
15 June 2018 | S2B | 57,000 | 0.28 | 15.49 | 58.96 | 25.1 | 6.66 | 101.5 |
20 June 2018 | S2A | 61,200 | 0.30 | 13.59 | 51.82 | 26.7 | 2.70 | 56.2 |
25 June 2018 | S2B | 52,300 | 0.32 | 19.21 | 73.27 | 27.7 | 2.75 | 104.6 |
05 July 2018 | S2B | 43,200 | 0.38 | 11.18 | 42.68 | 26.7 | 1.64 | 85.2 |
10 July 2018 | S2A | 33,000 | 0.45 | 5.06 | 19.30 | |||
15 July 2018 | S2B | 26,700 | 0.36 | 22.31 | 85.09 | 27.2 | 1.86 | 84.9 |
20 July 2018 | S2A | 31,400 | 0.38 | 22.68 | 86.48 | 28.2 | 3.28 | 71.6 |
30 July 2018 | S2A | 32,600 | 0.50 | 6.87 | 26.21 | |||
4 August 2018 | S2B | 23,100 | 0.54 | 3.80 | 14.49 | |||
11 August 2018 | L8 | 2700 | ||||||
14 August 2018 | S2B | 1100 | 0.50 | 5.78 | 22.04 | 30.1 | 2.04 | 46.2 |
19 August 2018 | S2A | 1200 | 0.54 | 3.15 | 12.02 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 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/).
Share and Cite
Soria, J.M.; Molner, J.V.; Pérez-González, R.; Alvado, B.; Vera-Herrera, L.; Romo, S. Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach. J. Mar. Sci. Eng. 2024, 12, 260. https://doi.org/10.3390/jmse12020260
Soria JM, Molner JV, Pérez-González R, Alvado B, Vera-Herrera L, Romo S. Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach. Journal of Marine Science and Engineering. 2024; 12(2):260. https://doi.org/10.3390/jmse12020260
Chicago/Turabian StyleSoria, Juan M., Juan Víctor Molner, Rebeca Pérez-González, Bárbara Alvado, Lucía Vera-Herrera, and Susana Romo. 2024. "Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach" Journal of Marine Science and Engineering 12, no. 2: 260. https://doi.org/10.3390/jmse12020260
APA StyleSoria, J. M., Molner, J. V., Pérez-González, R., Alvado, B., Vera-Herrera, L., & Romo, S. (2024). Monitoring the Extraordinary Ephemeral Emergence of Myriophyllum spicatum L. in the Coastal Lagoon Albufera of Valencia (Spain) and Assessing the Impact of Environmental Variables Using a Remote Sensing Approach. Journal of Marine Science and Engineering, 12(2), 260. https://doi.org/10.3390/jmse12020260