Sandy beach area estimation through open access satellite information: A calibration for the coast of Montevideo, Uruguay
DOI:
https://doi.org/10.25268/bimc.invemar.2023.52.1.1144Keywords:
coastal erosion, ecosystem management, Landsat, open access, sandy beachAbstract
Sandy beaches provide a variety of ecosystem services that support human well-being at coastal areas. These ecosystems are highly dynamic and primarily defined by the interaction between waves, tides and wind regimes. High variability makes beaches vulnerable to physical modifications and climate change, jeopardizing ecosystems functions. This has resulted in accelerated erosion rates and
ecological degradation with widespread socioeconomic implications. Coastal dynamic analysis is a data demanding process that requires long term monitoring programs; this study applies an open access methodology to the Landsat collection in order to estimate beach area. This informative variable can help elucidate coastal dynamics, ecosystem attributes and touristic potential. Sand (through Random forest classification) and vegetation (through a threshold of the normalized difference vegetation index) were considered as components of the beach ecosystem. The method was calibrated for the Montevideo coast by testing results against independent estimations of beach area,
the area of 20 beaches of the Montevideo coast was estimated for a 35 years’ period. This methodology can be applied anywhere at a very low operational cost, potentially multiplying the available information and allowing better management on the pressing matters of coastal dynamics and sandy beach use.
References
Amyot, J. and J. Grant. 2014. Environmental function analysis: A decision support tool for integrated sandy beach planning. Ocean Coast Manag, 102: 317–327. https://doi.org/10.1016/j.ocecoaman.2014.10.009
Barnard, P.L., D.M. Hubbard and J.E. Dugan. 2012. Beach response dynamics of a littoral cell using a 17-year single-point time series of sand thickness. Geomorphology 140: 588–598. https://doi.org/10.1016/j.geomorph.2011.12.023
Barnard, P.L., A.D. Short, M.D. Harley, K.D. Splinter, S. Vitousek, I.L. Turner, J. Allan, M. Banno, K.R. Bryan, A. Doria, J.E. Hansen, S. Kato, Y. Kuriyama, P. Randall-Goodwin, P. Ruggiero, I.J. Walker and D.K. Heathfield. 2015. Coastal vulnerability across the Pacific dominated by El Niño/Southern Oscillation. Nat. Geosci., 8: 801.
Boak, E. and I. Turner. 2005. Shoreline definition and detection: A review. J. Coast. Res., 21: 688–703. https://doi.org/10.2112/03-0071.1
Breiman, L. 2001. Random forests. Mach Learn, 45: 5–32.
Brieuc, M.S.O., C.D. Waters, D.P. Drinan and K.A. Naish. 2018. A practical introduction to Random Forest for genetic association studies in ecology and evolution. Mol. Ecol. Resour., 18(4): 755–766. https://doi.org/10.1111/1755-0998.12773
César de Sá, N., S. Carvalho, P. Castro, E., Marchante and H. Marchante. 2017. Using Landsat time series to understand how management and disturbances influence the expansion of an invasive tree. IEEE J. Sel. Topics App. Earth Obs. Rem. Sens., 10(7): 3243–3253.
Cifuentes Ossa, M.A., L.V. Rosero Henao y J.J. Selvaraj. 2017. Detección de cambios de la línea costera al norte del distrito de Buenaventura mediante el uso de sensores remotos. Bol. Invest. Mar. Costeras, 46. https://doi.org/10.25268/bimc.invemar.2017.46.1.719
Defeo, O. and M. Elliott. 2020. The ‘triple whammy’ of coasts under threat – Why we should be worried! Mar. Pollut. Bull., 111832. https://doi.org/10.1016/j.marpolbul.2020.111832
Defeo, O., A. McLachlan, D.S. Schoeman, T.A. Schlacher, J. Dugan, A. Jones, M. Lastra and F. Scapini. 2009. Threats to sandy beach ecosystems: A review. Estuar. Coast. Shelf Sci., 81: 1–12. https://doi.org/10.1016/j.ecss.2008.09.022
García-Alonso, J., D. Lercari, B.F. Araujo, M.G. Almeida and C.E. Rezende.2017. Total and extractable elemental composition of the intertidal estuarine biofilm of the Río de la Plata: Disentangling natural and anthropogenic influences. Estuar. Coast. Shelf Sci., 187: 53–61. https://doi.org/10.1016/j.ecss.2016.12.018
Gao, B. 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Rem. Sens. Environ., 58: 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau and R. Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Rem. Sens. Environ., 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Gutiérrez, O. 2010. Dinámica sedimentaria en la costa uruguaya: evolución y tendencias de playas urbanas en el marco del Cambio Global. Tesis Magíster Ciencias Ambientales, UdelaR, Montevideo. 98 p. https://doi.org/10.13140/RG.2.1.1904.4568
Gutiérrez, O., D. Panario, G.J. Nagy, G. Piñeiro and C. Montes. 2015. Long-term morphological evolution of urban pocket beaches in Montevideo (Uruguay): Impacts of coastal interventions and links to climate forcing. J. Integr. Coast. Zone Manag., 15: 467-484. https://doi.org/10.5894/rgci553
Gutiérrez, O., D. Panario, G.J. Nagy, M. Bidegain and C. Montes. 2016. Climate teleconnections and indicators of coastal systems response. Ocean Coast. Manag., 122: 64–76. https://doi.org/10.1016/j.ocecoaman.2016.01.009
Harley, M.D., I.L Turner, A.D. Short and R. Ranasinghe. 2010. Assessment and integration of conventional, RTK-GPS and image-derived beach survey methods for daily to decadal coastal monitoring. Coast. Eng, 58: 194–205. https://doi.org/10.1016/j.coastaleng.2010.09.006
Harris, L., R. Nel and D. Schoeman. 2011. Mapping beach morphodynamics remotely: A novel application tested on South African sandy shores. Estuar. Coast. Shelf Sci., 92: 78–89. https://doi.org/10.1016/j.ecss.2010.12.013
Jensen, J.R. 2005. Introductory digital image processing: A remote sensing perspective. Prentice Hall, Upper Saddle River. 512 p.
Lercari, D. and O. Defeo. 2015. Large-scale dynamics of sandy beach ecosystems in transitional waters of the southwestern Atlantic Ocean: Species turnover, stability and spatial synchrony. Estuar. Coast. Shelf Sci., 154: 184–193. https://doi.org/10.1016/j.ecss.2015.01.011
Luijendijk, A., G. Hagenaars, R. Ranasinghe, F. Baart, G. Donchyts and S. Aarninkhof. 2018. The state of the world’s beaches. Sci. Rep., 8: 6641. https://doi.org/10.1038/s41598-018-24630-6
McLachlan, A. and O. Defeo. 2018. The ecology of sandy shores. Academic Press, London. 542 p. https://doi.org/10.1016/B978-0-12-809467-9.00001-1
McLachlan, A., O. Defeo, E. Jaramillo and A.D. Short. 2013. Sandy beach conservation and recreation: Guidelines for optimizing management strategies for multi-purpose use. Ocean Coast. Manag., 71: 256–268. https://doi.org/10.1016/j.ocecoaman.2012.10.005
McLachlan, A., O. Defeo and A.D. Short. 2018. Characterizing sandy beaches into major types and states: Implications for ecologists and managers. Estuar. Coast. Shelf Sci., 215: 152–160. https://doi.org/10.1016/j.ecss.2018.09.027
Millard, K. and M. Richardson. 2015. On the importance of training data sample selection in Random Forest image classification: A case study in peatland ecosystem mapping. Rem. Sens., 7: 8489–8515.
Orlando, L. 2020. BeachAreaMontevideo1984_2019 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4327667
Orlando, L., L. Ortega and O. Defeo. 2019. Multi-decadal variability in sandy beach area and the role of climate forcing. Estuar. Coast. Shelf Sci., 218: 197–203. https://doi.org/10.1016/j.ecss.2018.12.015
Orlando, L., L. Ortega and O. Defeo. 2020. Urbanization effects on sandy beach macrofauna along an estuarine gradient. Ecol. Ind., 111: 106036. https://doi.org/10.1016/j.ecolind.2019.106036
Orlando, L., L. Ortega and O. Defeo. 2021. Perspectives for sandy beach management in the Anthropocene: Satellite information, tourism seasonality, and expert recommendations. Estuar. Coast. Shelf Sci., 107597. https://doi.org/10.1016/j.ecss.2021.107597
Ozturk, D. and F. Sesli. 2015. Shoreline change analysis of the Kizilirmak Lagoon series. Ocean Coast. Manag., 118: 290–308. https://doi.org/10.1016/j.ocecoaman.2015.03.009
R Development Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://cran.r-project.org/ 8/09/2020.
Sepúlveda, H.H., A. Valle-Levinson and M.B. Framiñán. 2004. Observations of subtidal and tidal flow in the Rı́o de la Plata Estuary. Cont. Shelf Res., 24:509–525. https://doi.org/10.1016/j.csr.2003.12.002
Short, A.D. 1999. Beach and shoreface morphodynamics. Wiley, Chichester. 379 p.
Short, A.D. and D.W.T. Jackson. 2013. Beach morphodynamics: 106-129. In: Treatise on Geomorphology. Academic Press, San Diego.
Simionato, C.G., M.L. Clara Tejedor, C. Campetella, R. Guerrero and D. Moreira. 2010. Patterns of sea surface temperature variability on seasonal to subannual scales at and offshore the Río de la Plata estuary. Cont. Shelf Res., 30: 1983–1997. https://doi.org/10.1016/j.csr.2010.09.012
Splinter, K.D., I.L. Turner and M.A. Davidson. 2013. How much data is enough? The importance of morphological sampling interval and duration for calibration of empirical shoreline models. Coast. Eng., 77: 14–27. https://doi.org/10.1016/j.coastaleng.2013.02.009
Takimoto, G. and D.M. Post. 2013. Environmental determinants of food-chain length: a meta-analysis. Ecol. Res., 28: 675–681. https://doi.org/10.1007/s11284-012-0943-7
Verocai, J.E., M. Gómez-Erache, G.J. Nagy and M. Bidegain. 2015. Addressing climate extremes in Coastal Management: The case of the Uruguayan coast of the Rio de la Plata System. J. Integr. Coast. Zone Manag., 15:91-107. https://doi.org/10.5894/rgci555
Vos, K., M.D. Harley, K.D. Splinter, J.A. Simmons and I.L. Turner. 2019. Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coast. Eng., 150: 160–174. https://doi.org/10.1016/j.coastaleng.2019.04.004
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Luis Orlando
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
