Resumen
Classical survey methods involve making in situ measurements of shoreline locations, which can never scale to the time spans and spatial extent necessary to cover large coastal areas. For 50 years now, satellites have provided images of the earth with reasonable resolution that can provide some insight into shoreline evolution over large timescales and wide areas. Determining shoreline location from satellite imagery involves a general pipeline of image pre-processing, segmentation of the image into water and beach, and then determination of the shoreline location. The goal of the research here is to expand the capabilities of CoastSat to include identification of coastal vegetation and tracking of vegetated shoreline evolution. Accurate measurements of coastal vegetation extent and evolution from satellites may help our understanding of the health of shoreline ecosystems as sea levels rise and storms become more intense. Shoreline trends observed from satellite imagery will provide information to coastal communities that can be used for effective spatial planning, sustainable coastal development, coastal engineering projects, and mitigation of climate change impacts.Referencias
Hossain (2016): Earth Science Satellite Applications: Current and Future Prospects: Springer.
Wulder, Loveland, Roy, Crawford, Masek, Woodcock, Allen, Anderson, Belward, Cohen and Dwyer, (2019): Current status of Landsat program, science, and applications. Remote sensing of environment, vol 225, pp 127-147.
Luijendijk, Hagenaars, Ranasinghe, Baart, Donchyts, Aarninkhof (2018): The State of the World’s Beaches: Scientific Reports, vol 8, pp 1-11.
Cenci, Disperati, Persichillo, Oliveira, Alves, Phillips (2018): Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management: GIScience & Remote Sensing, vol 55, pp 355-375.
Vos, Harley, Splinter, Simmons, Turner (2019): Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery: Coastal Engineering, vol 150, pp 160-174.
Vos, Splinter, Harley, Simmons, Turner (2019): CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery: Environmental Modeling & Software, vol 122.
McAllister, Payo, Novellino, Dolphin, Medina-Lopez (2022): Multispectral satellite imagery and machine learning for the extraction of shoreline indicators: Coastal Engineering, vol 174.
Feagin, Furman, Salgado, Martinez, Innocenti, Eubanks, Figlus, Huff, Sigren, Silva (2019): The role of beach and sand dune vegetation in mediating wave run up erosion: Estuarine, Coastal and Shelf Science, vol 219, pp 97-106.
Gracia, Rangel-Buitrago, Oakley, Williams (2018): Use of ecosystems in coastal erosion management: Ocean & Coastal Management vol 156, pp 277-289.
Dissanayake, Brown, Karunarathna (2015): Impacts of storm chronology on the morphological changes of the Formby beach and dune system, UK: Natural Hazards Earth System Science, vol 15, pp 1533–1543.
Nandi, Ghosh, Kundu, Dutta, Baksi (2016): Shoreline shifting and its prediction using remote sensing and GIS techniques: a case study of Sagar Island, West Bengal (India): Journal of Coastal Conservation, vol 20, pp 61-8.
Gong, Newman (2016): Dual Marching Squares: Description and Analysis: In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp 53-56.
Liu and Jezek (2004): Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. International journal of remote sensing, vo. 25, pp 937-958.
Bhandari, Kumar, Singh (2012): Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City: Procedia Technology, vol 6, pp 612–621.
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2023 Adriana Lanza, Ryan M. Sullenberger, Justin G. Chen, Julia Hopkins,