Long-Term Monitoring of Coastal Dunes in Rio Grande do Sul Southern Brazil: A Decadal Time Series of Satellite Imagery

Albuquerque, M.G.; Freitas, J.G.; Pereira, B.M.; Santos, J.R., and Souza, V.G., 2024. Long-term monitoring of coastal dunes in Rio Grande do Sul southern Brazil: A decadal time series of satellite imagery. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 499-503. Charlotte (North Carolina), ISSN 0749-0208. Dunes are integral to the functioning of coastal systems and provide valuable socio-economic services, including protection against extreme events. Researchers and coastal managers face the challenge of understanding and monitoring dune fields through medium- and long-term in situ measurements. The objective of this study is to demonstrate the potential of Google Earth Engine (GEE) for monitoring coastal dunes in Rio Grande do Sul, southern Brazil over a decade. The methodology employed was based on Landsat 8 sensor satellite images from 2013 to 2023. To process the images, a customized algorithm was developed to be executed in GEE, based on a spectral index for spatio-temporal mapping of dunes. The results show that an average of 110 thousand hectares of dunes were lost in 10 years, corresponding to a dune area reduction of 13.14% along 615 km of coastline. The largest losses and gains in dune area during this period were in 2015 (decrease of 19.30%) and 2018 (increase of 38.82%). The southern coast suffered the greatest loss (6.42% in 225 km), while the northern and central coasts lost 4.0% in 198 km and 2.72% in 192 km, respectively. The main factors associated with the decrease of dune area are the expansion of pine trees, especially on the central and southern coasts; the occurrence of extreme events, and human activities. These phenomena influence the complex dynamics of the dune environment, making long-term monitoring highly relevant to understand seasonal trend changes and to support management and conservation policies.