Hypersaline Tidal Flats Detection Using Deep Learning Over 37 Years of Landsat Data

Hypersaline tidal flats are plane areas usually related to mangrove forests, acting as guard and buffer against rising sea levels, and as maintainer of regional biodiversity. Such areas are primarily impacted by anthropogenic and natural activities, such as sea-salt extraction and pollution, so identifying and monitoring them is an important and challenging task. The present work uses a U-shaped Convolutional Neural Network architecture to systematically classify such formations over Landsat imagery. A large dataset containing data from 1985 to 2021 of the Brazilian Coastal Zone is used to train and evaluate our model. Experimental results show that the total area increased by 58.6 km
²
from 1985 to 2001, and decreased by approximately 92 km
²
from 2001 to 2021, representing a total reduction of ≈ 33.34 km
²
for the entire period. We also show that our model outperforms a related solution trained with the same dataset, achieving 70% and 86% for 1985 and 2020 respectively, against 69% and 82%.