Erosion and progradation are natural dynamics that take place across mangrove landscapes, but anthropogenic pressure may disturb these dynamics and cause increased erosion. The increased erosion of mangrove coastlines leads to mangrove degradation and loss of ecosystem services. However, to map the extent of erosion and progradation in dynamic landscapes is often time and data consuming and challenging to perform at a large scale. This study uses cloud computing-based remote sensing analysis in Google Earth Engine to map the extent of mangrove shoreline dynamics in two contrasting hotspots: the Sundarbans mangrove complex across India and Bangladesh, and the coast of French Guiana. Mapping was performed between 1984 and 2018 using available data on mangrove distribution and surface water change, classifying change of states as permanent, seasonal and ephemeral. Between 1984 and 2018, erosion and progradation accounted for 24.55% and 12.52% of total change, respectively. Contrastingly, in French Guiana erosion and progradation accounted for 9.53% and 4.48% of total change, respectively. The Sundarbans experienced more permanent changes as compared to French Guiana. The permanent loss of mangrove forests in the Sundarbans can be attributed to the reduction in sediment supply and the temporary loss in French Guiana to cyclical patterns of migrating mudbanks. The methods presented in this study performed with an overall accuracy above 90% in different settings of both the case studies. This study provides an important baseline of mangrove erosion and progradation extent and an improved method to analyse coastal dynamics in mangrove landscapes.

coastal accretion, coastal erosion, Google Earth Engine, mangrove loss, water classification
Estuarine, Coastal and Shelf Science
Department of Geography and Environmental Studies

Bhargava, R. (Radhika), Sarkar, D, & Friess, D.A. (Daniel A.). (2020). A cloud computing-based approach to mapping mangrove erosion and progradation: Case studies from the Sundarbans and French Guiana. Estuarine, Coastal and Shelf Science. doi:10.1016/j.ecss.2020.106798