Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm

Mangroves play an important role in protecting coasts against wave energy and storms. Mangrove ecosystems provide important habitats for fauna and flora and are an important carbon sink. Loss of mangroves forest may lead to enhanced coastal erosion. Mangroves are complex ecosystems and processes of settling and development are not fully understood. Characterizing the rates and patterns of mangrove gains and losses is needed to better understand the functioning of mangrove ecosystems, how mangrove dynamics are linked to coastal morphological behaviour and how human interference with the coastal system impacts mangroves. Here we present a study of the mangrove ecosystems at the Suriname coast, which are relatively pristine and characterized by strong dynamics due to migrating mudbanks along the coast. Satellite images between 2000 and 2018, available in the historic satellite image archives, were analysed using the LandTrendr (Landsat-based detection of trends in disturbance and recovery) algorithm to identify locations of mangrove erosion, mangrove colonization, surface areas of change and patterns of settlement, as indicated by (sudden) changes in NDVI. The algorithm requires careful setting of various parameters for successful detection of (abrupt) temporal changes in mangrove coverage. The algorithm was evaluated on its robustness using various parameter settings. Results show the value of the timeseries of Landsat imagery to detect locations of coastal erosion of up to 50 m/yr and accretion where loss or settlement of mangroves is prevailing between 2000 and 2018. Locally differences are very large. An overall westward mangrove progression along the coast is apparent from the images and probably linked to mud bank migration. Various patterns of mangrove colonization and development such as arc-, zonaland patcharrangements were identified, although at some locations the Landsat resolution of 30 m is somewhat coarse to allow detailed analysis. The success and robustness of the LandTrendr algorithm are controlled by NDVI threshold values, number of allowed breakpoints in the timeseries and fitting parameters. The presented method requires further testing and evaluation but is a promising tool for semi-automatic detection of coastal mangrove erosion and colonization that can be applied to other mangrove ecosystems in the world. The satellite timeseries analyses generate valuable information on coastal dynamics, which is helpful to identify coastal areas prone to erosion and mangrove retreat and provide as such a valuable tool for coastal management and protection.