A history of the rehabilitation of mangroves and an assessment of their diversity and structure using Landsat annual composites (1987–2019) and transect plot inventories

Abstract

Recently, there has been renewed interest in the ecosystem services of mangroves such as carbon sequestration or coastal protection, and consequently, the development of tools providing an effective and automatic monitoring of the dynamics of mangrove land coverage including rehabilitated or naturally regenerated forest stand is increasingly demanded. Satellite-based time series analysis in coastal areas can be limited by atmospheric contaminations, such as haze, and clouds and their shadows. Here, we present an “automatic regrowth monitoring algorithm” (ARMA) using the Google Earth Engine (GEE), based on Landsat interannual median composites from 1987 to 2019 with 30 m spatial resolution. The species and structural diversity were assessed using transect plot inventories. The Landsat-based normalized difference infrared index (NDII) and information obtained from plot inventories were used to assess the characteristics of the natural and rehabilitated mangrove forests. The ARMA identified the starting year of the rehabilitation project using the satellite data, the required stability period after the rehabilitation, and the stand age in the year 2019. The information obtained from the field survey data were linked to the results obtained using the ARMA. After 28 years, the rehabilitated mangroves at the study site consist of monocultures of Rhizophoraceae, while the undisturbed and naturally regenerated mangroves had greater species diversity. Nevertheless, the rehabilitated mangroves were found to reach the height of the adjacent natural mangroves. The period required to reach a stable NDII value (similar to natural stands) after rehabilitation ranged from 7 to 13 years. The careful assessment of the NDII upward trend was crucial for the performance of the ARMA. The application presented here shows, however, that the system can be used to evaluate both small- and large-scale rehabilitation projects. The results of this study provide valuable baseline information for the site assessed and for its comparison with other rehabilitated mangroves in Thailand. Due to the technical potential, we are convinced that the ARMA system is suited to investigate changes in mangrove coverage dynamics, in general, including gain (as presented here), but also mangrove losses, due to disturbances such as degradation or forest diebacks.

Isabella Hinks

she/her/hers
PhD Candidate, 2020-

I am a Ph.D. candidate at the Center for Geospatial Analytics at NC State University.