Mangrove forests support the livelihoods of millions of people by providing both direct and indirect goods and services, such as construction materials, fuel, protection from storm surges and consistent water quality. Despite the plethora of ecosystem services that they provide, mangrove forests are threatened across their range by direct human activity such as conversion to agriculture and aquaculture and indirect anthropogenic impacts caused by climate change (e.g. Sea Level Rise). Mangrove forests have lost between 35% and 50% of their extent over the last century. National rates of loss reach 8% in some mangrove-holding countries. However, these estimates are highly uncertain due to a disparate collection of published data, with inconsistent methodologies, quality and accuracy. Therefore there is a critical need for systematic and consistent estimates of historic and contemporary global mangrove land cover and land use change.

We propose a Multi-Source Land Imaging (MuSLI) approach to quantify historical and current drivers of human-induced mangrove forest “hotspots” in the coastal zone. Specifically, our objectives are to:

1) Quantify the global change in mangrove extent from the 1970’s to present;

2) Quantify mangrove degradation and regeneration in change hotspots;

3) Identify direct anthropogenic proximate drivers of change in change hotspots resulting from commodities and agricultural use (e.g. Aquaculture/rice), pollution (e.g. oil spill, mining) and Logging (e.g. degradation and/or clearing) or natural/climate change-related trends (e.g. shoreline erosion, storms and cyclones)

4) Develop a framework for assessing future global mangrove vulnerability.

Our proposal builds on years of research using remote sensing to map and monitor mangrove forests locally and globally. Previous work by our team has mapped mangrove structure globally, mangrove gains and losses and mangrove range expansion. In addition, our team qualitatively identified change hotspots for the 1996-2010.  We propose to use our existing Landsat-based mangrove change algorithms to map global land cover and land use change (LCLUC) back to the beginning of the Landsat archive (1970’s) and identify historical and contemporary hotspots of change. Within selected hotspot regions, we will do an in-depth analysis of change that combines an object-oriented method to characterize landscape features and a decision tree approach to identify drivers. Moderate resolution imagery (e.g., Landsat) will provide information on long-term LCLUC, while high and Very High Resolution (VHR) passive optical will be used to develop methods for monitoring subpixel changes in canopy cover from Landsat imagery to quantify degradation and regeneration. Finally, in these hotspot regions, we will develop relationships between the occurrence of LCLUC and their drivers, and the forest vertical structure (with radar and VHR optical data) and environmental setting.

Our proposal directly addresses the NASA LCLUC solicitation for using Multi-Source Land Imaging to identify high impact LCLUC hotspot areas around the globe where human-induced LCLUC is occurring at a landscape scale. We propose to use a large and diverse set of optical and radar remote sensing platforms with moderate to very high resolution.  Moreover, our investigation identifies proximate drivers of changes that include aquaculture, agriculture, forestry and urban expansion within the coastal zone where mangrove forests thrive. We address this solicitation’s emphasis using proven remote sensing methodologies that can be applied on a global scale to monitor coastal ecosystems that provide livelihood to millions of people. Thus, we expect our results and data products to provide a strong and reliable baseline to support socio-economic research and restoration projects in mangrove regions across the world.