Simple Machine Learning with Aerial Imagery Reveals Severe Loss of a Salt Marsh Foundation Species

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There attempts broadly quantify salt marsh communities, especially ubiquitous grasses which serve as foundation species such Spartina alterniflora and patens, latter is being sea-level rise. However, few researchers used high-resolution geospatial imagery fine-scale changes in distribution track losses S. patens. To address this issue, we utilized a simple rapid method classifying with cloud-based machine learning Google Earth Engine (> 94.59% accuracy for patens across all models 2006 2019). Our methods allowed us characterize large landscapes (two geospatially proximal areas, > 7000 ha each) critical on New Jersey coast evaluate (1 m) community transformations response change from 2019. Notably, one experienced very little while other an 81.17% (1087 ha) loss illuminating disparate patterns two geographically ecosystems. Further exploration revealed association increases streamflow total nitrogen content rivers that run through each marsh. These results signify importance broad-scale ecological studies management strategies do not generalize ecosystem type.