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

Abstract

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

Land-cover change in Cuba and implications for the area of distribution of a specialist’s host-plant

“…GEE, is a cloud geospatial processing platform on Google computational infrastructure, used in different studies with spatial and temporal scales ( Gorelick et al, 2017 ). This platform has been used in studies exploring vegetation succession ( Adagbasa & Mukwada, 2022 ), species distribution ( Crego, Stabach & Connette, 2022 ; Crego et al, 2023 ), and to characterize large landscapes ( Rippel et al, 2023 ). GEE has been also used to map deforestation and forest degradation ( Shimizu et al, 2022 ; Wimberly et al, 2022 ), predict effects of climate change ( Workie & Debella, 2018 ; Shiff, Lensky & Bonfil, 2021 ), assess the impacts of wildfires ( dos Santos et al, 2023 ; Parra et al, 2023 ), and detect changes in land use and land cover ( Phan, Kuch & Lehnert, 2020 ; Tassi et al, 2021 ; González-González, Clerici & Quesada, 2022 ; Biswas et al, 2023 ).…”