We will target the transition zone between marshes and upland agriculture and forestry lands in selected focal areas from new jersey to georgia (a region with a high proportion of working lands near…

Virginia Polytechnic Institute & State University (Virginia Tech) was awarded an $796K research grant by a civilian federal agency to study land cover change in the transition zone between marshes and upland agriculture and forestry lands from New Jersey to Georgia. The project combines remote sensing analysis using Sentinel-2 and Landsat satellite imagery with high-resolution imagery from NAIP and Maxar to detect both large-scale land cover changes and subtle degradation caused by sea level rise and salinization. Virginia Tech will employ machine learning models incorporating Fourier curve fitting to identify areas of concern, then use the ParcelPoint dataset to identify affected landowners and conduct targeted surveys to understand how environmental and economic factors influence their land management decisions and behaviors.

The research integrates remote sensing findings with social science data into a predictive structural equation model that will be validated against withheld datasets and used to project future land cover changes under various sea level rise and economic pressure scenarios. This work addresses the pressing challenge facing the U.S. Mid-Atlantic region, where relative sea level rise rates of 3-4 millimeters per year—exacerbated by local land subsidence—significantly exceed global averages. The project has no set-aside designation and is scheduled for completion on May 9, 2026, approximately three years from the May 2023 award date. No subcontractors were identified in the available award documentation.

We will target the transition zone between marshes and upland agriculture and forestry lands in selected focal areas from new jersey to georgia (a region with a high proportion of working lands near coastal ecosystems and shallow topography). We are interested in two main processes: 1. Areas of land cover change in the transition zone, which are larger signals that can be detected via sentinel-2 (in combination with historic change from landsat), and 2. Change in land condition due to SLR and salinization that may lead to a change in land use. We will assess both the magnitude and periodicity of the spectral response through fourier curve fitting of the sentinel-2 response combined with very high-resolution imagery (i.e., naip for historic analysis and maxar for more recent change), that will be combined in a machine learning model to identify degradation in the wetland/upland transition zone. After model identification of areas with a range of land cover changes, we will identify landowners for those pieces of land (using the parcelpoint dataset of landowners) and conduct targeted surveys to identify how the degree of change influences behaviors and decisions, as well as what other factors modulate or constrain those decisions (e.g., tax rates, market value for products). We will integrate both components (remote sensing model and analyses of landowner behaviors and decisions) in a predictive structural equation model (SEM), that we will validate with withheld data. We will create scenarios of SLR and economic pressures on landowners and project SEM estimates of relationships between landcover and landowner decisions to predict the type and degree of landcover change. SLR is a pressing societal issue in this region the U.S. Mid-atlantic is experiencing greater rates of SLR (3-4 MM/YR.) compared to the global average (1-2 MM/YR.) because local land subsidence exacerbates climate change induced SLR. Landowners have several potential options for how to respond: by retreating (e.g., selling agriculture or forestry lands to housing developers), resisting (e.g., installing infrastructure to forestall inundation on productive land), or adapting (e.g., switching land use to accommodate flooding and salinization from SLR) to changes. These decisions will have consequences for both the upland land use, as well as the landcover of the adjacent natural system (e.g., sea walls typically lead to degradation of adjacent natural ecosystems). By making these social-ecological connections, we will be able to make better predictions of how land cover will change as a function both of direct SLR impacts and human responses to them.