The Interplay between Land User Decisions and Landcover Change in Coastal Ecosystems and Working Lands Under Sea Level Rise in the Mid-Atlantic U.S.
Elizabeth A. Hunter, Virginia Polytechnic Institute and State University, ehunter1@vt.edu (Presenter)
Ashley A. Dayer, Virginia Polytechnic Institute and State University, dayer@vt.edu
Valerie A. Thomas, Virginia Polytechnic Institute and State University, thomasv@vt.edu
Sea level rise (SLR) will reshape coastal ecosystems and human communities across multiple scales. Whether natural ecosystems migrate upslope as a response to SLR may ultimately be determined by the interplay between ecological responses to SLR and human decision-making. Understanding these processes is important to identify potential hotspots of change and inform land-use adaptation to climate change and SLR in the coming decades. Here we focus on how SLR influences the land cover dynamics between natural ecosystems (marshes, tidal flats) and working lands, which we define as agriculture and forestry land uses, on the U.S. mid-Atlantic coastal plain. Our objectives are: 1. Identify where SLR-caused landcover change and salinization on working lands is occurring and whether this change can be detected by satellite remote sensing on a yearly time scale. 2. Estimate whether the proximity or severity of SLR-caused coastal landcover change influences landowner decisions. 3. Estimate whether coastal landowner decisions influence land cover of natural ecosystems. We will investigate areas of land cover change in the transition zone, where larger signals can be detected via Sentinel-2 (in combination with historic change from Landsat), as well as 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 that will be combined in a machine learning model to identify change 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, 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 agent-based model (ABM). We will create scenarios of SLR and economic pressures on landowners and project ABM estimates of relationships between landcover and landowner decisions to predict the type and degree of landcover change.
Associated Project(s):
Poster Location ID: 2-46
Presentation Type: Poster
Session: Poster Session 2
Session Date: Wed (May 10) 5:15-7:15 PM
CCE Program: LCLUC