THE CHALLENGE
Land cover/land use change (LCLUC) is driven for a host of biophysical and socio-economic factors, some local and some external to islands. Understanding the pattern and process relationships that influence LCLUC decisions are important to island sustainability. Deforestation, urban development, agricultural extensification, globalization, and natural hazards are important drivers of LCLUC.
KEY QUESTIONS
- Which satellite systems, and their corresponding spatial, spectral, and temporal resolutions, are most important for mapping and monitoring LCLUC patterns for remote island settings?
- What are the critical drivers of LCLUC, how might they be characterized, and how can multiple space-based platforms by fused for maximum interpretation?
People:
Steve Walsh, Francisco Laso, Richard Bilsborrow, Brian Frizzelle, Laura Brewington, Yang Shao, Hernando Mattei, and Phil Page.
This project is designed to synthesize land cover/land use change (LCLUC) patterns on islands and to assess the drivers of change. We approach this analysis in five steps: (1) First, we are performing a meta-analysis of global islands by focusing on the socio-economic, geographic, and biophysical drivers of LCLUC, and we assessing the role of satellite remote sensing for characterizing the composition, pattern, and structure of LCLUC on islands; (2) Next, Primary Island Sites (Hawaiian Islands, Galapagos Islands, Puerto Rico) will be characterized by an assembled social-ecological data set, including population census data, tourism data, environmental data, and a blended multi-resolution, satellite image stack; (3) We will then develop a dynamic systems model of the Primary Island Sites, informed through statistical analyses and case study models, and determine if a single synthetic model can be generated that is capable of capturing social-ecological dynamics to understand the drivers of LCLUC on each island; (4) Once the operating protocols have been tested and dynamic system models developed for the Primary Island Sites, we will perform sensitivity analyses to assess model performance by varying data inputs of stocks, flows, rates of exchange, and feedback loops to determine the impacts of variables on model outcomes; (5) Kast, we will expand the analysis of the generalizability of the model by fitting it to Maui Island, Hawaii using population census data, tourism data, environmental data GIS data layers, and satellite assets to assess LCLUC patterns and the drivers of change. Through the analysis of the Primary and Secondary Island Sites, we explain the impacts of tourism, demographics, environmental, and economic transitions on broader LCLUC issues, decision-making, and the politics of change.
This research is funded by the National Aeronautics and Space Administration (NASA).