Integrating Remote Sensing and Field Measurements to Identify Environmental Nonstationarity on Interior Alaska DoD Training Lands
Jennifer Watts | The Woods Hole Research Center
The objective of this project is to develop an environmental change analysis framework integrating decadal, multi-scale satellite remote sensing and field observations to identify and characterize abrupt and longer-term ecosystem shifts across Department of Defense (DoD) lands. The research team will do this by applying statistical time series change detection and down-scaling methods to critical Earth System Indicators. This work will improve the understanding of non-stationary across spatial and temporal scales, and will identify terrains on DoD lands that are most vulnerable to change under a warming climate.
The first objective is to identify and characterize heterogeneity in landscape change occurring on DoD training lands in Interior Alaska. The research team will do this by integrating observations from optical and microwave satellite remote sensing with existing in-field monitoring efforts. The second objective is to detect shifts in ecosystem thermal properties, flood events, landscape water stress, and vegetation productivity through comprehensive time series data analysis. The third objective is to apply advanced spatiotemporal down-scaling methods to produce finer resolution (e.g. 500-m) fused optical and microwave Earth System Indicators that will provide better detection of change occurrence of terrain warming, ecosystem wetting or drying and vegetation decline.
This project will provide land managers and infrastructure planners with a new geospatial change analysis platform to identify the location, trajectory and characteristics (e.g. distribution shape), and underlying drivers of ecosystem shifts occurring within DoD terrains. The resulting change maps and terrain delineations can be readily included in DoD supported geographic systems including the Geographic Information Supporting Military Operations (GISMO) platform. This framework will initially be applied for cold regions in Interior Alaska, but can be readily modified to provide change detection support for other geographic locations.