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Using Advanced Satellite Products to Better Understand I&M Data within the Context of the Larger Ecoregion . Kevin James, NPS I&M Heartland Network Jeff Morisette, USGS Fort Collins Science Center w/ Colin Talbert, USGS Fort and Pete Ma, NASA Goddard Space Flight Center. Background….
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Using Advanced Satellite Products to Better Understand I&M Data within the Context of the Larger Ecoregion Kevin James, NPS I&M Heartland Network Jeff Morisette, USGS Fort Collins Science Centerw/ Colin Talbert, USGS Fortand Pete Ma, NASA Goddard Space Flight Center
Background… • “Parks are part of larger ecological systems and must be managed in that context” (Fancy, Gross, & Carter, 2008). • Work funded through the USGS/NPS National Park Monitoring Project. Starting third year of funding. • Primary objective: help park managers use cutting edge moderate- (250m) and high- (30m) resolution remote sensing products to place I&M observations within the context of the larger ecosystem.
Background… • The foundation for this work is existing and freely available remote sensing data products: • NASA-funded 250m spatial resolution land surface phenology product for North America • Landsat data available from USGS for free. • These products represent a significant national investment in ecosystem monitoring.
Objectives: • Characterize phenology across the Southern Plains, Heartland, and Northern Great Plains Networks from 2000 on. • Building on the remote sensed vegetation indices and available Network data, identify appropriate sampling periods and locations that maximize information content for vegetation vital signs monitoring. • Evaluate the impact of management actions in light of intra- and inter-annual vegetation and climate variability.
Initial parks: selection criteria • Proposal focused on three networks: Southern Plains, Northern Great Plains, and Heartland Network • We wanted at least one park per network • Fairly large in spatial extent • Existing data from either I&M or fire monitoring programs • Fairly native grassland
Modified TIMESAT Parameters Beginning of season End of season Length of season Base value Peak time Peak value Amplitude Left derivative Right derivative Integral over season - absolute Integral over season - scaled Maximum value Minimum value Mean value Root Mean Square Error Phenology Product User Guide, Tan et alhttp://accweb.nascom.nasa.gov/project/docs/User_guide_PHN.pdf.
A (very) preliminary model for bird species richness • An ensemble model* using • Maxent • Boosted Regression Trees • Logistic Regression • MARS • Random Forest * Following Stohlgren TJ, P. Ma, S. Kumar, M. Rocca, J.T. Morisette, C.S. Jarnevich, and N. Benson. 2010. Ensemble habitat mapping of invasive plant species., Risk Analysis, 30(2)224-235.
Future work • Develop a standard operating procedure (SOP) for integrating MODIS Phenology Data into the I&M program. • Correlative analysis connecting the I&M data to these remote sensing variables • Analysis of comparing phenology metrics inside and outside the park but with the stratification based on land cover type.