Citation:

LUNETTA, R. S., J. F. KNIGHT, J. EDIRIWICKREMA, JOHNG LYON, AND L. D. WORTHY. LAND-COVER CHARACTERIZATION AND CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA. Presented at USEPA Remote Sensing Workshop, Chicago, IL, November 01 - 03, 2005.

Impact/Purpose:

Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.

Description:

The purpose of this research and development effort is to investigate the feasibility of using MODIS derived Normalized Difference Vegetation Index (NDVI) data to delineate areas of LC change on an annual basis and identify the outcome of LC conversions (i.e., new steady state). This research is being conducted across the 52,000 km2 Albemarle-Pamlico Basin System (APES) located in North Carolina and Virginia. The study area includes diverse ecoregion types ranging from coastal plain (east) to the Blue Ridge Mountains (west). Landscape "patch" sizes are fine in scale with approximately <5% of the study area containing homogeneous LC corresponding to individual MODIS-NDVI (250 m) pixels. Additionally, biological diversity and vegetation regrowth rates are at the high end of the spectrum for locations within the conterminous United States.

Record Details: