Citation:

LUNETTA, R. S., J. F. KNIGHT, J. EDIRIWICKREMA, JOHNG LYON, AND DORSEY D. WORTHY. LAND-COVER CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA. REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, (105):142-154, (2006).

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:

Monitoring the locations and spatial distributions of land-cover changes and patterns is important for establishing links between policy decisions, regulatory actions and resulting landuse activities. The monitoring of change patterns across the landscape can also supply valuable information for assessing ecosystem condition and potentially serve as an early warning indicator for impending functional impairment. Past activities including two-date change detection efforts using Landsat data have tended to be performance limited for applications in biologically diverse systems. This study explored the use of 250 m multi-temporal MODIS NDVI 16-day composite data to detect land-cover change on a one-year time-step for the 52,000 km2 Albemarle-Pamlico Estuary System (APES) in the Mid-Atlantic United States. Detection accuracy was assessed for 2002 at 87.9%, with a reasonable balance between change commission errors (21.9%) and change omission errors (27.5%) and Kappa coefficient of 0.67. Annual change detection rates across the APES over the entire study period (2002-2004) were approximately 0.6% per annum and varied from an estimated low of 0.4% (2003) and high of 0.9% (2004). These results represented a substantial advancement over past change detection capabilities previously reported for the APES using Landsat data. An important aspect of this research was the development of an automated protocol to first filter the MODIS NDVI data to remove poor (corrupted) data values and then estimate the missing data values using a Fourier transformation technique – to provide a high quality uninterrupted data stream to support the change detection analysis.

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