Land-cover change detection using multi-temporal MODIS NDVI data

Ross S. Lunetta, Joseph F. Knight, Jayantha Ediriwickrema, John G. Lyon, L. Dorsey Worthy

Research output: Contribution to journalArticlepeer-review

695 Scopus citations


Monitoring the locations and distributions of land-cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be performance limited for applications in biologically complex systems. This study explored the use of 250 m multi-temporal MODIS NDVI 16-day composite data to provide an automated change detection and alarm capability on a 1 year time-step for the Albemarle-Pamlico Estuary System (APES) region of the US. Detection accuracy was assessed for 2002 at 88%, with a reasonable balance between change commission errors (21.9%), change omission errors (27.5%), and Kappa coefficient of 0.67. Annual change detection rates across the APES over the study period (2002-2005) were estimated at 0.7% per annum and varied from 0.4% (2003) to 0.9% (2004). Regional variations were also readily apparent ranging from 1.6% to 0.1% per annum for the tidal water and mountain ecological zones, respectfully. This research included the application 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 discrete Fourier transformation technique to provide high-quality uninterrupted data to support the change detection analysis. The methods and results detailed in this article apply only to non-agricultural areas. Additional limitations attributed to the coarse resolution of the NDVI data included the overestimation of change area that necessitated the application of a change area correction factor.

Original languageEnglish (US)
Pages (from-to)142-154
Number of pages13
JournalRemote Sensing of Environment
Issue number2
StatePublished - Nov 30 2006


  • Accuracy assessment
  • Change detection
  • Land cover
  • Multi-temporal imagery


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