Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach

Joseph F. Knight, Ross S. Lunetta, Jayantha Ediriwickrema, Siamak Khorram

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

Abstract

Currently available land cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environmental status and trends assessments and ecosystem process modeling. This research examined the potential for vegetation phenology-based land cover classification over the 52,000 km2 Albemarle-Pamlico estuarine system (APES) that could be performed annually. Traditional hyperspectral image classification techniques were applied using MODIS-NDVI 250 m 16-day composite data over calendar year 2001 to support the multi-temporal image analysis approach. A reference database was developed using archival aerial photography that provided detailed mixed pixel cover-type data for 31,322 sampling sites corresponding to MODIS 250 m pixels. Accuracy estimates for the classification indicated that the overall accuracy of the classification ranged from 73% for very heterogeneous pixels to 89% when only homogeneous pixels were examined. These accuracies are comparable to similar classifications using much higher spatial resolution data, which indicates that there is significant value added to relatively coarse resolution data though the addition of multi-temporal observations.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalGIScience and Remote Sensing
Volume43
Issue number1
DOIs
StatePublished - Jan 2006

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