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Remote Sens. 2010, 2(2), 526-544; https://doi.org/10.3390/rs2020526

Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data

1
ASRC Research & Technology Solutions, Contractor to US Geological Survey Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA
2
US Geological Survey Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA
3
Department of Natural Resources and Environmental Management, University of Hawaii, 1910 East-West Road, Honolulu, HI 96822, USA
4
School of Natural Resources and the Environment & School of Geography and Development, University of Arizona, Tucson, AZ 85721, USA
5
US Geological Survey, 12201 Sunrise Valley Drive, Reston, VA 20192, USA
*
Author to whom correspondence should be addressed.
Received: 22 December 2009 / Revised: 3 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
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Abstract

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies. View Full-Text
Keywords: phenology; remote sensing; MODIS NDVI; geographic framework; phenological classification; pheno-class phenology; remote sensing; MODIS NDVI; geographic framework; phenological classification; pheno-class
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Gu, Y.; Brown, J.F.; Miura, T.; Van Leeuwen, W.J.D.; Reed, B.C. Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data. Remote Sens. 2010, 2, 526-544.

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