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

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

1,* email, 2email, 3email, 4email and 5email
Received: 22 December 2009; in revised form: 3 February 2010 / Accepted: 3 February 2010 / Published: 11 February 2010
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: phenology; remote sensing; MODIS NDVI; geographic framework; phenological classification; pheno-class
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MDPI and ACS Style

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.

AMA Style

Gu Y, Brown JF, Miura T, Van Leeuwen WJD, Reed BC. Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data. Remote Sensing. 2010; 2(2):526-544.

Chicago/Turabian Style

Gu, Yingxin; Brown, Jesslyn F.; Miura, Tomoaki; Van Leeuwen, Willem J. D.; Reed, Bradley C. 2010. "Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data." Remote Sens. 2, no. 2: 526-544.


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