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Remote Sens. 2017, 9(8), 863; https://doi.org/10.3390/rs9080863

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

1
W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
2
Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
3
Google, Inc., Mountain View, CA 94043, USA
4
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
5
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Prasad Thenkabail, Lalit Kumar and Onisimo Mutanga
Received: 13 July 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 21 August 2017
(This article belongs to the Special Issue Google Earth Engine Applications)
Full-Text   |   PDF [19550 KB, uploaded 21 August 2017]   |  

Abstract

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. View Full-Text
Keywords: Google Earth Engine; NDVI; vegetation index; Landsat; remote sensing; phenology; surface reflectance Google Earth Engine; NDVI; vegetation index; Landsat; remote sensing; phenology; surface reflectance
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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. (CC BY 4.0).

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Robinson, N.P.; Allred, B.W.; Jones, M.O.; Moreno, A.; Kimball, J.S.; Naugle, D.E.; Erickson, T.A.; Richardson, A.D. A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sens. 2017, 9, 863.

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