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

Robinson, Nathaniel; Allred, Brady; Jones, Matthew; Moreno, Alvaro; Kimball, John; Naugle, David; Erickson, Tyler; Richardson, Andrew

doi:10.3390/rs9080863

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

Journal Article · Mon Aug 21 00:00:00 EDT 2017 · Remote Sensing

DOI:https://doi.org/10.3390/rs9080863· OSTI ID:1426166

Robinson, Nathaniel ^[1]; Allred, Brady ^[2]; Jones, Matthew ^[2]; Moreno, Alvaro ^[3]; Kimball, John ^[2]; Naugle, David ^[4]; Erickson, Tyler ^[5]; Richardson, Andrew ^[6]

Univ. of Montana, Missoula, MT (United States). W.A. Franke College of Forestry and Conservation; Univ. of Montana, Missoula, MT (United States). Numerical Terradynamic Simulation Group; W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA
Univ. of Montana, Missoula, MT (United States). W.A. Franke College of Forestry and Conservation; Univ. of Montana, Missoula, MT (United States). Numerical Terradynamic Simulation Group
Univ. of Montana, Missoula, MT (United States). Numerical Terradynamic Simulation Group
Univ. of Montana, Missoula, MT (United States). W.A. Franke College of Forestry and Conservation
Google, Inc., Mountain View, CA (United States)
Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems; Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society

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. Here, 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 Accepted Manuscript (DOE)

Research Organization:: Univ. of Montana, Missoula, MT (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USGS

Grant/Contract Number:: SC0016011

OSTI ID:: 1426166

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 12 Vol. 9; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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

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