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.
Highlights
The Normalized Difference Vegetation Index (NDVI) is arguably the most widely implemented remote sensing spectral index for monitoring Earth’s land surface
Examination of the quality band indicates this site is often obscured by clouds and snow in the winter months, resulting in a spurious NDVI time series with poorly defined seasonality, while the GCC90 time series provides a well-defined seasonal signal
The resulting NDVI time series is smoothed to approximate natural vegetative phenology
Summary
The Normalized Difference Vegetation Index (NDVI) is arguably the most widely implemented remote sensing spectral index for monitoring Earth’s land surface. The Landsat Mission, with its first sensor launched in 1972, is the only uninterrupted long-term (>30 years) high-resolution remote sensing dataset that can provide a continuous historic NDVI record globally. The Landsat record at 30-m resolution is ideally suited for local or regional scale time-series applications, with the recent release of higher-level surface reflectance products from Landsat sensors 5 ETM, 7 ETM+, and 8 OLI from 1984 to present. Utilizing these products across scenes and through time, is not without complications [3], for users without GIS and Remote Sensing training and resources. To create consistent mosaics or long-term time series, users must account for data record gaps, radiometric differences across sensors [4], scene overlaps, malfunctions
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