Abstract
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.
| Original language | English |
|---|---|
| Article number | 863 |
| Journal | Remote Sensing |
| Volume | 9 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 1 2017 |
Funding
Acknowledgments: We thank the Google Earth Engine developers for their support and technical advice. This work was funded through a Google Earth Engine research award and by the NRCS Wildlife Conservation Effects Assessment Project and Sage Grouse Initiative. The development of PhenoCam has been supported by the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (award EF-1065029 and EF-1702697), DOE’s Regional and Global Climate Modeling program (award DE-SC0016011), and the US National Park Service Inventory and Monitoring Program and the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey). We thank Koen Hufkens and Tom Milliman for their contributions to producing the PhenoCam data. We thank the PhenoCam site collaborators and funding sources (listed in the Supplementary Materials) for their support of the PhenoCam project.
| Funders | Funder number |
|---|---|
| 1702627, 1702697 | |
| Hornocker Wildlife Institute/Wildlife Conservation Society | |
| G10AP00129 | |
| National Stroke Foundation, Australia | DE-SC0016011, EF-1702697, EF-1065029 |
Keywords
- Google Earth Engine
- Landsat
- NDVI
- Phenology
- Remote sensing
- Surface reflectance
- Vegetation index
