A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
"> Figure 1
<p>(<b>a</b>) A 30 m continuous CONUS Landsat NDVI composite for 28 July 2015. Our methods produce broad scale composites with minimal gaps in data and reduce the effect of scene edges and Local scale comparison of (<b>b</b>) Landsat NDVI at 30 m and (<b>c</b>) MODIS MOD13Q1 at 250 m from the same composite period. The Landsat product provides added spatial detail important in measuring certain ecological processes.</p> "> Figure 2
<p>(<b>a</b>) A simple 16-day mean NDVI composite from 28 July to 12 August 2015 created from Landsat 7 and 8 sensors. The composite contains missing data due to cloud cover and scene edges are apparent due to differing acquisition dates. (<b>b</b>) A 16-day climatology (5-year) gap filled composite for the same time and location. The climatology is user defined in order to produce an appropriate composite for the question being asked.</p> "> Figure 3
<p>A timeline showing the data availability for Landsat NDVI, based upon Landsat surface reflectance products and MOD13Q1. The extended Landsat record provides a longer continuous record of high resolution NDVI.</p> "> Figure 4
<p>A flow chart demonstrating the NDVI compositing process, in which the best available pixels from all available Landsat sensors are selected and combined to produce the final NDVI composite value.</p> "> Figure 5
<p>A screen shot of the NDVI web application (<a href="https://ndvi.ntsg.umt.edu" target="_blank">https://ndvi.ntsg.umt.edu</a>). To download a composite, users set their desired parameters in the left panel. The region of interest can either be an uploaded shapefile or a polygon drawn directly on the map. The composite is processed on the fly and users are notified via email when it is ready to download.</p> "> Figure 6
<p>The distribution of Pearson correlation coefficients between MOD13Q1 NDVI and Landsat NDVI for each land cover class. * represent suspected outliers (observations that fall outside the upper or lower quartiles plus or minus 1.5 times the interquartile distance).</p> "> Figure 7
<p>Time series of 30 m Landsat NDVI and 250 m MOD13Q1 NDVI time series from 2013 to 2015, separated by land cover class. After April 2013, the Landsat NDVI time series include data from both Landsat 7 and 8, while before April 2013 they included just Landsat 7 data. Each time series is from a single point, within a homogenous area (i.e., pixels where both Landsat and MOD13Q1 represent the same land cover), sampled at a location indicative of the major land cover classes.</p> "> Figure 8
<p>(<b>a</b>) Pixel locations in central Washington, USA. Landsat derived NDVI can provide increased detail in heterogeneous landscapes. The difference in pixel shape is due to native projections being transformed to a common projection. (<b>b</b>) Chart for 2015 of a Landsat derived NDVI and MOD13Q1 NDVI time series.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Compositing
2.3. Smoothing
2.4. Quality
2.5. Product Creation and Distribution
2.6. NDVI Comparisons Across Spatial Scales
2.6.1. Phenology Cameras
2.6.2. MOD13Q1
3. Results
3.1. Phenology Cameras Results
3.2. MOD13Q1 Results
4. Discussion
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Pixel Value | Description |
---|---|
10 | Clear not smoothed |
11 | Clear and smoothed |
20 | Snow or water not smoothed |
21 | Snow or water smoothed |
30 | Climatology not smoothed |
31 | Climatology smoothed |
Statistic | All Pixels | Clear Pixels | Snow/Water Pixels | Climatology Pixels |
---|---|---|---|---|
Mean Bias | −0.03 | −0.03 | −0.01 | −0.02 |
MAB | 0.06 | 0.05 | 0.10 | 0.09 |
RMSE | 0.10 | 0.08 | 0.15 | 0.14 |
Pearson’s r | 0.94 | 0.97 | 0.71 | 0.88 |
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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. https://doi.org/10.3390/rs9080863
Robinson NP, Allred BW, Jones MO, Moreno A, Kimball JS, Naugle DE, Erickson TA, Richardson AD. A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sensing. 2017; 9(8):863. https://doi.org/10.3390/rs9080863
Chicago/Turabian StyleRobinson, Nathaniel P., Brady W. Allred, Matthew O. Jones, Alvaro Moreno, John S. Kimball, David E. Naugle, Tyler A. Erickson, and Andrew D. Richardson. 2017. "A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States" Remote Sensing 9, no. 8: 863. https://doi.org/10.3390/rs9080863