This is the second installment in a series on developing alternative indices to automatically delineate features such as clouds, snow/ice, water and vegetation in Landsat imagery.
The previous article focused on utilizing the Normalized Difference Water Index to differentiate water from non-water (see Differentiating water using NDWI).
In this series of investigations, the challenge we have given ourselves is to utilize relatively simple indices and thresholds to refine some or all of the existing Landsat 8 quality assessment procedure, and wherever possible to also maintain backward compatibility with previous Landsat missions.
In this article we explore one of the most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) (Kriegler et al. 1969, Rouse et al. 1973, Tucker 1979), to see how it can be utilized to delineate the presence of vegetation. Since the Landsat 8 quality assessment band currently does not include output for vegetation, NDVI seems like a logical foundation for performing this assessment.
NDVI is typically used to indicate the amount, or relative density, of green vegetation present in an image; however, here we adapt this index to more simply indicate confidence levels with respect to the presence of vegetation.
To calculate NDVI in ENVI, you can either directly use the included NDVI tool (Toolbox > Spectral > Vegetation > NDVI) or calculate NDVI yourself using Band Math (Toolbox > Band Ratio > Band Math). If using Band Math, then implement the following equation (float(b5)-float(b4))/(float(b5)+float(b4)), where b4 is Band-4 (Red), b5 is Band-5 (NIR), and the float() operation is used to transform integers to floating point values and avoid byte overflow.
After visually inspecting output to develop thresholds based on observed vegetation characteristics in our test images, results of the analysis indicate the following NDVI vegetation thresholds: low confidence (NDVI ≥ 0.2), medium confidence (NDWI ≥ 0.3), and high confidence (NDWI ≥ 0.4).
Example 1: Lake Tahoe
This example illustrates output from a Landsat 8 scene of Lake Tahoe acquired on April 12, 2014 (LC80430332014102LGN00). The NDVI output for this image successfully differentiates vegetation from water, cloud, snow/ice and barren/rocky land. Note particularly how the irrigated agricultural fields to the east and southeast of Lake Tahoe are appropriately identified, and how the thresholds properly indicate increased vegetation trending westward of Lake Tahoe as one transitions downslope from the Sierra Nevada into the Central Valley of California.
Example 2: Cape Canaveral
This example illustrates output from a Landsat 8 scene of Cape Canaveral acquired on October 21, 2013 (LC80160402013294LGN00). As with the Lake Tahoe example, NDVI once again performs well at differentiating vegetation from water, cloud and barren land. Given the cloud extent and high prevalence of both small and large water bodies present in this image, NDVI demonstrates a robust capacity to effectively delineate vegetation. Such results are not unexpected given the general acceptance and applicability of this index in remote sensing science.
We’ll continue to explore other enhancements in future posts, and ultimately combine the various indices into a single integrated quality assessment algorithm.
In the meantime, we’re interested in hearing your experiences working with Landsat quality assessment and welcome your suggestions and ideas.
Kriegler, F.J., W.A. Malila, R.F. Nalepka, and W. Richardson (1969). Preprocessing transformations and their effects on multispectral recognition. Proceedings of the Sixth International Symposium on Remote Sensing of Environment, p. 97-131.
Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering (1973). Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 I, p. 309-317.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), p. 127-150.