Enhancing the Landsat 8 Quality Assessment band – Differentiating water using NDWI

(Update: 09-23-2014) Just added – see our related post on Using NDVI to delineate vegetation.

Are you working with Landsat 8 or other earlier Landsat data? Are you looking for solutions to automatically delineate features such as clouds, snow/ice, water and vegetation? Have you looked at the Landsat 8 Quality Assessment band, but find the indicators don’t meet all your needs?

If so, you’re not alone. This is a common need in most remote sensing applications.

After recently exploring the contents of the Quality Assessment (QA) band for examples from Lake Tahoe and Cape Canaveral (see Working with Landsat 8), it became readily apparent that there is room for improvement in the quality assessment indicators. So we set out to identify possible solutions to help enhance the output.

Landsat8 Lake Tahoe - Water

The challenge we gave ourselves was to utilize only relatively simple indices and thresholds to further refine some or all of the existing Landsat 8 quality assessment procedure, and wherever possible to also maintain backward compatibility with previous Landsat missions.

As a first step, let’s explore how the Normalized Difference Water Index (NDWI), as described by McFeeters (1996), can be utilized to differentiate water from non-water.

To calculate NDWI in ENVI, we used Band Math (Toolbox > Band Ratio > Band Math) to implement the following equation (float(b3)-float(b5))/(float(b3)+float(b5)), where b3 is Band-3 (Green), b5 is Band-5 (NIR), and the float() operation is used to transform integers to floating point values and avoid byte overflow.

The NDWI output was visually inspected to develop thresholds based on known image and landscape features. Additionally, as with the QA band, rather than identify a single absolute threshold, three threshold values were used to indicate low, medium and high confidence levels whether water is present.

As a caveat at this stage, note that this analysis currently only incorporates two example test images, which is far from rigorous. Many more examples would need to be incorporated to perform thorough calibration and validation of the proposed index. It is also expected that developing a robust solution will entail integrating the different indices into a rule-based decision tree (e.g., if snow/ice or cloud, then not water).

Results of the NDWI analysis for water indicate the following: low confidence (NDWI ≥ 0.0), medium confidence (NDWI ≥ 0.06), and high confidence (NDWI ≥ 0.09).

Example 1: Lake Tahoe

This example illustrates output for a subset Landsat 8 scene of Lake Tahoe acquired on April 12, 2014 (LC80430332014102LGN00). Here we see improvement over the QA band water index, which exhibits significant confusion with vegetation. The NDWI output performs very well at the high confidence level, but includes some confusion with snow/ice and cloud at the low and medium confidence levels. We expect much of this confusion can be resolved once a decision tree is incorporated into the analysis.

Landsat8 Lake Tahoe Quality Assessment - Water

Example 2: Cape Canaveral

This example illustrates output for a subset Landsat 8 scene of Cape Canaveral acquired on October 21, 2013 (LC80160402013294LGN00). As with the previous example, there is significant improvement over the existing QA band water index. There is again some confusion with cloud at the low and medium confidence levels, but strong performance at the high confidence level. As a result, this output also shows promise as the foundation for further improvements using a decision tree.

Landsat8 Cape Canaveral Quality Assessment - Water

We’ll continue to explore other enhancements in future posts. In the meantime, we’d love to hear your experiences working with Landsat quality assessment and welcome your suggestions and ideas.

– –

McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432.


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