Enhancing the Landsat 8 Quality Assessment band – Detecting snow/ice using NDSI

This is the third installment in a series on developing a set of indices to automatically delineate features such as clouds, snow/ice, water and vegetation in Landsat imagery.

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.

The two previous articles focused on Differentiating water using NDWI and Using NDVI to delineate vegetation.

Landsat 8 Lake Tahoe Snow/Ice

Here we explore the Normalized Difference Snow Index (NDSI) (Dozier 1989, Hall et al. 1995; Hall and Riggs 2014) to demonstrate how this index can be utilized to delineate the presence of snow/ice.

Note that NDSI is already included in the Landsat 8 quality assessment procedure; however, as currently implemented, NDSI is used primarily as a determining parameter in the decision trees for the Cloud Cover Assessment algorithms.

Additionally, as discussed in the MODIS algorithm documentation (Hall et al. 2001), NDSI has some acknowledged limits, in that snow can sometimes be confused with water, and that lower NDSI thresholds are occasionally needed to properly identify snow covered forests. This suggests that NDSI performance can be improved through integration with other assessment indices.

For now we consider NDSI on its own, but with plans to ultimately integrate this and other indices into a rule-based decision tree for generating a cohesive overall quality assessment.

NDSI is calculated using the following general equation: NDSI = (Green – SWIR)/(Green + SWIR). To calculate this index for our example Landsat 8 images in ENVI, we used Band Math (Toolbox > Band Ratio > Band Math) to implement the following equation (float(b3)-float(b6))/(float(b3)+float(b6)), where b3 is Band-3 (Green), b6 is Band-6 (SWIR), 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 snow/ice characteristics in our test images, results of the analysis indicate the following NDSI snow/ice thresholds: low confidence (NDSI ≥ 0.4), medium confidence (NDSI ≥ 0.5), and high confidence (NDSI ≥ 0.6).

Example 1: Lake Tahoe

This example illustrates output from a Landsat 8 scene of Lake Tahoe acquired on April 12, 2014 (LC80430332014102LGN00). For this image, both the NDSI output and QA assessment successfully differentiate snow/ice from other image features. The only significant difference, as can be observed here in the medium confidence output, is that the QA assessment identifies two lakes to the east and southeast of Lake Tahoe that are not included in the NDSI output. Without knowledge of ground conditions at the time of image acquisition, however, it is not feasible to assess the relative accuracy of whether these are, or are not, ice-covered lakes. Otherwise, the snow/ice output is in agreement for this image.

Landsat 8 Lake Tahoe NDSI Snow/Ice

Example 2: Cape Canaveral

This example illustrates output from a Landsat 8 scene of Cape Canaveral acquired on October 21, 2013 (LC80160402013294LGN00). Given its location, there is not expected to be any snow/ice identified in the image, as is the case for the high confidence NDSI output. However, for the medium and low confidence NDSI output there is some confusion with clouds, and for the QA assessment there is confusion with clouds and sand. This suggests a need to either incorporate other indices to refine the snow/ice output and/or include some geographic awareness in the analysis to eliminate snow/ice in regions where it is not expected to occur.

Landsat 8 Cape Canaveral NDSI Snow/Ice

Stay tuned for future posts on other Landsat 8 assessment options, as well as a discussion on how to combine the various indices into a single integrated quality assessment algorithm.

In the meantime, we welcome your feedback on how these indices perform on your own images.

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Dozier, J. (1989). Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote sensing of Environment, 28, p. 9-22.

Hall, D. K., Riggs, G. A., and Salomonson, V. V. (1995). Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote sensing of Environment, 54(2), p. 127-140.

Hall, D. K., Riggs, G. A., Salomonson, V. V., Barton, J. S., Casey, K., Chien, J. Y. L., DiGirolamo, N. E., Klein, A. G., Powell, H. W., and Tait, A. B. (2001). Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms. NASA GSFC. 45 pp.

Hall, D. K., and Riggs, G. A. (2014). Normalized-Difference Snow Index (NDSI). in Encyclopedia of Snow, Ice and Glaciers, Eds. V. P. Singh, P. Singh, and U. K. Haritashya. Springer. p. 779-780.

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