Borneo Biofuels: May 2009

So far, when I try to discriminate cloudy areas from usable data in a Landsat scene, I tell it to ignore everything that has a pixel value above 100 in band 3. This process discriminates a lot of unusable pixels that are clouds while avoiding the elimination of good pixels. This said, inspection of the band 5 histogram has inspired me to try to discriminate shadows in a similar way I use band 3 to get rid of clouds.

Histogram of band 5 of a close up of a Landsat scene

Close up of Landsat scene (pixels in red have a value inferior to 30)

As can be seen from the close up above and its histogram, discriminating pixels that have a value under 30 are almost exclusively cloud shadows. The exception being shadows that result of abrupt topography and water bodies.

Comparing an image where I discriminate only pixels that have a value above 100 in band 3 to an image where I discriminate both pixels that have a value above 100 in band 3 and pixels that have a value under 30 in band 5 gives the following results:

(If you ignore the stripes, green are pixels that should
be good data, yellow are pixels that have a value above
100 in band 3 [clouds] and red are pixels that have
a value under 30 in band 5 [cloud shadows, abrupt
topography shadows and water bodies])

Close-up of above scene

Further testing needs to be done to determine if I should use this thresholding method on band 5 and/or if the threshold value should be changed.

On a different note, I’ve observed that certain bands seem to be far better suited to the method I’m developing to create cloud free composites: