- On thing that caught my eye was the way that they used the color channels. They calculated an index based on the green spectrum: (2*Green) – (Red+Blue). In the figures they published it gave a really good measure of the green-up of the measured forest. This could be used in the initial segmenting of the images. We could use a model based on this color normalization to segment the green patches from the non-green patches :).
- In the context of measuring NDVI-like values with digital cameras, this paper does not try to use digital imagery for NDVI measurements. It’s using the images to measure phenological happenings.
- It does compares the behavior of NDVI with digital camera measurements in light of known phenological happenings. It states similarities and differences and possible explanations.
- The paper makes a good defense of detecting phenological happenings with digital cameras. But notice that we are trying to go a bit further than just measuring the amount of green in a ROI. We are trying to detect individuals in plots.
- May 12: (About comment 1). I completely missed an additional way of combining the color channels. The thing with the “2G-(R+B)” is that it is very sensitive to brightness, according to Richardson. What was done to correct for brightness sensitivity was to use a channel percentage measure within the ROI. Chanel%=sum(channel)/(sum(R)+sum(G)+sum(B)). This, Richardson says “eliminated the variability associated with overall brightness”
- May 12: Another comment that caught my eye on the second read was the fact that Richardson did not see any new insight from a normalized 2G_RBi measurement. He does not give an equation for the normalized 2G_RBi.