Does Citizen Science Consensus Alter Time Series?

One question that has come up a few times with our consensus classifications is, does the level really matter when it comes to looking at change in kelp forests over the long-term?

While our data isn’t quite up to looking at large-scale timeseries yet (we’re still digging through a few thorny methodological issues), I grabbed the complete dataset we have for the Landsat scene around Los Angeles from work Isaac is doing and decided to take a look. Note, this is totally raw and I haven’t done any quality control checking on it, but the lessons are so clear I wanted to share them with you, our citizen scientists.

After aggregating to quarterly data and then selecting the scene that had the highest kelp abundance for that quarter (i.e., probably the fewest clouds obscuring the view), we can see a few things. First, yeah, 1 classifier along is never good.

Screen Shot 2018-02-02 at 1.38.03 PM

Note, I haven’t transformed the data to area covered, instead we’re just going with number of pixels (1 pixel = 30 x 30m). But, wow, we need consensus!

But what if we impose a more conservative filter? Say, a 4-10 user agreement threshold? What does that show us?

Screen Shot 2018-02-02 at 1.38.34 PM

What I find remarkable about this image is that while we see the effect of decreases in detection when more and more citizen scientists have to agree on a pixel, the trends remain the same. This means that while we will try to chose the best threshold that will give us the closest true estimate of kelp area, that there will be multiple intermediate thresholds that give us the same qualitative results to any future attempts at asking questions of this data set.

This is a huge relief. It means that, as long as we stay within a window where we are comfortable with consensus values, this data set is going to be able to tell us a lot about kelp around the world. It means that citizen science with consensus classifications is robust to even some of the choices we’re going to have to make as we move forward with this data.

It means you all have done some amazing work here! And we can be incredibly confident in how it will help us learn more about the future of our world’s Floating Forests!

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