Welcome back to Floating Forests! We’ve had a few snafus that have been lowering the image quality that folk have mentioned, and we’ve got them locked down! What issues, some of you might be saying? During an image upload, something got snagged in the color translation, so images were coming out looking dark and red. Some users (thanks, artman40!) were quite skilled at seeing the kelp anyway. Hats off to you!

Yeah, that’s not an easy one. Interestingly, if you selected the image area, the scene pops out clearly.

We’re actually kind of excited about this, as it will give us a second validation dataset so we can really calibrate user views of images under different conditions. Some of the images that we have up there have been previously been viewed by research assistants (undergrads) at UC Santa Barbara. Part of our analysis of the data involves calibrating against any noise of different viewers looking at the same image. Now we’ll have three different classifications types for a set of the images – undergrad RAs, Floating Forest users, and Floating Forest users with a color-skewed dataset. It’s going to help us reduce error of estimation of kelp bed size and get better quality data in the end as we build better models of the data. There’s actually a nice article on just this topic in last month’s issue of Methods in Ecology and Evolution (one of my favorite journals!) (what, don’t look at me that way, I’m a scientist!)

A number of folk have asked us about the color skewing, and also asked us about the algorithm we use to select images. Stay tuned, as we’re working with Zooniverse to release the code they use to select images, and then anyone who is interested in having at it – either for their own applications (say, spotting coral reefs in the tropics, and needing to subset out only coastal images) or who is interested in trying to make our process better (and reduce the number of land images while not losing coastline images). We’d love to collaborate with more folk out there!

No, there’s no giant kelp in Poland (sorry) BUT a generous volunteer has translated our entire site into Polish! So check it out by going to the site, clicking the globe, and selecting Polski, and then ODKRYWAJ PŁYWAJĄCE LASY!

Every great thing in this world has an origin story, right? Here’s ours.

Years ago, I was a postdoc at the Santa Barbara Coastal Long-Term Ecological Research Site (SBC LTER) working on the links between waves, kelp, and food webs. I had this problem. Big waves came through in the winter and removed a lot of kelp. Only, we only had divers going out in the summer, by which time kelp had often recovered, so I couldn’t estimate how much damage had been done.

Enter the brilliant Kyle Cavanaugh. As a grad student, he was pushing the limits on how we use satellite imagery for marine science. He’d devised a way to use imagery from Landsat, stretching back into the 1980s, to see giant kelp forest canopies from space. Better still, as every area around the earth was photographed about twice a month, this meant that he could see the forests I was analyzing right after storms hit them.

Fig 1 from Kyle’s Original work on kelp & Landsat

So, we struck up a collaboration to use the Landsat data he was collecting (paper here) to bring in the wave story. It was fantastic, and led to a wonderful collaborative paper.

And that could have been the end of it.

Except…there was this niggling thing. Landsat has images of the whole globe. And Giant Kelp isn’t just in California, but rather it’s found in areas all over the planet. Could we look at giant kelp across the planet?

As other members of the lab Kyle was in moved forward, they slowly expanded the dataset to include Baja, Northern California, and more. Why slow? Well, turns out, computers can’t just scan an image and tell you where the kelp is. Kelp looks like a lot of things in those pictures – like parts of the land, like clouds, like sea foam. But, to the human eye, it has some pretty distinct patterns in where it is and how it clumps together. The Siegel lab had setup a great pipeline of undergraduates who looked over the images, circled where kelp was, and then sent it back to grad students and computers to determine how much was there.

It took years just to get California. The globe seemed impossible.

A few years later, this came up while Kyle and I were seated in a room of amazing kelp forests scientists. We were at the National Center for Ecological Analysis and Synthesis for a working group on kelp forests and climate change. One of the tasks we had assigned ourselves was to build a global database of all of the kelp forest monitoring data out there. We found a number of wonderful datasets – some stretching into the 1970s, and one even into the 1950s. But all of these sets were limited to just a handful of sites. And there weren’t many of them.

What to do?

The NCEAS Kelp Forests and Climate Change working group

A few project members brought up Kyle’s Landsat data, but, expanding to the globe seemed daunting – until we realized that there might be a way to harness the power of the Crowd. I’d recently read Nielsen’s Reinventing Discovery where he talked about a project called Galaxy Zoo. In Galazy Zoo, astronomers had folk look at pictures of hundreds of thousands of galaxies and help them classify them into types. Along the way, citizen scientists not only had a good time, but even made some discoveries of types of celestial objects never before recorded.

Why not tap the power of folk interested in our oceans? Maybe we could provide images of coastlines that many people had never seen – to set them up as explorers of a potentially kelpy world, and help us get data along the way? People could see not only the kelp forests that might be lurking in their back yard, but discover features and forests in places we’d hardly ever been able to look before.

So, we emailed Zooniverse. It wasn’t their regular time to receive new submissions, but they wrote back right away, very excited. We put together a crackerjack team of scientists from our working group, and submitted an application to be a project, and as the next round swung around, they selected us to take part! We built a small science team from our larger network (who is still pretty active in the whole process!), began conversations with the Zooniverse team after going to their marvelous conference in Chicago, and we were off!

From that point on, it’s been smooth sailing to the great site you see now letting you sail the seas of Floating Forests (from space!)

A number of you have been getting this message – either on login or when clicking through images.

Are we really done?

In short, no.

Basically, you all have been too amazing, and have gone through images far faster than we anticipated. We started with a number of Landsat scenes from California and Tasmania from 2012-2014 last Thursday. We thought it would take at least until Wednesday to get through that, and in that time, we’d have the rest of the California and Tasmania images chopped up and ready to go.

How wrong we were.

By Friday, the Zooniverse folk were scrambling to get the rest of the California and Tassie images into the system. And now you all are blowing through those, too! We had a beautiful artful roll-out plan for the entire globe that would stretch out over the course of the year. Clearly, we were wrong.

So, via Twitter we reached out to the NASA Landsat folk who put us in contact with the USGS Landsat folk who maintain the data, and we’re trying to see if we can fast track data acquisition. We’ll keep you posted.

But, now, we do have new data that the Zooniverse folk have put into the system (faster than anticipated because you all are awesome), and more on the way, so fear not! If you get the above message, it’s just a minor glitch that should go away shortly. Hit reload once or twice, and you should be all good!