Spring Challenge more results

Last week I described some initial results from the Spring Challenge. I showed how we used individual classifications to build a dataset for a single site in a a single year. And we discovered that using paired images 7 days apart and then smoothing the classifications gave us a good estimate of the “start of spring” date and “end of spring” date for that site in that year.

Since then, I’ve been comparing these “start of spring” and “end of spring” estimates with other estimates that we get that are automatically derived from our greenness time series. To get these automated estimates, we first take the daily greenness values and draw a smooth curve through them. Then we look at the total amplitude (height) of the greenness signal and pick the date where the smoothed curve passes through 20% of that amplitude. We call that the “start of spring.” We call the date where the smoothed curve passes through 80% of that amplitude the “end of spring.”

We can plot our Season Spotter estimates on a curve, along with the automated estimates to get an idea of how the two compare. Here is a plot from 2014 using data from the “canadaOA” camera at Prince Albert National Park, Saskatchewan, Canada — the same site and year we looked at last time.

canadaOA-2014Each of the green dots represents the greenness measure for a single day. The black line is the smoothed line that is fit through the green points, and the gray region around it is our certainty range. The two orange squares are the automated estimates of “start of spring” and “end of spring.” The blue squares are the estimates of “start of spring” and “end of spring” from Season Spotter. Both the orange and blue squares may have horizontal lines coming out of them showing the likely range of dates for these estimates. The longer the line, the less confidence we have in exactly where our square lies. For all the squares, I’ve drawn dotted lines from them to the smoothed line so we can visually compare them more easily.

As we suspected from our analysis last time, the estimates from pairs of images 1 day apart and 3 days apart are closer to the middle of spring, where it’s easier to see the change in leaves. The estimates from the 7-day apart images look very good — even better than the automated estimates!

If we look at “start of spring” estimates from 7-day apart images from all the sites and years that we put into Season Spotter, we see a trend:

sos_delta7_comparisonHere, each site has a different color and each rectangles is a year. So, for example, there are three purple rectangles showing three different years from the canadaOA camera. The lines coming out of the rectangles show us our certainty, as before. Going across is the “start of spring” from the automated method. And going up is the “start of spring” from Season Spotter classifications. The diagonal dotted line is the one-to-one line. If all our rectangles were on this line or scattered evenly around it, it would mean that the estimates from the automated method and from Season Spotter pretty much agree. Instead, we can see that Season Spotter regularly predicts an earlier spring than the automated method, because most of the rectangles lie below the one-to-one line. This suggests that we might want to tweak our automated process to use a lower threshold.

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Friday favorites: Something odd in Maryland

NEON.D02.SERC.DP1.00033_2016_03_23_151506

A turkey vulture takes a rest on in front of the new NEON PhenoCam at the Smithsonian Environmental Research Center (SERC) in Maryland.

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Spring Challenge first results

Having all the pairs of spring images classified means I can now analyze them! In particular, I’m using these images to get a best estimate for the “start of spring” and the “end of spring”. These are metrics that are used in remote sensing — both using the PhenoCams to do automated greenness processing and by researchers using satellite imagery to understand earth’s vegetation.

Several weeks ago, I wrote about what images are in Season Spotter and how we will do the analysis. Read about it here, if you haven’t already.

Let’s look at some actual data from a camera called “canadaOA”, which is in Prince Albert National Park, Saskatchewan, Canada. First, here’s what the view looks like at this camera in spring:

canadaOA_2014_05_24_120134Now here’s a graph showing the distribution of classifications you provided for this site in 2014 when image pairs were spaced one day apart:

spring-canadaOA-1-2014The way to read this graph is as follows: Going from left to right, we have days during the spring, with some late winter and early summer days on either end to make sure we capture the full spring. Each green bar shows how many people classified the image that occurred second chronologically as the one having bigger or greener leaves. In other words, the green bars show confidence that spring changes were able to be seen by volunteers. The blue bars show how many people classified an image as “the images are the same” or said that the earlier image had bigger or greener leaves. In other words, blue bars show confidence that spring changes were NOT able to be seen between the two images. The red bars indicate the number of people who said at least one of the images was a bad image. If more than half of people said that there was a bad image, we don’t use any data from that pair. You can see, for example, that the longest red bar doesn’t have any green or blue bars above it. All the bars have been scaled so that the longest possible bar length means “everybody who saw this pair of images” and a bar half that long means “half of all people who saw this pair of images”.

The orange and purple dotted lines are the best guess “start of spring” and “end of spring” dates based on this data. In this case, the orange line is between May 21 and May 22, indicating that May 22 is the “start of spring”. And the purple line is between May 25 and May 26, indicating that May 25 is the “end of spring”.

Hmm, but it seems a bit odd that spring is only 4 days long. It generally takes longer than that for leaves to go from buds to leaves and then fully grow all the way out. Let’s look at the greenness curve for this site in 2014:

canadaOA_2014_gccFrom the greenness curve, we see that start of spring should be around mid-May and that the end of spring isn’t until the very beginning of June. It looks like the May 21-25 period is the steepest part of the curve — where day-to-day change might be most obvious.

Let’s look next at the data from this same site and year where images were 3 days apart:

spring-canadaOA-3-2014

We see the same sort of pattern again, but now we have more confidence that things are still changing later on in the spring. Our estimate now is that we start to see change between May 19 and May 22, and that we stop seeing change between June 3 and June 6. This makes sense. It’s easier to see that something has changed three days apart than one day apart.

And if we look at the data from when images were 7 days apart, it looks like this:

spring-canadaOA-7-2014

Here, we start to see change between May 15 and May 22 and stop seeing change between June 1 and June 8. That seems pretty accurate based on the greenness curve. But those ranges are really big. We’d really like to know what day is the start of spring, not in what week it occurred.

We can get a day estimate from the week-apart images. To do so, we create a new dataset derived from the 7-day-apart one. We take the classifications from a pair of images 7 days apart, and consider those classifications valid for each day in that range. So each day consists of classifications from seven different image pairs (or fewer if they’re at the beginning or end of the time period we’re looking at). This also has the advantage of smoothing over pairs of images that were bad and those that were simply hard to tell. Our new smoothed dataset looks like this:

spring-canadaOA-7-2014

This dataset tells us that the start of spring is May 19 and the end of spring is June 5. This seems very reasonable when we look at the greenness time series.

I’ve done this same analysis for the seven sites and all 31 site-years that you made classifications for. And the same points seem to be true across them all:

  • People have a hard time seeing differences in the leaves when paired images are only one day apart. (For some sites, people almost never see changes between one-day-apart images.)
  • People are most able to see differences in the leaves when paired images are seven days apart.
  • Using a smoothed dataset derived from the one where paired images are seven days apart seems to give good estimates for start of spring and end of spring.

The next thing for me to do is to measure the uncertainty in these estimates for start of spring and end of spring. And then I am going to compare our estimates from Season Spotter with some automated estimates done by running algorithms directly on the greenness curves. I’ll talk about these analyses in a future post.

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Friday favorites: North Carolina spring

alligatorriver_2016_04_06_123108

Trees are putting out leaves at Alligator River National Wildlife Refuge in North Carolina.

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Spring Challenge a Success!

thankyouWe wanted to take this opportunity to thank everyone that participated in our Spring Challenge over the past month. Not just our volunteers but also our partners at SciStarter, Project Budburst, and the Zooniverse for helping us spread the word through social media, newsletters, and blog posts.

We launched our Spring Challenge on March 7 and ran it until April 4. With your help, we were able to classify the 9,512 images of spring (our initial goal) by March 23. A week and a half early! Being that it is currently fall in the southern hemisphere, we then uploaded our images of fall for classification to keep the momentum going. By the end of our challenge, we had reached 60% of our fall goal as well! During the entire campaign, we had 1,598 registered Zooniverse users and 4,008 unregistered users from 105 countries do 56,756 classifications. IMPRESSIVE! If you want to see how all these data from the spring and fall classifications will be analyzed, please read this earlier post by Dr. Kosmala. We will continue to provide updates on the analysis as it is completed.

Also, please note that Dr. Kosmala and two other researchers (Drs. Koen Hufkens and Josh Gray) from the Season Spotter team answered questions related to climate change, phenology, citizen science, and the Season Spotter project during a Reddit.com #AskMeAnything on March 31. To see the questions and responses, you can visit this site. It’s a great way to learn more about all the research being done by the team and how your contributions fit into the big picture. Again, thanks to everyone for helping us meet and go beyond our goal for this challenge!

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Friday favorites: Subtle spring

lunar-surface-nasaOur lunar PhenoCam helps us track seasons on the moon. Because of the minor tilt in the moon’s axis (only about 1.5 degrees), seasonal change on the moon is subtle and hard to measure with on-the-ground methods.

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Friday favorites: The still waters of early spring

Olympic National Park, WA,Lake Crescent

The still waters of Lake Crescent in early spring reflect the surrounding hills and mountains in Olympic National Park, Washington.

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