DaymetR / DaymetPy updates

The ORNL DAAC which hosts the Daymet data is shifting to a HTTPS only policy. As such I updated my DaymetR R package to reflect these changes. Users running an old version will have to reinstall the new version to reflect the migration to secure data connections.

You can find the updated package on my github page (see link on the software page). Please reinstall the package to guarantee continued functionality. Similar corrections have been made to the DaymetPy code.

On perception and autumn colours

Richardson Lab members almost unanimously stated that this year was the brightest and most spectacular autumn display of colours in a long time. However, people are notoriously bad at accurately quantifying pretty much anything they see, hear, smell or feel around us. We often perceive the world differently from it’s physical (absolute) reality. This is easily illustrated by various types of optical illustions or the fact that we do not feel absolute temperatures, only change.

Not only does our brain play tricks on our senses, it also fails us when it comes to interpreting long term (environmental) change, as climate change is slow and our cognitive bias leans towards instant action and gratification.

Begs the questions, can we tease apart the intensity of this years autumn colours, and how they compare to previous years using PhenoCam based data using a more quantitative approach? More so, was this autumn really as spectacular as most people in the lab seem to agree upon?

In order to answer these questions we need to look at some PhenoCam data. Normally we look at the greenness signal, which is mostly driven by the presence of healthy green leaves. In this case however, I focused on how red the leaves were during this autumn. In the figure below you see a time series of canopy redness at the Bartlett research forest in the White Mountains, NH. In this graph you see two high peaks which are caused by leaves turning colour, from green to yellow / red, in autumn in 2014 and 2015.

time_series_rcc

The yearly maximum value in this graph tells us how intense the colours were. A metric for the duration of the colour intensity can be calculate as the sum of a set number of values before and after this peak value, where a higher value indicates a longer duration of peak colour. Below I show peak values and the summed values for a week before and after the maximum Rcc value.

When looking at absolute values one can see that 2014 was actually the better (left figure, dashed line), brighter year. However, if considering the duration of peak colour 2015 wins by far (right figure, dashed line). At this location autumn colours were especially intense and long lasting (which increases the odds for leaving lasting impressions on lab members going hiking and leaf peepers in general). So in this case, our senses weren’t fooled - and we have the data to prove it!

OV5647 spectral response

In order to use the raspberry pi cameras within a more rigorous framework I needed the full spectral response of the chipset used in these cameras, the OV5647 by OmniVision. I set out to do acquire the response curves using a DIY diffraction grating approach.

During this process I was contacted by Howard Shapiro who volunteered his old spectrofluorometer to accomplish this task faster and more precisely. Although, initial tests together with Howard showed promise, it would remain an arduous task to quantify the whole spectral response. Recently, Howard managed to dig up some documentation on the chipset which displayed the spectral response (quantum efficiency, QE) in a graph (given that the OmniBSI chipset as displayed is the one residing in the OV5647). This has made things considerably easier. I used these slides to digitize the quantum efficiency of the OV5647 between 400 and 700 nm. A physical measurement will still be needed to quantify the remaining near infrared spectrum (> 700 nm, data can be found on the OV5647 spectral response page).

Sadly, the original graph only covers wavelenghts between 400 and 700 nm, leaving out the near infrared (NIR) part of the spectrum. The red edge, located at 680-730nm, is a key part of the spectrum key in vegetation remote sensing applications. At the red edge vegetation reflectance changes from low to higher values. The magnitude of this differences is an indication of plant health.

Although I got most of the picture, due to good detecitve work by Howard, I still don’t have the complete picture. Some physical measurements will still be necessary to get the complete spectral response / QE of the chip but I’m at least halfway there.

University of Sheffield EEE Seminar

Past week I had to present in the Ecology, Evolution & Environment (EEE) seminar series at the University of Sheffield. I was invited by a good friend Donatella Zona with whom I’m collaborating on some arctic research involving changes in snow melt dynamics and their influence on ecosystem productivity.

I had the pleasure to present an overview of past and current research, mostly dealing with vegetation phenology. It was a varied crowd so I glossed over some of the more intricate details. Comments afterwards were positive, suggesting that everyone understood my talk. The latter is key in talking to a broad audience, as you don’t want to lose half of the people before you are halfway through.

I also had a interesting chat with Gareth Phoenix on his arctic research and how camera based work might help him. We also discussed some of his research on the coupling of above- and below-ground turnover rates and carbon stocks to leaf area index (LAI) in arctic ecosystems ((http://onlinelibrary.wiley.com/doi/10.1111/gcb.12322/abstract)). This research, by Sloan et al., suggests that: “the coupling of leaf and root carbon stocks and turnover rates to LAI across plant communities allow estimates of fine root and leaf carbon pool size and cycling rates across heter-ogeneous Arctic landscapes, using just one readily remotely sensed parameter – LAI”. These results have important implications towards PhenoCam use in arctic ecosystems, given the strong relationship between LAI and and the greenness chromatic coordinate (Gcc).

(header image: fall colours in the peak district West of Sheffield)

Modelling grassland growth using PhenoCam data

The phenology of deciduous forests is similar year to year. In it’s simplest form, modeling the influence of climate on phenology can therefore be modeled as discrete events, such as leave emergence in the spring. However, the appearance of leaves for some vegetation types is less well-defined seasonally, because changes are gradual: continuous leaf growth in grasslands or slow changes in the leaf physiology in evergreen forests (more on evergreens in a later post). Modeling this ongoing phenology is more challenging, as it requires an understanding of the biological and physical processes that govern the yearly trajectory rather than just a discrete event, such as leaf emergence.

Pagination


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