AmeriFluxR: a R toolbox to facilitate Ameriflux Level2 data exploration

Today I launch the first version of AmerifluxR. The AmeriFluxR package is a R toolbox to facilitate easy Ameriflux Level2 data exploration and downloads through a convenient R shiny based GUI. This toolset was motivated by my need to quickly assess what data was available (metadata) and what the inter-annual variability in ecosystem fluxes looked like (true data).

The package provides a mapping interface to explore the distribution of the data (metadata). Subsets can be made geographically and/or by vegetation type. Summary statistics (# sites / # site years) are provided on top of the page. The Data Explorer tab allows for more in depth analysis of the true data (which is downloaded and merged into one convenient file on the fly). A snapshot of the initial Map and Site Selection landing page is shown below.



In the Data Explorer tab one can plot ecosystem productivity data (GPP / NEE) for a selected site. You can select a plot displaying all data on a daily basis (consecutively) or overlaying data yearly. Note that although all sites are listed, not all of them have accessible data. The plot area will notify you of this.

[caption id=”attachment_1227” align=”aligncenter” width=”768”]daily_gpp GPP at Harvard Forest[/caption]


[caption id=”attachment_1228” align=”aligncenter” width=”768”]yearly_nee overlaying daily NEE values (together with the long term mean and standard deviation; LTM and SD respectively)[/caption]

The package can be conveniently installed using only 3 commands on the R terminal (the first line takes care of dependencies, the second line loads devtools which is required to install from a github repository, line 3).


To get started, just type


on the command line and the above screen will pop-up in your favourite browser (preferentially Chrome).

Future development will include higher level products as well as other metrics (yearly summaries, etc…). I welcome anyone to join this effort and potential scientific endeavours that spring from this. Drop me a line by email or on GitHub.

map colours onto data values in GDAL

This is a quick post originating from a discussion I had recently. Sometimes GIS data does not come with it’s original colour map but only as raw numbers. These raw numbers (classes) are fine for calculations, but rather limit the way you visualize things. Here, I’ll show how to map colours to the classes or ranges using the Geospatial Data Abstraction Library (GDAL).

All you need is a list of classes which you want to map to particular colours. The format of this colour table is rather flexible and is described in full on the GRASS r.colors page. For this particular example I used the colours of the 0.5 km MODIS-based Global Land Cover Climatology map, which translates into a table with 16 classes (I attached the table at the end of the blog post). You can download the data form the USGS website if you want to try this example (warning: large file - 4GB unzipped).



# If the colour table is saved in colours.csv the following
# command links a proper colour table to a geotiff file
# without this information.
gdaldem color-relief input.tif colours.csv output.tif

The above command might map the colours to the classes but the map still remains rather static. If you want to create a Google Earth compatible file (mapped onto a 3D sphere), you can do so by translating the file format. The resulting KML file should open in Google Earth if you have a copy running.

gdal_translate -of KMLSUPEROVERLAY input.tif output.kml -co format=png

The full colour table for the image as linked to above is shown below.

0 186 254 253
1 0 100 1
2 77 165 87
3 125 204 15
4 98 232 101
5 55 198 132
6 214 119 117
7 253 237 160
8 185 231 141
9 255 224 27
10 254 192 107
11 37 138 220
12 252 251 0
13 251 3 4
14 147 144 5
15 254 220 211
16 191 191 191



Harvard 360

I’m starting a new series of blog posts called Harvard 360 (a bit of my life and research on the Harvard campus). I’ll be posting 360 immersive pictures to provide a real feel for my work around Harvard University, and in particular during field work.

I kick off the series with an image of Harvard Yard. Harvard Yard houses the oldest buildings on campus as well as all undergraduate housing and several libraries. In front of the white building in the distance is the statue of John Harvard. [click the grey bar to load the image if not loading automatically, or click the link to access the VR Flickr page]

Life cycle events and weather in the tropics (DR Congo)

Life cycle events in tropical forests, like their more temperate counterparts, are in part tuned to changes in the weather throughout the year. Unlike the more temperate regions the temperature in the tropics is not the main driver of phenology, as it stays more or less stable throughout the year. Below you see a summary graph of the temperature of Kisangani (120 km to the east of Yangambi), which averages around a ~25C or rather perfect growing conditions.

However, the region and most of the basin goes through two wet seasons. This is due to the fact that the intertropical convergence zone (ITCZ), a band of clouds and thunderstorms, moves back and forth across the equator following the sun’s zenith point. Given the location of the Congo Basin around the equator the ITCZ passes over the basin two times a year, due to tilt in the Earth’s axis, creating two rainy seasons. In the bar graph below you see the monthly totals, where values over 150mm are considered “wet” months.

If you compare an example of life cycle events of some of the intermediate Jungle Rhythm results you see that this particular tree flowers at the start of the wet season, while leaf senescence starts at the end of the dry season. Not all trees will show this pattern, as different species might take different environmental cues (which are or are not met depending on the seasonal changes or yearly variability). However, the example illustrates the relation between weather and life cycle events very clearly. The importance of these seasonal changes in the tropics can not be understated. A recent study also linked these seasonal changes to measurable differences in CO2 uptake from the atmosphere, a main incentive for me to document these life cycle changes in the Jungle Rhythms project.



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