First samples!

Shuifu sample on a trib to the Yangtze.

Winter term 2016 - pandas

I sent the team to see the pandas this morning. From left to right: Marcus, Al, Zanna, Maisy. Liam is taking the photo.

Mae Kate an Philip Signing Off!

MK's bit: This semester, Philip and I were working with some new soil pit samples! This project was a continuation of the winter term project I did last year and presented this year at GSA. A lot of the beginning of the year, I was focusing on re-doing the analyses I had completed on the soil pits to incorporate XRF data. This meant shatterboxing all the samples, which was a time intensive project. When the new soil pits arrived, Philip and I had a bit of difficulty sorting through them because the labels on the bags had worn off. We were able to sort them, sieve each sample to <2mm, and pack and seal them before the semester was out, so someone else will be able to take this project over!

It's been a busy semester, but working with Philip has been a blast! We read a lot of interesting papers, survived mineralogy, had some crazy shatterboxing times, and learned how to use the autoclave. I'll be spending next semester in Cuba, and upon my return I'll be jumping in with my EPA project focusing on the effects of agricultural drainage tiles on subsurface erosion rates. I'm excited to see what the future holds!

Philip's bit: This semester was my first experience working in a research lab. It was a great experience working with Mae Kate and learning how to problem solve in the lab. My first responsibility in the lab was to help Mae Kate and Adrian shatterbox some soil samples to prepare them for XRF analysis. Over the course of the semester, I got to learn how to shatterbox, siv, autoclave, and catalogue samples, but my main job in lab was to maintain the liquid nitrogen levels in the labs germanium detector and the geology department's scanning electron microscope. I learned a lot working in Amanda's lab through both the frequent papers Amanda gave to us but also because my lab work tied in to what I was learning in Mineralogy. It's been a great, busy semester trying to balance my class work with my lab work. Working with Mae Kate has been great, and I'm excited to keep doing research in the geology department. 

Zanna's fall research

          This fall was my first semester working in Amanda’s lab. I started out by assisting Megan on compiling and analyzing her data from this past summer’s fieldwork in China. It was a great opportunity for me to continue building on the GIS skills that I have. Megan’s objective was to quantify the river’s change in a rural village in Yangjuan, China. She did this by using repeat photography, GPS data, GIS mapping techniques, and traditional surveying methods. She found that parts of the restoration effort of the rural village were relatively effective, but that during a 50-year flood they proved to be largely ineffective in protecting agriculture.

My part in this presentation was helping her to quantify the change by visually analyzing the data in ArcMap. I started out doing this by taking the GPS data she had tracking the left and right banks of the river and transforming the feature vertices to points. After adding surface information and running the Euclidean distance, I was able to quantify the change between the available 2010 data and the 2015 data so that I could measure the change in bank width between that period on both the left and right bank.  I also digitized the riverbanks in order to create a simple graphic for Megan, which she could use to display her surveyed area. Lastly, I helped Megan to customize a map that showed the different landform properties of the river in order to help her audience visualize how the river has shifted and affected the land over the past seven years.

If I’d had more time, I would have turned the riverbanks into routes in order to create a longitudinal profile of the river displaying both elevation and distance.

Otherwise, I have spent the semester assisting in running sediment samples, sieving, and have been prepping for my own trip with Amanda to China this January. We will be looking to generate a comprehensive understanding of the fire history in western China. We are hoping to determine the background and long-term erosion rates in the Lower Jin Sha. Additionally, we want to describe the stratigraphy of the river and collect samples for radiocarbon dating. Combined, this data will allow us to figure out how frequent fires were in past years. The information will also inform us as to if large debris flows were related to these events. Ultimately, a greater understanding of how fire use in the Lower Jin Sha region relates to erosion will arise.

GSA2015

Last week students from the lab were at the Geological Society of America meeting in Baltimore, where they presented research done in the lab. Here are some photos.

Article on recent field work

Here is an article recently published on our August 2015 field trip.

--

Amanda Henck Schmidt
Assistant Professor
Geology Department
Oberlin College

http://www.oberlin.edu/faculty/aschmidt

In Which an Exciting New Land Use Dataset Makes Itself Known to the Group

Hello,  Joe here.  With the carto-freak David, we make up the GIS side of the research group’s “Spatial Command”.  We don’t like to get our fingers dirty and so are instead lucky enough to spend hours on end in a windowless computer lab making minute adjustments to processes that we know will inexplicably fail anyway.  Not that I’m bitter about the work I do or anything…

Anyway, Spatial Command is currently looking at a series of watersheds along the Mekong river, tying isotope data (specifically 210Pb and 137Cs) from soil samples to other parameters, like relief, rainfall, and land use.  The isotopes get counted in Harbin, our reliable germanium detector.  Data like relief, we can calculate from elevation models, and in fact, we used the wonderful GDEM dataset from NASA to that effect.  Rainfall was a bit harder to do in-house, so we turned to the quite useful APHRODITE dataset from the Research Institute for Humanity and Nature (RIHN) and the Meteorological Research Institute of Japan Meteorological Agency (MRI/JMA).  That leaves us with land use data, the source of some exciting new developments for Spatial Command, and the subject of this blog post.

Harbin, showing enormous patience with one the bozos from the lab, this is something Harbin has to put up with on a daily basis, as everyone who works in this lab is quite a joker.

Previously, we had been using the GlobCover dataset from the European Space Agency for our larger scale analysis and an in-house supervised classification of Landsat 8 data trained by yours truly for a smaller scale study of three basins.  Spatial Command was all set to use GlobCover for our larger project, when word came in that a newer, more precise dataset existed, GlobeLand30, a global dataset with a spatial resolution of 30 meters.  Needless to say, our cartographic natures were excited by this possibility.  But we were still cautious.  Where had this dataset come from?  Sure it was more precise, but was it more accurate?  We decided to take one step beyond guessing, and put it to the test!

GlobeLand30 is a product from the National Geomatics Center of China, and was released in 2014.  The image data used to create GlobeLand30 were primarily 30 meter Landsat TM and ETM+ images from within a year of 2010.  They undertook an extensive accuracy assessment, and reported an accuracy of 83.51%, but we decided it would be prudent to assess the accuracy of our study area specifically.

When comparing the GLOBCOVER and GlobeLand30 against accuracy assessment regions defined by me from Landsat 8 data, certain issues presented themselves.  Namely, that each dataset was divided into different land use classes.  The GlobCover dataset was divided into 23 different classes, GlobeLand30 had 10, and because we were primarily interested in quantifying agriculture, we had only 4 classes.  Both the accuracy assessment and eventual application necessitated some amount of class combination.

Eventually, all data was combined into four classes:  Agriculture, Forest (comprising all natural vegetation), artificial surfaces, and water.  Some classes that were irrelevant to the areas used for accuracy assessment, like “tundra”, were ignored.  Then it was smooth sailing to the goods; we plugged the data into ENVI, generated some confusion matrices, and then we were Good To Go!

The overall accuracy of GlobCover for our area was 72.22%, and the overall accuracy of GlobeLand30 was 76.76%.  The accuracy for each individual class was also good, although classes that were not widely represented in the data used for accuracy assessment tended to have lower calculated accuracy.

As you can see from the maps comparing the GlobCover and GlobeLand30 datasets, the precision afforded by the GlobeLand30 means that the boundaries between classes are much more accurate.  However there are also still areas of disagreement between GlobeLand30 and the hand-classified dataset used for accuracy assessment.  The classification I did by hand was primarily from Landsat 8 satellite images, between that and my unfamiliarity with the region, it is certainly possible that I have misclassified things that the GlobeLand30 classification system classified correctly because they used a wider range of data sources that may have helped distinguish between things that were visually similar in the satellite data, like cropland and grassland.

Still, it seems clear that GlobeLand30 is the better choice, and we at Spatial Command are excited to get a chance to work with it.  The next time you hear about it from us will be when we have some preliminary results about how land-use correlates with our isotope counts.