Think carefully: these are very, very expensive machines. No guide can replace thinking critically before you act. Don’t be dumb. Have fun! Vacuum is required. The analyzer vacuum pump must be connected and running whenever the analyzer is powered. Don’t turn off the pump, unplug it, or pull the vacuum line while the analyzer is operating. Introduction The Picarro G2201-i is a benchtop cavity ring-down spectrometer that measures CO2, CH4, δ13C-CO2, and δ13C-CH4 in a stream of gas. ...

Keep water away from the sensors. Stop the pump if water enters the trap, condensation appears in the lines, or the vacuum gauge suddenly moves more negative. Check for a flooded intake or kink before continuing. Introduction The Field Portable Gas Analyzer (FPGA) draws soil gas to the surface, measures its CO2 and O2 concentrations, and can collect a sample for later laboratory analysis. The design comes from Brecheisen et al. (2019), which describes a set of commercially available sensors and plumbing assembled into a battery-powered instrument small enough to carry between wells. ...

Backup your data regularly. The EGM-4’s internal memory is limited to 1,500 records across up to 99 plots. Failing to dump data regularly will result in data loss when this buffer fills. When memory is full, the oldest records are overwritten. You are responsible for your data. Significant care has been taken to ensure data safety. However, by using the open-egm4 software, you accept the risk of data loss. Open-egm4 can not send commands to the EGM-4 itself, and therefore cannot damage it internally. Don’t get water in the gas lines. If you do, it might enter the IRGA itself and damage it. It’s like $15,000 to replace. Introduction The PP Systems EGM-4 (EGM) is a portable infrared gas analyzer (IRGA) designed for measuring soil CO₂ efflux. The device connects to various chamber types and soil probes, and stores measurements in its internal memory. ...

Introduction Goal: To create animations of soil cores spinning from .vol files obtained from the Pacific Northwest National Laboratory. We’re getting our files from the Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory, who have scanned the soil cores with a Nikon XTH CT scanner. Requirements .vol files from a CT scanner At least 16gb of memory Ideally more. Both Fiji and Dragonfly attempt to load the entire file into memory; the downloaded .vol is almost 40gb. Dragonfly from Comet Software. You’ll need a license: 30 day free trials are easily accessible, and non commercial licenses are available. Fiji or ImageJ, but use Fiji. A windows or Linux machine to run Dragonfly on; everything but that can be done on Mac TL;DR Download .vol files from EMSL Data portal Import the files into Fiji as raw data, 2000x2000x2000 Convert to 8-bit, save as .tiff Open the .tiff in Dragonfly, crop to slices 250-1800 Make a cylindrical mask to crop the garbage off the core Use movie maker to make a rotating movie, export to .avi Use ffmpeg or similar to convert to mp4 Use kdenlive or similar to crop to square Use Gifski to convert to reasonably sized gif Getting .vol files from EMSL data portal Selecting cores To start, we need to get the CT scan files from EMSL, provided as .vol files. ...

Warning: The XRF emits X-rays up to 50kV. Improper use can and will cause serious, long term harm to your health. Follow all safety protocols including those beyond this article. Introduction This article assumes you already are in possession of pucks of finely ground soil. Procedure Prepare the XRF Analyzer Take the battery from the charger and insert it into the back of the XRF. Note that a good amount of force is required to latch the battery into place. Power on the XRF by holding the power button on the left of the front for about 3 seconds. Wait for the system to boot. Log into the device using the password. The four directional buttons are used to select items on the interface. The button to the right of the pad is the enter or confirm button, not a back button. The center of the pad is not a button, which is incredibly unintuitive and confused me for an unbelievably long period of time. From the main menu, select System Check to calibrate the scanner. Connect the XRF to a Computer ...

Introduction to Git and GitHub It’s easiest to think of Git (and other forms of source control) like a time machine. When you make changes to your project, it’s easy to see what has changed. You have a perfect history of everything that’s ever happened in your project, and can always revert to any point in that history. Git allows you to work with other people on the same project, while guaranteeing that you won’t mess up each other’s work. Git allows you to send and receive files from your computer to a server, making it easy to put your projects online. Concepts Commits: Think of a commit as taking a snapshot of your project. Every time you make a commit, you are saving a record of what your project looks like at that moment in time. This allows you to track changes over time and gives you a perfect history of your entire project. ...

Introduction “Keyhole Markup Language” .kml files are useful for a number of reasons, namely storing pins, locations, polygons, images, and other GIS info. One of the easiest ways to make them is with Google Earth. You can approach this in a couple of ways. Both the Earth website and the Google Earth Pro desktop app are usable. Google Earth Web Open Google Earth in a browser. Open the left hand side bar. It’s a little arrow in the middle of the left edge. Click the “+ New” button, select “Local KML file”, and click “Create”. You should see a new section, “Local KML files”, and your new project, “Untitled”. You can rename the project by clicking on the kebab menu while hovering on it, and clicking “Rename”. Click on the project to select it. To add a location to the project, navigate to it, and then click the “Save to project” button that appears in the card. Alternatively, you can use the pin / placemark tool (first on the left in the toolbar) and it will be automatically added to the selected project. You can rename the pin / placemark by either doing so when you initially place it, or by using the kebab menu in the project menu. Once you are done adding to your project, you can export by clicking the kebab menu on it, and then selecting “Export as KML file”. Google Earth Pro Open Google Earth Pro. Create a folder under Add > Folder. This is your project, so name it accordingly. Create placemarks with the placemark tool. They should be automatically placed into the new folder. You can drag other objects inside of the folder to add them to the project. Once you are done with the project, export it by right clicking on the folder and selecting “Save place as..” There are two options to save: a .kml and .kmz. If you don’t know what to use, select .kml. .kmz files are used to compress larger projects, typically ones that include images, but have some compatability issues.

Note: I have a sneaking suspicion that trying this in Earth Engine proper using JS will significantly improve the results. The same goal attempted in rgee will remain available below, and a hopefully better JS version will appear above this in the coming days. Note 2: JS version had the same issues and created more problems. Nevermind! Rgee version Introduction Goal: create global maps showing Dryness Index and Evaporative Index in both 2012 and 2019, using rgee. ...

Introduction In general, exporting data to a .csv with rgee is pretty easy. The general gist of the process is: Define the area or points you are interested in Filter data Convert the results to a list, and Export to a csv. Example Make sure to load and initialize rgee first! # Define an AOI over Kansas aoi <- ee$Geometry$Rectangle(c(-102.05, 36.99, -94.6, 40.0)) # Define sample points in the area points <- ee$FeatureCollection(c( ee$Feature(ee$Geometry$Point(-98.5795, 39.8283), list(label = "1")), ee$Feature(ee$Geometry$Point(-97.5795, 38.8283), list(label = "2")), ee$Feature(ee$Geometry$Point(-96.5795, 37.8283), list(label = "3")) )) # Grab an image from Sentinal 2 and calculate NDVI s2_collection <- ee$ImageCollection("COPERNICUS/S2")$ filterDate('2020-06-01', '2020-06-30')$ filterBounds(aoi)$ map(function(image) { ndvi <- image$normalizedDifference(c("B8", "B4"))$rename("NDVI") return(image$addBands(ndvi)) }) ndvi_image <- s2_collection$select("NDVI")$mean()$clip(aoi) # Grab NDVI values at the sample points ndvi_values <- ndvi_image$reduceRegions( collection = points, reducer = ee$Reducer$mean(), scale = 30 ) # Convert the result to a list and then to a data frame ndvi_list <- ndvi_values$getInfo()$features ndvi_df <- do.call(rbind, lapply(ndvi_list, function(x) data.frame( label = x$properties$label, NDVI = x$properties$mean, lon = x$geometry$coordinates[1], lat = x$geometry$coordinates[2] ))) # Save the data frame as a CSV write.csv(ndvi_df, "ndvi_values_hello.csv", row.names = FALSE)

Data types (link to method of gathering) MAT (Mean Annual Temperature) Average yearly temperature. MAP (Mean Annual Precipitation) Average yearly precipitation. GPP (Gross Primary Productivity) Total amount of energy captured by plants. Does not account for respiration losses. NPP (Net Primary Productivity) Amount of energy that remains after plants have used some of the captured energy for their own respiration. Actual amount of new biomass that is available for consumption by other critters. NPP = GPP - Respiration PET (Potential Evapotranspiration) Amount of water that would be evaporated and transpired by vegetation if there was sufficient water available. Atmospheric demand for water. AET, ET (Actual Evapotranspiration) - Actual amount of water that is evaporated from soil and transpired by vegetation. Less than or equal to PET. Depends on availability of water. DI (Dryness Index) PET / MAP EP (Evaporation Potential) 1 - (PET /MAP) Sites Data was gathered from multiple sites, sorted with a RegionName, SiteName, and Pit. It’s important that the Pit is unique. ...