Archive for the ‘Remote Sensing’ Category

To test the feasibility of a low cost, mobile environmental sensor system that could be used to sample air quality as needed and in areas where conventional sensor systems may not be available or cost effective.

Based around the low-cost Arduino microcontroller, a GPS shield for capturing geographic location was paired with a relative humidity and temperature sensor to act as a mobile data logger. The relative humidity and temperature sensors were chosen for their cost and ease of implementation as an initial set of sensors. The sensors were mounted at the end of a five foot long section of one-inch PVC mast inside a one-inch to two inch tee fitting. The tee fitting was used to protect the sensors from direct sunlight and weather (Figure 1).


Figure 1

The entire unit is powered by a single 9-volt alkaline battery and housed inside a rigid plastic storage container to protect the equipment from handling and the elements. The 9-volt battery should power the unit for several hours and a 9-volt lithium may provide even more operating time (Figure 2).


Figure 2

Software was modified based on that supplied by Adafruit industries, the makers and suppliers of the sensors and GPS unit. The software reads the raw GPS datastream and parses out the $GPRMC string, reads the relative humidity and temperature sensor, then writes these values as a line to a comma separated value text file on a microSD card on the GPS shield. Each additional reading from the time the unit is started is appended to this text file. If power is interrupted to the unit, when it restarts it will create a new file before appending data.

For the initial test of the unit, it was placed inside a backpack with the mast rising to approximately seven feet above the ground. The trip was made on a standard mountain bike and ridden at a nominal pace. The initial test site chosen was a road that runs from north to south from West Walnut Street in Greencastle, IN to the DePauw Nature Park parking lot. There is a change in elevation of roughly fifteen feet where the road enters the forest area at the north end. Otherwise the road is nearly level throughout. The distance covered was approximately 1.7 miles roundtrip and the sensor unit gathered 1059 data points during this trip. Riding was done by staying as close as comfortable to the outside edges of the road for not only safety reasons, but to also help determine the accuracy of the GPS unit.

The unit was turned on and allowed to get a fix on the satellite constellation, then an additional five minutes was allowed to elapse to allow the sensors to acclimate to ambient conditions. Upon return to starting position, the unit was turned off and the file from the microSD card removed and the file copied off.

The data file was opened in Microsoft Excel and the GPRMC latitude and longitude coordinates converted to decimal degrees for use in the GIS. This was done by extracting the degree portion of the latitude string, dividing the rest of the string by 60, then adding the two back together. The temperature was also converted from degrees Celsius to degrees Fahrenheit using the standard formula. Table 1 shows a sample of the raw data file collected and Table 2 shows a sample of the finished spreadsheet.


The resulting Microsoft Excel table was then opened in ArcGIS 10.1 and the XY data used to plot the points (Figure 3).


Figure 3

The projection used by the GPS constellation is WAS 1984 and that was what was used here. A basemap of aerial imagery was then added for reference. Finally, the point data was buffered to 25 meters, the points were kriged, and then the kriged raster was clipped using the buffer (Figure 4).


Figure 4

This project has shown the feasibility of using low cost, mobile arduino-based devices as environmental sensor systems that could be used to sample air quality as needed and in areas where conventional sensor systems may not be available or cost effective.

Several future considerations should be considered, including increasing the number and type of sensors such as adding benzene, NO2, or similar sensors. Additional or different sensors can be added or changed out with minimal effort in programming or hardware.

Another area of interest would be to outfit similar units with small solar panels and battery backup systems so that they could be placed in remote locations, thereby increasing the ability to monitor environmental pollution throughout a much wider area.


Back on January 6 of this year Kevin Butler posted a great article on converting Landsat 8 thermal bands to at-satellite brightness temperatures using the Raster Function Template Editor. The link to his article can be found here .
I will admit I have never used the raster function editor (ok, I didn’t even know it existed), so not being familiar with the raster editor, I had a hard time trying to follow Kevin’s instructions. I am familiar with ArcGIS’s Modelbuilder, though. Using the valuable information in Kevin’s article as a guide, I created a model that does the same thing. The result turned out pretty well and I have included it here as a toolbox that can be added to ArcGIS. You are free to use, modify, and/or change it all you want. All I ask is that if you find it useful or have any comments about it, let me know.
To use the tool, click on the link above and save the Landsat8.tbx file to your computer. Next, open ArcGIS and make sure the Toolbox side-panel is visible (The ESRI website has better instructions for adding toolboxes here . Right-click inside the panel and select “Add toolbox…” from the menu. Navigate to where you saved the Landsat8.tbx and select it (do not open it to show the model, just select the .tbx file). The Landsat8 toolbox is now added. Open it and inside there should be a model called LS8Therm.
Double click the LS8Therm model and enter the location of your Landsat 8 Band11 image, Band12 image, and where to save the output and what to call it (I used .tif files when I created the model).


Click ok and the tool should run;


I ran the tool using the LC80210322013121LGN01 dataset I downloaded from the USGS. Here is the result;


And zooming in for a closer look at Indianapolis, IN;


And then adding a quick symbology;


For anyone that wants to play around with the model, you can right click the model in the toolbox window and select edit. This will open the model in Modelbuilder.

One thing I did not do was preprocess the Landsat 8 data for cloud cover. On the left side of the above image you can see what looks like a series of lakes just to the west of the city. This is a line of clouds, so ideally you would preprocess to reduce cloud cover. The purpose of this was to show urban heat island effect, as can be seen by the darker red of the city compared to the outlying areas. If you were to add a shapefile of the administrative districts, they would follow the outlines of the high-heat areas very closely.


Allen, D. (2011). Getting to know ArcGIS Modelbuilder. Redlands, CA: Esri Press.

Butler, K. (Jan. 6, 2014). Deriving temperature from Landsat 8 thermal bands (TIRS). Retrieved from

What do you see in the image below? A series of black blobs, or the words MAIL BOX?




I recently started reading the book “Gödel, Escher, Back: an Eternal Golden Braid” by Douglas Hofstadter, and Chapter III really got me thinking. The chapter is on Figure and Ground, which mainly relates to art where the figure is the subject of the artwork and the ground is the background. Hofstadter’s book, though, is not so much about art as it is about logic and the way we think and perceive the world.

I started thinking about figure and ground from a geospatial point of view. As an example, let’s say we are analyzing an area of interest trying to determine a specific type of habitat or feature. Depending on the type of analysis, the features we are looking for may be small, complex, or not neatly defined. But what if we looked at it from the point of view of what is not our target feature? Then, like in the image above, the ground becomes the figure and the figure becomes the ground. Similar to the way astronomers used to look at the glass plate exposures from their telescopes. They would look at the negative image where the stars were black on a clear or white background, thereby making them easier to see because of the contrast. Using this same idea applied to geospatial analysis, in some cases, might useful in highlighting the areas we are interested in by looking at what it is not.


Hofstadter, D. R. (2000). Gödel, Escher, Bach: An eternal golden braid : 20th-aniversary edition with a new preface by the author. N.Y: Penguin Books.


I was honored to be able to present a poster at the 2013 Research Day held at IUPUI on April 5th. Being an undergrad it is an especially big honor. This event is hosted each year and showcases the research being done on campus. Everything from neuromolecular studies to using trash as an energy source to bobcat habit fragmentation. “Bobcat habitat fragmentation?” you ask raising one eyebrow. Why, yes, that happens to be what my poster was on, “Impact of the I-69 Corridor on Bobcat (Felis rufus) habitat in Southwestern Indiana” which looked at the further fragmentation of potential bobcat habitat in southern Indiana using a combination of remote sensing and GIS. I have attached the full sized poster to this blog for anyone interested. Below is the abstract from the poster, and as always, please feel free to comment.

“Habitat loss is known to be the main cause of the current global decline in biodiversity, and roads are thought to affect the persistence of many species by restricting movement between habitat patches” (Eigenbrod, Hecnar et al. 2008). This research looks at the impact of the I-69 corridor being built in southern Indiana on Bobcat habitat (Felis rufus) identified through the use of remote sensing and GIS. Bobcats are solitary animals that require steep, forested areas with plenty of cover for both themselves and the small mammals they prey upon. Identifying where Bobcats are likely is the first step in knowing the impact on their diversity in Southwestern Indiana. In this research we used the 2012 National Agriculture Imagery Program (NAIP) imagery for each of the 47 counties in this study, along with the 2005 IndianaMap Elevation Model (DEM) data, both obtained from the Indiana Geospatial Portal ( These were combined with the interstate and highway shapefiles from the IndianaMap website (, and then classified and assigned suitability values to highlight locations for Bobcats within the study area. The I-69 corridor shapefile was then added and buffered to show the impact the corridor will have on existing Bobcat habitat.

Bobcat habitat poster-final