Sunday, October 15, 2017

Module 6: Spatial Enhancement

In this weeks lab I downloaded satellite data from a source online (GLOVIS) and performed multiple spatial enhancement techniques in both ArcMap and ERDAS Imagine in order to better interpret the data.  The data from a Landsat 7 Image had uniform white stripes of null data so the goal was to blend these stripes into the background, while retaining as much detail as possible.  It was cool to see how ArcMap and Imagine work together to enhance the image.

The first enhancement performed was done in ERDAS Imagine.  I performed both a 3x3 Low Pass and 3x3 High Pass in order to set the stage for later enhancements.  Each filter grouped cells into 3x3 groups of nine.  The Low Pass gave a smoothing effect, while the High Pass enhanced the edges in the image.  Next I switched to ArcMap and opened the Focal Statistics tool. I used the Mean Stats and Range filters which have a similar effect as the low and high passes respectively, but in 7x7 blocks.

I then switched back to ERDAS Imagine to perform the rest of my image enhancements. I first performed a Fourier Transformation to blend the white stripes into the background image.  This turned the white stripes more grey and blended them.  Next I performed a 3x3 sharpen and sharpen 2.  This did sharpen the edges slightly.  Finally, my last filter applied was a 7x7 low pass.  I did this to blend the lines into the image even more.  This resulted in a slightly darker image, but enough detail was retained to be useful.  My final image can be seen below.

Tuesday, October 3, 2017

Mod 5a: EMR and ERDAS

This weeks lecture focused on Electromagnetic Radiation (EMR) and the lab introduced the basics of geoprocessing with the program ERDAS Imagine.  Learning how EMR waves interact with our atmosphere and remote sensing sensors really helped me understand how maps such as the false infrared are produced.  I think this background information will be valuable in the future as I continue to analyze various maps. 

In this weeks lab, I learned how to navigate through the program ERDAS Imagine as well as how to use the viewer.  After getting the basics down, I then got down to business dealing with image data. First I imported into ERDAS a raster image depicting land classes in a section of forest in Washington State, USA.  I then altered the colors of the map to make certain areas easier to depict.  Finally I used the Inquire Box to export a smaller piece of the map as an image file. To make the final deliverable product, I imported the image file into ArcMap and added the essential map elements.  I'm looking forward to learning more ERDAS Imagine features!  My map can be seen below.

Sunday, September 24, 2017

Module 4 - Ground Truthing

This week, we learned about ground truthing techniques which are used to verify classification schemes, also known as accuracy assessment.  Most ground truthing is done in-situ, or in the field in person.  This would be the most accurate way to ground truth, but this isn't always practical so supplemental data such as high resolution aerial imagery or google street view can be used to verify classifications.  In this lab, I used google earth street view to "travel" to each of my 30 sample points and verify whether the original land use/land cover classification was correct.  If it was not correct then I identified the correct classification.  Out of the 30 sample points which I classified, 24 of them were verified as correct by using street view.  My ground truthing map with 30 sample points can be found below.

Monday, September 18, 2017

Mod 3 - Land Use and Land Cover Classification

This week we focused on identifying various features in aerial imagery and supplying these areas with a land use/land cover classification.  Using an aerial image from Pascagoula, MS, I identified features based on their color, size, shape, and surroundings.  I used the USGS Standard Land Use/Land Cover Classification System to assign areas a level 2 classification which  uses a 2 digit number code.  I identified a couple areas using the level 3 classification which used a 3 digit code and is more specific.  I digitized polygons to highlight my classification areas and exported them as shapefiles.  My completed map can be seen below.  

Monday, September 11, 2017

Remote Sensing Module 2

This is the first blog post in my Remote Sensing and Photo Interpretation class.  During our Module 2 lab, we focused on visual  interpretation of aerial photographs and false color infrared photographs. In Exercise 1 we focused on identifying areas of varying tone and texture and creating polygon shape files to depict these areas.  For tone, the polygons  were labeled very light, light, medium, dark, and very dark.  For texture, the areas were categorized as very fine, fine, mottled, coarse and very coarse. My map 1 identifying features can see be seen below.
In Exercise 2, we identified features in an aerial photograph of a Pensacola, FL beach based on four criteria: shape and size, shadow, pattern, and association.  Using map examining techniques and my background knowledge on what natural features near a beach look like, I was able to confidently identify different objects by thinking about these criteria. You can see my map identifying these objects below.

In the third exercise, we were introduced to colors in false color infrared photographs and how they differ from the original image.  Some features such as trees which are green, show up as a red color in a false color infrared photograph.  False color images are meant to enhance certain features in a photograph so they can be more easily identified.  For example, the marsh areas in the original image looked brown, however they showed up as green in the false color infrared.  This green color likely indicates some photosynthesis occurring an therefore leads us to believe that there are living plants in the marsh areas.

I liked learning how much information you can obtain from looking at features in an aerial photograph, and particularly the false color infrared images.  I can see the advantages of investing in remote sensing because you can gather a vast amount of data from the air, and with many less resources than if you had to physically go examine an area.  

Wednesday, August 9, 2017

Module 11: Sharing Tools

This GIS Programming class was the first I'd ever been introduced to coding, and I feel I've learned a good deal over the semester.  The most important thing I can take away from this class is how to use Python to run Geoprocessing tools in ArcMap.  Of the topics covered this semester, this is at the top of my list because Geoprocessing is what makes ArcGIS so useful when analyzing large amounts of data and turning it into a visual form.  Using Python can expedite processes such as selecting data by attribute or clipping multiple feature classes.  This allows the user to do things with data in ArcGIS that they usually wouldn’t do because of the amount of time it would normally take.  I’m also thrilled to now understand the basic syntax of Python, and how to do things such as run for loops.  I believe understanding the basic syntax of Python will allow me to at least half way understand other coding languages. 

One more very important thing I’ve learned while working through these modules is to take breaks if things aren’t working out.  At times when I would get error message after error message, I simply had to stop for the night and go walk or run to clear my head.  Usually I would come back the next day and I would be able to identify my error rather quickly.  

This final module focused on Sharing tools.  During this exercise and lab we learned the three ways to share tools, how to add python file type to ArcCatalog, and how to create tool documentation. During the lab assignment we were given a toolbox containing a script and script tool.  We were instructed to fill in six parameters in the tool and then edit the script to ensure the variables corresponded to the parameters.  During this process, we were introduced to to the sys.arg[] expression which allows the script to talk to the parameter in the tool instead of using a hard coded file path.  Finally we edited the tool dialouge to make it more user friendly, imported the script to make it easier to share, and set a password for security reasons.  Below is a screenshot of the tool result on a map and the tool dialog box.  

Well this is it GIS Programming, it's been real.

Wednesday, July 26, 2017

Module 10: Creating Custom Tools

This week we learned how to turn Python scripts into custom tools.  This is important because it allows us to integrate scripts into ArcGIS and easily share scripts and their functionality with others, even if they're not familiar with Python.  This was a really useful lab because it showed me how I may use scripting in the real world.  It really tied together the knowledge I've obtained throughout the semester and made me realize how powerful coding can be.  I like that I can now easily share scripts with others.

Here is a simplified 5 step process for creating a tool out of a stand alone script:

1.  Open ArcMap and create a toolbox.
2.  Add a script tool to the toolbox and link the desired stand alone script.
3.  Add script parameters by filling in "Display Name" and "Data Type" fields.  (Input, Output,                   number of features)
4.  Using the GetParameter function, edit the script code to read parameters.
5.  Open ArcMap and double click the script tool to run it.

Below is a screenshot of my script tool window after the parameters had been set as well as a screenshot of the results window after the tool had been successfully run in ArcMap.