GIS4035 - Module5a - Intro to ERDAS Imagine and Digital Data 1

This week's lab appeared simple enough but truly understanding the differences in images and the devices used to capture them as well as continuing to learn about the properties of Electromagnetic Radiation and the Electromagnetic Spectrum proved to be challenging.  This class so far has covered more subject matter material than previous courses.   The focus points of this lab were to ensure we knew how to calculate the wavelength, frequency, and energy of Electromagnetic Radiation and to learn about and how to use ERDAS Imagine to view and create images.

The first exercise was to perform simple calculations using formulas such as Maxwell's Wave Theory: The speed of light is equal to the wavelength in meters times the frequency in Hertz.  The important takeaway was the inverse relationship between wavelength and frequency.  The longer the wavelength the lower the frequency and conversely, the shorter the wavelength the higher the frequency.  The wavelength can be used to determine the spectral region/location.  

Max Planck proposed the quantum theory of electromagnetic radiation where energy is transferred in discrete packets called photons (Jensen, p. 45).   The energy Q, of a quantum measured in joules, is equal to Planck's constant multiplied by the frequency of the radiation.  The relationship between energy and wavelength is also important.  Longer wavelengths result in lower energy content.

The second exercise introduced us to ERDAS Imagine.  We learned how to open provided raster images of different types and how to display the images using the Raster Options tab.   We used different tools and techniques to view the images such as panning, zooming, fitting the image to frame or changing its scale as well as how to display multiple images.  We also viewed an Arc Coverage file which is a digital vector format file that can be found in the ERDAS Imagine default installation filepath.   The images we viewed were obtained from different devices.   One image was from an Advanced Very High Resolution Radiometer (AVHRR) and the other images were obtained using a Landsat Thematic Mapper (TM).   These images had vastly different spatial resolutions.   This exercise also had us explore the different band combinations for multispectral data.  Changing the band combination can aid in identifying different elements in the image.

The final exercise had us open a thematic single layer in ERDAS Imagine and tasked us to focus on a smaller area of the image.   Using ERDAS we were able to create a subset image which was then used to create a map in ArcMap.   While in ERDAS we also viewed the image's attribute table and added a new "Area" column that calculated the area of each classification.

Classified Subset Image of Washington State Created in ERDAS Imagine

The subject material in this lab was the challenge for me this week; understanding the concepts and properties of EMR.  Learning how to change the view or display of images, in order to aid in the interpretation of feature elements, was the fun part.

GIS 4035 - Module 4 Lab - Ground Truthing and Accuracy

In this week's assignment we were tasked to "ground truth" our Lab 3 map.   In lieu of "in situ" data collection, we used Google Maps to verify the classification codes we assigned to different areas on the image file in the Lab 3 map.  

1.   First I opened ArcCatalog and created a Truthing point shapefile with the same projection as the TIFF image and LULC shapefile from Lab 3.

2.   Next I opened up a copy of the Lab 3 map and added the Truthing layer and saved the new Lab 4 map.

3.   Next I reviewed the map and used sampling rules of thumb to determine my sampling requirements.

a.     Lab 3 was classified to Level II so sampling the transportation class was probably not important.

b.     Bodies of water and Level II wetlands I assumed were homogenous and decided not to locate samples in these classes.

c.     The large coverage area of residential classification (11) led me to choose the stratified random sampling method since this method is proportional to LULC type.

d.     I placed at least 1 sample in each classification code (except Water, Wetlands, and Transportation, see above).

e.     I added samples proportionately to each classification based on the coverage area size.

4.   I began creating point features in the Truthing shapefile after adding the required fields.

5.   I then added a field for coordinates of each point feature.  Using the Identify tool I recorded the degrees, minutes, and seconds of each point.

6.   Next, I opened Google Maps and began the ground truthing process using the coordinates of each point location.   I took notes in a comment field I had added so I wouldn’t get confused between points.  

7.   As part of this lab I realized that I did not maintain scale or MMU properly in Lab 3.  I tried to keep my scale to 1:10,000 in Data View when randomly locating my sample points.   Likewise, in Google Maps I tried to be aware of the scale in the bottom right before going into Street View.   I think if I had consistently applied MMU and scale in Lab 3 I would have had fewer classifications.   Fewer classifications may have improved my accuracy in Lab 4.

8.     The biggest issue for me in classification was that there were several “mixed use” areas on the map.   For example, while some areas would appear predominately residential my sampling point was on a large commercial building that was “mixed in” within residential.  Seven of the eight misclassified sample points should have been classified as either Residential (11) or Commercial and Services (12). I think this might indicate lax zoning ordinances and poor urban planning.

Ground Truthing of Lab 3 Map Using 30 Stratified Random Sample Points and Google Maps

GIS4035 - Module 3 Lab - Land Use/Land Cover Classification

In this week's lab we continued learning how to interpret aerial photographs using various elements but took it another step further by then classifying the interpreted areas using the USGS Land Use/Land Cover Classification System.  This module again covered a large amount of information.   Wrapping my head around the text and supplemental readings was overwhelming.  Once I started the lab however I was able to let go of some of the details and depth of information and focus on a Level II classification of the provided image.   This one image assignment made it clear how enormous a task digitizing an image can be and the amount of detail that could be required.   As the lab assignment stated "we could spend all semester examining this one photograph."

Lab 3 - LULC Classification Of Features In An  Image From Mississippi

1.     First step was to open ArcCatalog and review the TIFF image that was provided for the lab assignment.  

2.     Second, was to create a new shapefile, also in ArcCatalog, and then to set its coordinate system to match that of the TIFF file.

3.     The previous week’s lab had us use the Drawing toolbar to create polygons and then convert the graphics to a feature shapefile.   This week we used the Editing toolbar to build the polygon features that would be contained in the shapefile. 

4.     I recalled only using the Editing toolbar one other time in a class last Spring so I had to refresh my snapping and vertices techniques as well as learn about the Trace option.

5.     Saving edits was the key not only after creating the feature but also after editing the attribute table for each feature.

6.     I used the clip option for several of the polygons where there was overlap or intersection.  I feel the clip option could have been better utilized when initially creating a plan on digitizing the various classes.   Since the residential and body of water sections were a majority of land use/land cover in this image, in retrospect I probably should have created those class polygons first.

7.     Because I did very little mapping in my last class (Python programming), I am having to get back into the mapping mode.   Working on this map legend helped refresh some of my skills.

8.     I figured out how to display the spatial resolution on the TOC after using ArcGIS Help for spatial resolution.   I did not figure out the best way to calculate or formulate a MMU (Minimum Mapping Unit) for this image.   I roughly chose an MMU of 1/8 of a mile or somewhere between 500-660 ft².

GIS4035 - Module 2 Lab - Visual Interpretation

Having no prior experience with raster files besides my previous introductory GIS courses, the first lab assignment for Remote Sensing had me a little nervous.   The chapter assignments for Module 1 and Module 2 were detailed and lengthy but necessary to provide the background information needed to have an understanding of the history of remote sensing and the technology behind it.   Module 2's focus was on aerial photography.   In Exercise 1 we were tasked to interpret the tone and texture of a provided aerial photograph.   Tone is the brightness or darkness of an area.   We had to identify five areas on the image that depicted variations in tone ranging from very light to very dark.  I realized my mapping skills were a bit rusty but was able to convert identified tone areas to a feature shapefile.  Identifying five varying areas of texture proved to be more difficult as this interpretation seemed more subjective.   Knowing where this aerial photograph was taken might have aided my interpretation.   Variations in texture were on a scale from very fine to very coarse with "mottled" being the middle of the range.   Hopefully as the course progresses my interpretation skills will improve.

Exercise 1 - Tone and Texture Interpretation

Exercise 2 had us examine another aerial photograph and identify different features/objects in the photograph using four different elements of image interpretation (shape/size, pattern, shadow and association).  We had to identify three different objects in the image for each criteria, except association which required two.  Some features were easily found, while others were identified by more carefully studying the image, especially when using the shadow element.   Noting the angle of the shadow and also applying the element of patterns.   For example, at first glance I thought long stick like objects were light/lamposts but after looking at the shadows and noticing a repeating pattern I realized these sticks were utility/transmission line poles.   This exercise was my favorite part of the lab because I began to see how the different elements could be used individually as well as combined to aid in the visual interpretation of an image.

Exercise 2 - Features Identified Using Elements of Image Interpretation

In Exercise 3 we were tasked with comparing a True Color image of an area with a False Color Infrared image of the same area.   This exercise proved to be the most difficult for me.   I identified five different features to compare between the two images.   I tried to understand which colors were being absorbed and which colors were being reflected.   Some of the color differences of my selected features did not make sense to me.   What appeared as a field in the True Color image appeared as water in the False CIR image.   Also, some areas that appeared white in the True Color image appeared as blue water features in the False CIR image while other white areas remained white.   Again, having an idea of the location of these images would have aided in the visual interpretation of the two raster files.   It could also be that the images were taken at different times, possibly months apart, and this would explain why the True Color image showed a feature as a field while the same area in the False image appeared as water.   Possibly during construction of the area the field had  originally been a water retention area.   Some areas that appeared as water (blue) in the False image that did not appear as water in the True image may have changed due to a flow release from a nearby power plant or industrial facility.   I realized after this exercise how important it is to know your study area when possible.

I am looking forward to learning about remote sensing technology and how to best use it in GIS applications.