Intro to ERDAS Imagine

The basics and navigating of ERDAS Imagine were gone over, with the end result being a portion of a subset map we were analyzing exported to be used in ArcMap. We created a new area column in the attribute table for the subset image in Imagine and exported a small portion of the image (of our choosing) to be finished in ArcMap. In ArcMap, map essentials were added, as well as a more descriptive legend concerning the extra area column we created from Imagine.

Here is my final output map:

SWIR Supervised Classification - Angkor Wat

For my training sample of Angkor Wat I used a SWIR (short-wave infrared) composite image. From the USGS Earth Explorer search image options, I choose the Landsat 7 image taken on January 10, 2002. I narrowed down this image to be the clearest of cloud cover. I added nine additional classification features in addition to the temple feature. These are Water (grouped all large bodies of water into this), Urban, Dense Forest, Cloud, Cloud Shadow, Crop (which was hard to separate from ground, urban, and rivers), River, Grassland, and Forest. I created and merged additional samples for Crop, River, and Urban, as these samples created the most issues while classifying. The training sample I took of the temple resulted in several large clusters of potential archaeological sites.

Ground Truthing

In this lab, 30 sample points were chosen at random in order to conduct a ground truthing analysis. This analysis determined how accurate I labeled features on my map. Google Maps was the source used to confirm or deny correct identification of select features in a classification area. My Overall Accuracy was 60%.

My map output below:

Identifying Maya Pyramids: Data Analysis

The main focus of this week's lab was to conduct an Interactive Supervised Classification of a training sample created based on features in the Composite Band 4, 5, 1 image. First, a NDVI image was created in order to determine if it was suitable for analysis and detection of Maya pyramids. NDVI measures biomass, giving us a view of plant growth and stress. As the pyramid had not been discovered at this point in the image, or cleared of vegetation, it is not suitable to use for the detection of this pyramid. The 4, 5, 1 image predominately shows vegetation as reds and bare ground as greens. Band 5 is included to aid in the detection of plant growth or stress. This image was used for the training sample for the Interactive Supervised Classification. In order to get the classification image, features are selected on the 4, 5, 1 image to be represented as colors on the classification image. This way, if a pyramid feature is represented as red, other features identified in the process as pyramids show up as red. This helps predict future archaeological sites.

Below is my final map output, with additional information included:

LULC Classification

This week's lab required the classification of land use and land cover of a portion of Pascagoula, MS.
The USGS Level II classification scale was used as a reference in determining how we should classify certain land types.

Here is my map outcome

Identifying Mayan Pyramids: Data Preparation

In this week's lab, our main focus was classifying and applying raster imagery to a real world scenario. We will analyze this imagery to identify Mayan pyramids in Mexico.

Below is my map output from this week's lab assignment.

The Landsat raster data was downloaded from USGS Earth Explorer and covers a portion of the Rio Azul National Park in Mexico. The downloaded data came with 9 different (raster) bands, for this lab we worked with Bands 1, 2, 3, 4, and 8.

Band 1 = Blue, Band 2 = Green, Band 3 = Red, Band 4 = Infrared, Band 8 = Panchromatic

Band 8 is panchromatic and has the highest resolution (at 15 m). It appears in black, white, and grey tones. Natural Color is the combination of the Bands 1, 2, and 3. They were combined using the Composite Bands tool in the Image Analysis window of ArcMap. The result is a natural looking color image. False Color is the combination of the Bands 2, 3, and 4, also using the Composite Bands tool. In false color, vegetation appears red because it readily reflects infrared energy.

Module 2 - Aerial Photography Basics & Visual Interpretation of Aerial Photography

In this lab, aerial photographs were examined and analyzed based on tones, textures, and features.

This map shows the varying tones and textures identified in the aerial photograph.

This map shows the varying patterns, shadows, shapes and sizes in the aerial photograph.