Module 5: Conversion of LIDAR to Raster Image

This is Module 5 LIDAR Challenge Assignment. We were required to take raw LIDAR data and transform it into a raster image in ArcMap. I first imported the data into Excel and added the appropriate headings. This made the data easily importable into ArcMap. Then using the Inverse Distance Weighted (IDW) tool in the spatial analysis tool set,

I was able to produce a rastere image of the data points. I set the symbolism to stretched and chose an appropriate color ramp to highlight features. Then in ArcCatalog, I created 3 new shapefiles(Water,Dunes, and Road). I added ighlighted the the new shapefiles to my map and edited the specific features. I then added a grid as instructed spreading the grid interval a bit. I then added all other required features like north arrow and legends, scale bar and text. I also added a small description of what the image was and how it was produced. Finally, I highlighted the features in the image that were identified such as the road,waters, and sand dunes. I only had problems with projection. Like most, I got a scale of over 4400 miles at first. I went back and redid the image with the correct WGS 1984 UTM Zone 16n projection in Universal Tranverse Mercator and did not have any other issues.

Module 4: Supervised Classification Challenge

This week we were required to take an image of Germantown, Maryland and do a Supervised Classification in ERDAS Imagine of the image to known land cover types. We were required to use 14 categories and label each type. Then using histogram values, we were to categorize the land in the image.
I really did not have too many problems with the assignment. I was able, through trial and error and the known value ranges, to match all but two categories. The grass areas and Ag 4 areas had a bit high histogram values. I found that if I adjusted these values many other categories fell out of range so I felt that this was as close as I could get. I did spend many hours adjusting the Euclidean Distance values to get the histogram values within range.

Week4:Supervised Classification

Module 3- Orthorectification Challenge

This is my submission for the Module 3 Challenge Assignment for GIS 4035 Remote Sensing and Photointerpretation at the UWF Online GIS. We were required to take an image of downtown Pensacola and orthorectify the image to an existing map using GCP or Ground Control Points. The requirement of a RMS error of under 1. Using 7 control points, I was able to get low RMSE numbers with an Avg. error of .2545.

Challenge3 Map
GCP Table

The problems I had with the assignment were in the practice section. I could not get my RMSE below a couple thousand on some of the fiducials. I did not have this problem with the challenge. The only problem I had in the challenge waas that I had to move my UTM grid slightly to the left in order to see the numbers better on my map. I think this might cause a slight error in my Longitude on the grid.

Week 2: Spectral Bands Basics

Disregard this post.

Week 2: Spectral Bands Basics Challenge

Below are the 3 maps required for GIS 4035L/Remote Sensing and Photointerpretation for Summer term at UWF online. The first map shows a water feature which we were required to identify using the different spectral bands and their associated pixel values. The spike in the histogram for layer_4 between pixel values 12-18 are associated with the many water features in the image.

The second map we were required to determine from the histogram of Layer_5 and Layer_6, what sort of feature would likely cause a spike around pixel values 9-11. At first glance, I thought it was snow but after a little research I determined it was a glacier feature. The image we are looking at is within the state of Washington and around the Mt. Rainier area. This area is well known for its glaciers.

Finally, the third map we are required to identify a feature in the image of shallow water. I changed the image to better show the feature by using LandSat 5 TM Bathymetry Red: 3, Green: 2, Blue: 1. This enable me to easily identify the shallow water feature in the SW region toward the Pacific Ocean as an area where the feature was present.

Map1: Water Feature

Map2: Glacier Feature

Map3: Shallow Waters