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Spectral Signature Analysis & Resource Modeling

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Goals: The goal of this lab was measure levels of spectral reflectance from various surface and materials. We also evaluated natural resources using remote sensing bands. We focused on Eau Claire and Chippewa counties for this lab. Methods: Part 1: Spectral Signature Analysis We used Erdas and ArcMap to process the data needed for this lab. Part 1  of this lab focused on measuring and plotting the spectral reflectances of 12 surface types. In order to identify different surface areas with more ease I reference the satellite image to google earth. We used the polygon tool in Erdas to draw an area within the desired surface. Then we used the signature editor tool to view the area's spectral signature mean plot. We collected all 12 surfaces and displayed their spectral reflectance signature mean plots together for further analysis (Figure 13). We analyzed vegetation and moisture content mostly. Figure 1. Spectral Signature Mean Plot for airport runways Figure 2. Spectra
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Photogrammetry

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Goals: The purpose of this lab was to develop skills in photogrammetry. The main goals of this lab were to  calculate scales,  measure areas and perimeters,  calculate the amount of relief displacement. Also a focus was to develop skills in  stereoscopy and finally  orthorectification of satellite images Methods Part 1: Scales, Measurements and Relief Displacements   To be able to  calculate the  scale of a vertical aerial image we c ompared the size of objects measured in the real world with the same objects measure on a photograph and used the scale to complete the mathematical equation. We also used an equation f inding the relationship between the camera lens focal length and elevation of the aircraft. We then created a polygon shape around the edge of the lake in order to calculate the water bodies' area and perimeter. Relief displacement which is the displacement of a part of the image was corrected by applying an equation and reprocessing the image.
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Geometric Correction

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Geometric Correction Goals: This lab was an introduction to the pre processing method used when dealing with images that need to be geometrically corrected. This lab focused on  image to map rectification using a reference map to find points on the landscape and image to image registration which compared two images.   Methods: Part 1: Image to Map Rectification The first part focused on geometrically correcting an image in Erdas using the image to map rectification method.  To do this, I first opened both the image to be rectified and the reference image in Erdas Imagine. Using processing tools and the Create GCP tool we created ground control points (GCPs) which is part of the geometric correction process. This first section dealt with a 1st order polynomial transformation of images. For this 1st order polynomial transformation a minimum of three GCPs were needed. We added points until the model solution was current. These GCPs needed to be spread out over the images in
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LiDAR

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Goals: The goal of this lab is to work with LiDAR data and be able to process and retrieve various surface terrain models. Also to create and process intensity images from point cloud data. Working with point clouds in LAS data formats is another goal of this lab. Methods: In this lab Erdas Imagine GIS techniques and ArcMap were utilized. Part 1: Point Cloud Visualization in Erdas Imagine In the first part of this lab we loaded LAS files of Eau Claire into Erdas Imagine. This was to check for any errors in the data. The next step was to look over the metadata. We then analyzed the same data set  in ArcMap and further manipulated the data in this program. (Figure 1)  Figure 1. Eau Claire data in Erdas Imagine Part 2: Generate a LAS Dataset and Explore LiDAR Point Clouds with ArcGIS In this part of the lab we created a LAS dataset and explored the properties of this dataset. We then began applying visualization techniques to the LAS dataset and manipulat
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Image Enhancement

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Goals:  The purpose of this lab was to develop skills in image enhancement, mosaic, model building, image enhancement for visual interpretation and processing techniques. The lab work on altering the spatial resolution of images for analysis as well as enhancing the radiometric quality of images and linking Google Earth Pro and Erdas viewers in order to analyze and interpret features and their associations to the surrounding landscape.  Methods:  In this lab we used the inquiry box tools and raster tools. To enhance the spatial resolution of the satellite images we pan sharpened them using the resolution merge tool. The image was further enhanced by re sampling the pixels by using both the nearest neighbor method and the bi linear interpolation method. We also reduced the haze on an image by using radiometric enhancement using the haze reduction tool provided on the toolbar. Results: Part 1: Image Sub Setting This part looked at two sub setting methods. The firs
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