Lab 8 - Spectral signature analysis & resource monitoring

Goal and Background

The goal of this lab assignment was to gain experience with measuring and interpreting the spectral reflectance of various Earth features as well as performing basic Earth resource monitoring using remote sensing band ratio techniques.

Methods

Part 1 - Spectral Signature Analysis

For part one of this lab, the task was to measure the spectral reflectance of 12 different surface features present in the image provided by the professor.  These features were standing water, moving water, deciduous forest, evergreen forest, riparian vegetation, crops, dry soil, moist soil, rocks, asphalt highway, airport runway, and concrete surface.  To do this, I first used Drawing > Polygon tool to draw a polygon on the feature I wished to view the reflectance of.  With this polygon made, the next step is to select Raster > Supervised > Signature Editor tool.  Using this tool, I was able to view the reflectance for all 12 of the features needed as well as view a spectral plot of each features signature.

Part 2 - Resource Monitoring

For the second part of this lab, the task was to use band ratio techniques to monitor the health of vegetation as well as the iron content of soil.  To first find vegetation health, I used the Raster > Unsupervised > NDVI tool, inputted the correct data, and ran the tool.  Then, I took the output image and opened it in Arcmap to create a more visually pleasing map with five distinct classes.  To measure soil iron content, the Raster > Unsupervised > Indices tool was used with the 'Ferrous Minerals' function chosen as an input.  Once the tool was run, I similarly opened the output image in Arcmap to once again create a better looking map with five distinct classes.

Results

Spectral Reflectance of Standing Water

Spectral Reflectance of Dry vs Moist Soil

Spectral Reflectance of All Features Tested

Sources

Satellite image is from Earth Resources Observation and Science Center, United States Geological Survey

Lab 7 - Photogrammetry

Goal and Background

The primary goal of this lab exercise was to develop our skills in various photogrammetric tasks to applied to aerial and satellite images.  This lab was designed to help us comprehend the mathematics of calculating visual scale, the area and perimeters of features, and relief displacement.  This lab also introduced us to the concepts of stereoscopy and orthorectification. 

Methods

Part 1 - Scales, Measurements, and Relief Displacement

Part 1 of this lab was focused around calculating scale, area and perimeter measurements, and relief displacement.  To calculate the scale of the aerial images we were given, the equation s = pd/gd was used where s is the scale, pd is the photo distance, and gd is the real world distance.  To calculate the perimeter and area of features, the 'Measure > Polygon' and 'Measure > Polyline' digitizing tools in Erdas Imagine were used to calculate the perimeter of an object in meters and in miles and the area of the same object in acres and hectares.  Finally, to calculate the relief displacement of a tall object we were given an aerial photograph, its scale, and the altitude of the sensor at the time the image was taken.  Using this information the equation d = (h * r)/H was used to calculate the relief displacement.  In this equation d is the displacement, h is the height of the real world object, found by using the provided scale and measuring the photo height of the object, r is the distance from the top of the object to the principal point of the image, and H is the height of the camera.

Part 2 - Stereoscopy

Part 2 of this lab was all about creating stereoscopic images using both a DEM and a LiDAR derived DSM.  To do this, the 'Terrain > Anaglyph > Anaglyph Generation' tool was used in Erdas Imagine.  Using the DEM and DSM as well as the provided image of the city as the inputs, the tool was run and the output images saved.  These output images are Anaglyph images that can be viewed with a Stereoscope. 

Part 3 - Orthorectification 

Part 3 of this lab was all about using the Erdas Imagine Lecia Photogrammetric Suite (LPS) for triangulation and orthorectification.  Using the images provided by the professor, I first created a new Photgrammetric Project, added in the necessary images, and specified the sensor to correct the Interior Orientation.  Next, to correct the image horizontally, I used the 'Classic Point Measurement' Tool to add GCP's to the image I was working on orthorectifying from a reference orthorectified image provided by the professor.  Once eleven GCP's were added to the first image, I repeated steps for creating GCP's with a second image to correct the image vertically.  Once all teh GCP's were collected for both reference images and corrected both horizontally and vertically, I ran the 'Automatic Tie Point Generation Properties' tool to collect 40 tie points.  Finally, the 'Start Ortho Resampling Process' tool from IMAGINE Photogrammetry Interface could be run.  In the 'Ortho Resampling' Dialog, I added the images to the correct inputs and ran the tool.  Once the tool was finished running, the output images were properly orthorectified.  

Results

Sources

National Agriculture Imagery Program (NAIP) images are from United States Department of Agriculture, 2005

Digital Elevation Model (DEM) for Eau Claire, WI is from United States Department of Agriculture Natural Resources Conservation Service, 2010.

Lidar-derived surface model (DSM) for sections of Eau Claire and Chippewa are from Eau

Claire County and Chippewa County governments respectively.

Spot satellite images are from Erdas Imagine, 2009.

Digital elevation model (DEM) for Palm Spring, CA is from Erdas Imagine, 2009   

National Aerial Photography Program (NAPP) 2 meter images are from Erdas Imagine, 2009.