Remote Sensing Term Project: LIDAR Road Management Application

Follow this link to the full lab report:
https://drive.google.com/file/d/0B36dlU8PtG9pMmxBVjhUZzI4OGc/view?usp=sharing

Remote Sensing Lab 8: Spectral Signature Analysis and Resource Monitoring

   Background and Overview: In this lab I became accustomed to some procedures having to do with spectral signatures; simple and manual procedures were practiced, and also those using an index function were practiced (we used the NDVI and Ferrous Mineral index functions).

   Methods:
Part 1 (Spectral Signature Analysis): Opening the image supplied by my instructor of the Eau Claire Area, I clicked on the Drawing tab then the polygon button to draw a polygon over a large area of Lake Wissota, our area of spectral interest. I now clicked on the Raster tab, then on Supervised, and then Signature Editor in order to open the Signature Editor window. I now clicked on the Create New Signature From AOI button on the top tool bar of this window and renamed the created signature Standing Water. After this I clicked on the Draw Mean Plot Window icon in order to see the spectral signature drawn on a graph. I next did the same for all of the following types of terrain: standing water, moving water, forest, riparian vegetation, crops, urban grass, dry soil, moist soil, rock, asphalt highway, airport runway, and concrete parking lot. All of these spectral signatures can be seen below in the results section as signature mean plots showing the spread of reflectance across bands. I was also asked a few questions about these signature and the answers are listed here:

1. Water reflects most energy in the visible bands and especially in the blue (hence it looks blueish) and it absorbs the infrared and thermal bands quite well.
2. Vegetation displayed high NIR reflectance because it really just absorbs the visible band to make food and reflects higher bands to prevent damage. It looks green because it absorbs mostly much more red.
3. Dry and moist soils differ most in their reflection of the MIR band because of the MIR water absorption.
4. The following are comparisons of spectral signatures: Vegetation, urban grass and riparian vegetation are very similar because they are all live plant life! Airport runway and highway are also fairly similar but less similar. Moving water and crops are very different.
5. Bands three, four, five, and six seem like they have a lot of weird variations even among similar surfaces so I would choose these bands if I had to choose 4 bands to include in a sensor for differentiating these surfaces.

Part 2 (Resource Monitoring) Section 1 (Vegetation Health Monitoring): In this section I used the normalized difference vegetation index (NDVI) to create a new raster file based on its results of an image of the Eau Claire and Chippewa counties area. I brought the image into a viewer in ERDAS Imagine, then clicked the Raster tab, then Unsupervised, then NDVI. I made sure to input the specific image, and name the output image appropriately and save it in my folder, then selected the appropriate Landsat 7 Multispectral sensor and the NDVI index in the Indices window. I next clicked run and then opened the resulting image in ArcMap to display the raster in an appropriate 5 class and equal interval classification and symbology system and then created a cartographically pleasing map. I also when asked noted that the white areas are areas of a high index value, denoting areas of high vegetation health, and gray and black areas denoted either water or lower vegetation health. My map is included in my results.

Part 2 (Resource Monitoring) Section 2 (Soil Health Monitoring): In this section I used much the same procedure but instead of using the NDVI function and index I used the Ferrous Minerals index. I also created a map in the end in ArcMap with the same classification system and it too is included in my results. When asked I noted that the ferrous minerals are more prevalent in the south-west half of the image.

   

   Results:

Part 1 Signature Mean Plots:

Riparian Vegetation

Airport Runway

Asphalt Highway

Standing Water

Standing Water

Dry Soil

Vegetation

Urban Grass

Parking Lot

Crops

Moving Water

Part 2 Maps:

Ferrous Mineral Content in Eau Claire and Chippewa Counties

Vegetation Health (NDVI) in Eau Claire and Chippewa Counties

Sources: Imagery and instruction was provided by the instructor, Dr. Cyril Wilson.

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

Remote Sensing Lab 7: Photogrammetry

   Goals and Background: In this lab we learned how to do many different tasks pertaining to photogrammetry and orthorectification. These included finding scales and relief displacement, making stereoscope images, and performing orthorectification on satellite images.

   Methods:

Part 1 (Scales, Measurements and Relief Displacement) Section 1: In this section we used two equations to find scale in different problems. The first equation was the simple picture distance/ground distance=scale. The second had to do with focal length and height the aerial image was taken from (scale=focal length/height from ground).


Part 1 Section 2: In this section we used the measurement utility to digitize the perimeter of a lagoon to find area and perimeter. After opening the image supplied by my professor in a new viewer in ERDAS Imagine, I clicked on the measurement button under the Manage Data tab with the Home tab opened, then created a polyline to measure the perimeter and a polygon to find the area. The resultant length and area were then displayed in the view measurements table at the bottom of the screen.

Digitization for Measurement

Part 1 Section 3: 

Part 2 (Stereoscopy) Section 1: In this section, we created a stereoscopic image from a DEM and an aerial image of the city of Eau Claire using the anaglyph generation function of ERDAS Imagine. To create this image I clicked on Terrain, then Anaglyph, and tuned the settings as are shown in the image below. The resultant file could be viewed in 3D with red and blue glasses.

DEM Anaglyph Generation

Part 2 Section 2: In this section I created a stereoscopic image using an aerial image of Eau Claire and a LiDAR derived DSM using the same technique as the last section. This created a much higher spatial resolution image with greater three dimensional detail that is shown below.

Part 3 Section 1: In part 3 we used already orthorectified imagery as a source for ground control points for the orthorectification of two SPOT panchromatic images of Palm Springs, California. We used the Erdas Imagine Lecia Photogrammetric Suite (LPS).

To begin section 1 I opened a fresh viewer in Erdas Imagine, created an orthorectification output folder in my personal storage, and opened the LPS Project Manager by clicking on Toolbox, then on IMAGINE Photogrammetry. Clicking on the create new block file icon, I created a new block file in my previously mentioned output folder with a specific name. With the resulting Model Setup window open I chose the Polynomial-based Pushbroom option, then chose the SPOT Pushbroom specification in the Geometric Model Category as my data was from SPOT. Now in the Block Property Setup dialog I set the Horizontal Reference Coordinate system to the appropriate UTM projection type, Clarke 1866 spheroid, NAD27 (CONUS) datum, UTM zone 11 and North. I now set the Horizontal units to Meters.

Part 3 Section 2: I now added the imagery needing to be orthorectified and defined the sensor model of the block. I began by clicking the image folder in the tree view on the left side of the project manager and then clicking the add frame icon next to the save icon at the top. Navigating to the Lab 7 folder, I inputted the first SPOT panochromatic frame. I now clicked on the Show and Edit Frame Properties icon (the one with the lowercase I) at the top of the project manager. Reviewing this info I clicked okay.

Part 3 Section 3: In this section I began to collect GCPs with the point measurement tool and set my vertical reference source to import Z elevation data. Clicking on the point measurement tool icon at the top of the manager (the circle with crosshairs), I began the tool, selecting the classic point measurement tool when given the option. With the tool open, I clicked the reset horizontal reference source icon in the upper right grouping of icons (the icon with the black and white circle and the horizontal double arrowed line under it). I now navigated to the first Orthorectification subfolder of the Lab 7 folder my instructor provided and I selected the spot image to be used as a reference. After clicking okay, I checked the Use Viewer As Reference box in order to view the reference image side by side. Moving the inquire boxes appropriately in order to find matching points, I now selected the add button, then clicked at the same point in both images to add my first reference point. I did this 9 total times, after the second clicking the Automatic (x,y) Drive icon in order to ease the finding of matching areas, each time zooming to the a large scale in order to get a high degree of accuracy for later computation.

After creating my ninth point, I clicked save to save my progress. I now reset the Horizontal Reference Source again in order to to use a different source. This next source was an already orthorectified aerial photo rather than SPOT satellite data. Creating a new point, I changed the Point ID to 11 instead to 10 like the Point number to note the change in source, and then created another point.

Now I set my vertical reference source by clicking its icon which was similar to the set horizontal reference source icon. Selecting DEM, then find DEM, and setting my source to my DEM supplied, I set my vertical reference source. Now, with all point numbers selected, I clicked the Z icon which updated all of my elevations from my past specified source.

Part 3 Section 4: In this section I set the type and usage of the points collected, and added a second image to the block, collecting its GCPs. I clicked on the title of the type column to highlight it, then right clicked on the column and selected formula, then Full in order to label the coordinates of each point as full. I now repeated this process in order to label all of the usages of the points as control, designating them GCPs. 

I now saved and closed in the Point Measurement Tool in order to get back to the manager. In the manager, following the same procedure as I did to add the first block image, I added the second spot panographic image to be orthorectified. I also again clicked on the frame properties icon again and clicked okay in order to let the software know that I had verified the properties. Opening the Classic Point Measurement Tool again, I began to collect GCPs from the first image for the second. Adding a new point and selecting a spot at first in the second image and then in the original image, I matched points for use as control points for the second image. I did this for every point that was contained in the overlap between the two images that already existed. I then clicked save.

Part 3 Section 5: In this section I did the last necessary processes to finish the orthorectification process. I first clicked the Automatic Tie Point Generation Properties icon. I set the image used to all available, the initial type to Exterior/Header/GCP and the Image Layer Used for Computation to 1, then I changed tabs to Distribution and set the Intended Number of Points/Image to 40, made sure the keep all points option was unchecked so poor tie points were discarded and clicked run. After checking the tie points for accuracy, I saved and closed the Point Management Tool. I now clicked edit, then triangulation properties, changing iterations with relaxation value to 3, then image coordinate units for report to pixels. Changing to the point tab, I changed the x, y, and z SDs to 15, then checked the Simple Gross Error Check Using box and clicked run. Opening and saving the report generated from the triangulation, I clicked the Start Ortho Resampling Process icon selecting my appropriate DEM file name, setting the output cell sizes to 10 for both x and y, setting a descriptive output name in my personal storage, setting the resampling method to bilinear interpolation, adding my second image to correct, and clicked run to finish the entire process. I saved my block and then viewed my orthorectified images.

 

   Results:

Final Result of Orthorectification (both images shown)

LiDAR Derived Stereoscopic Image

DEM Stereoscopic Image

   Sources: All sources were provided by the instructor.

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.