Lab 8: Spectral Signature Analysis & Resource Monitoring

Goal 

The main goal of this lab is to gain experience on the measurement and interpretation of spectral signatures of various Earth surface and near surface materials captured by satellite images and also perform basic monitoring of Earth resources using remote sensing band ratio techniques. Multiple spectral signatures will be collected from an image and analyzed.  Techniques for monitoring soil and vegetation health will also be explored.

Methods

Collecting Spectral Signatures from a Satellite Image

The Landsat ETM+ image eau_claire_2000.img was brought into ERDAS Imagine and 12 measurements of spectral reflectance were taken.

1. Standing Water
2. Moving water
3. Forest
4. Riparian vegetation.
5. Crops
6. Urban Grass
7. Dry soil (uncultivated)
8. Moist soil (uncultivated)
9. Rock
10. Asphalt highway
11. Airport runway
12. Concrete surface (bridge, parking lot, or any type of concrete surface)

To do this the polygon draw tool was used to create a polygon in the area of interest.  The Raster - Signature Editor tool was used to create a list of the 12 signatures.  The signatures were then plotted on a signature mean plot to show the reflectance in each of the 6 bands.

Figure 1

Multiple spectral signatures could be put on one mean plot to compare and analyze the signatures.

Figure 2

Resource Monitoring

To monitor vegetation health a simple band ratio was performed by implementing the normalized difference vegetation index (NDVI) on an image of the Eau Claire and Chippewa area. To do this the Raster - Unsupervised - NDVI tool was used.  The satellite was specified and NDVI function was selected.  The tool then created a separate NDVI image. This image was imported into ArcMap and the data was classified into five groups to create a map of vegetation health.

To monitor ferrous minerals, a similar process was used as the vegetation monitoring but this time a different band ratio was used.  The Raster - Unsupervised - Indices tool was used this time.  The satellite and function were selected and an image was created.  This image was imported into ArcMap and classified into 5 classes to create a map of Ferrous Minerals in the Eau Claire, Chippewa area.

Results

The signature mean plot with all signatures included allows for analysis between the different materials on earth (Figure 2).  

After the NDVI image was created, a cartographically pleasing map was made in ArcMap to show areas that have healthy, to no, vegetation. A map like this could be useful in many studies. 

  

A similar process was used to create a map of ferrous minerals in Eau Claire. Ferrous minerals are present in many common metal used for construction.  The white areas on the map below are mostly populated areas.

Conclusion

Having the ability to extract spectral signatures of different materials is a useful skill that has many real world applications.  It is a simple process that can save time and money.  Instead of having to send multiple people out into the field with expensive equipment, a simple analysis can be done in house and accurate results can be found.  Monitoring techniques of vegetation and soil done on a computer can also save time and money.  It can raise questions about an area or answer questions.

Sources

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

Lab 7: Photogrammetry

Goal:

The main objective of this lab was to develop skills of performing photogrammetric tasks on aerial photographs and satellite images. More specifically, this lab was to help understand the math behind the calculation of photographic scales, measurement of areas and perimeters of features, calculating relief displacement, and introduce stereoscopy and performing orthorectification on satellite images.

Methods

The first section of the lab was to calculate scale of a photo when measuring the distance between two objects on the screen and using that distance and the distance in real life to come up with a scale.  This resulted in a scale of 1/39,393.  The distance measured was from point A to point B.

The next thing done was to measure perimeters and areas from a image.  The measure perimeters and areas tool was used in Erdas to measure the area and perimeter of the lake in the middle left portion of the image above.  The pond has a area of 94.5 Acres and a perimeter of 2.54 Miles.

The next section was to calculate relief displacement from object height from an image. The camera height and scale of the image were known.  The relief displacement was 4.58 in.

The next part of the lab was to generate a 3D image using an elevation model.  To do this the Terrain-Anaglyph tool was used in Erdas to combine a digital elevation model with an image of the study area with a 1 meter spatial resolution. This image below is 3d when viewed with Polaroid glasses.  

This same process was done with a digital surface model.  The result is seen below. 

The last part of the lab was to orthorectify images and create a planimetrically true othoimage. Erdas Imagine Lecia Photogrammetric Suits (LPS) was used to perform this task.  A new block file was created in LPS and certain parameters were set.  This geometric model interface was set to polynomial-bases pushbroom and the geometric model catagory was set to SPOT pushbroom.  After this the coordinate system and projection type were chosen along with other parameters.  This puts the images that are going to be used in the process to the same coordinate system and projection.  

The next step was to bring in satellite imagery of Palm Spring, CA (spot_pan.img) and add it to the block in LPS.  No changes were done to the image but the verification process was completed to specify the sensor.  Next, ground control points were collected using the Classic Point Measurement Tool in LPS using a reference image.  The corresponding points were collected on the spot_pan.img using the point measurement tool.  Using the automatic drive function, the remaining GCP's were collected. The last two points were collected from a different image with a different horizontal reference source.  Elevation information was collected from a DEM file using the Reset Vertical Reference Source button in the point measurement tool palette. 

The next step was to bring in another image (spot_panb) and GCP's were collected for this image.  The GCP's for this image were collected using the already rectified spot_pan image.  Tie points were then collected for both images using Automatic Tie Point Generation Properties in the Point Measurement Tool Palette.  Parameters were set and the accuracy of the tie points were confirmed to make sure the image was properly orthorectified. 

LPS project manager was then used to perform Triangulation.  Parameters were set and the resulting text file was saved.

The final orthorectified image was created by using the Start Ortho Resampling Process in LPS.  Parameters were set and images were created.  The images were then brought in and overlapped to compare the spatial accuracy of the images.  Seen Below.

Results:

Anaglyph using DEM

Anaglyph using DSM

Orthorectifed Images

Conclusion:

When using satellite images, scale, perimeter, area, and relief displacement can be calculated and used for further analysis.  Anaglyphs can be created from elevation and surface models to make 3D models that would be useful in many situations.  Lastly, Erdas makes orthorectifying images relatively simple.  This makes a planimetrically correct image that has a constant scale that represents objects where they truly are. 

Sources:

Hexagon Geospatial. (2009). Erdas Imagine [computer software]. Georgia: Norcross. 

United States Department of Agriculture. (2005). [Satellite images in img. format]. National Agriculutre Imagery Program. Retrieved from: https://gdg.sc.egov.usda.gov/.

United States Department of Agriculture Natural Resources Conservation Service. (2010). [Digital elevation model for Eau Claire in dbf. format]. Retrieved from: http://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home/.