Lab 8:

Goal:

The main goal of this lab was to introduce us to the ability to use reflectance output as a means of analyzing the earth surface. We would accomplish this by gaining a better understanding spectral reflectance signatures of various earth surfaces including water, asphalt, vegetation, rocks, and soils. We need to gain a better understanding for this process if we plan to advance our skills in the remote sensing world for if we ever need to classify structure we first need to be able to read the spectral reflectance output.

Methods:

To obtain a spectral reflectance output we first needed to digitize a small area of known structure. In the first case we navigated to Altoona lake and digitized a small portion of the lake. Once we acquired our digitized area we then could run it through a raster signature editor. The editor would generate a print our of the spectral reflectance and the proportion of each band that was showing (figure 1).




Figure 1: spectral reflection signature for standing water of lake Altoona.

We then repeated this process for the following surfaces

standing water

moving water

vegetation

riparian vegetation

crops

urban grass

dry soil

moist soil

rock

asphalt highway

Airport highway

concrete surface (parking lot)

Once collected we could place all of the signatures onto the same graph and infer the following. A) what reflectance bands would be best suited for collection of these surfaces ( in this case band 2-5). B) why some of the same material had different spectral reflectance (may be caused by variation in moisture). Last why would some areas not show similar results to what we expected ( could be due to high cloud cover).

Figure 2: All spectral signatures in one graph

Figure 3: NDVI model for Eau Claire county

Section two of this lab was to introduce us to a widely used application in remote sensing. A tool used to monitor the health of vegetation. It is known as a NDVI or a normalized difference vegetation index. This index can show you the areas that are represented by high loads of healthy vegetation and does so by changing all of the band number to print out as a one or a zero. Any area that prints out as a one will be very bright white and represents healthy vegetation, while the dark greys and blacks are non-healthy vegetation or urban areas. The out put for the eau Claire area can be seen in figure 3.

The second model is of similar properties but generates an output to locate highest ferrous material. Material that is critical for mining soils. This model similar to the NDVI will calculate all of the exposed soil and place one values in all areas of high ferrous material. The output can show any user high valued areas for mining. Although one needs to be careful with this output because as shown in figure 4. The output can not analyze any areas that are covered and it can be skewed based on farmland areas that have placed ferrous material back into the soil when it may not originally be there.

Figure 4: Shows the eau Claire county ferrous material output

Results:

The results of this lab were very straight forward. We were able to better understand the use and necessity of a spectral reflectance signature. These are very useful for any user that needs to classify areas within an image.

We also where able to generate both a NDV and a ferrous model which enlightened us not only on the tools needed to run these models but the areas around our homes that have healthy vegetation and areas with high ferrous. As you can not comparing the two images there is a direct correlation to areas of low vegetation and or unhealthy vegetation and areas of high ferrous this could have been caused by lack of exposure to bare soil in the models.

Sources:

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

Survey.

Lab 7: Remote Sensing of the Environment

Goals:

The main goals of this lab is to introduce all students to the basic mathematics behind photogrammetric correction. The lab is designed to to train us on photographic scaling, area calculations and calculating relief displacement. From these skills all students will be introduced to stereoscopy and orthorectification both of which are essential skills any remote sensing technician needs to know. We were tasked with three parts to this lab. Pat one introduced us to scales and relief displacement. Part two introduced us to stereoscopy, technique used  for enhancing the illusion of depth in an image. Part three of the lab was introducing us to the process of orthorectification, process of removing 

Methods:

Part one:


Part one of the lab consisted of us students grasping the concepts of image scaling and correction of relief displacement for an image. In order to understand image scaling we were tasked with two objectives calculate the representative fraction of an image using known ground measurements against image measurements. Second was to find the representative fraction using focal camera length and elevation of capture. For the first process one must known the true distance or length of a known object on the ground. Then with in the captured image you will measure the same object or distance. In our case we measured a portion of interstate highway 94. We found that the image distance was 2.7 inches and the ground distance was 105,869 inches this gave us a representative fraction scale of 1:39,000. For the second calculation we knew the height the object was captured and the focal length so we where able to use the calculation S= f/ H-h  or scale is equal to focal length divided by Height of camera minus the height of the terrain elevation. This calculation also generated a representative fraction of 1:39,000 supporting the previous claim and double checking our answers to the right scale.


The second section of part one was to introduce us to a tool that is used often within remote sensing applications. The tool we were introduced to was the area/parameter measuring tool. Section two of part one objective was to measure and collect both the area and parameter of dells pond located just south west of the University of Wisconsin Eau Claire. To accomplish this tool we open up a local image of the Eau Calire area and selected the  measure perimeters and areas digitizing tool. Then we needed to digitize the area of interest ( in this case dells pond). After we had finished our digitizing a print out of the area and perimeter would show up. One can then change the units to what ever they desire to obtain the output they want. Section three of part one introduced us to relief displacement and how to calculate the total amount of displacement on an object. We accomplished this task by looking at an image taken in 200 of the upper half of UW Eau Calire. With in this image we found a tall industrial smoke stack that was not corrected yet for relief displacement for it was leaning away from the principle point. To calculate how much we needed to move this we had to obtain two measurements. The first measurement was the height of the stack from the base to the very top (.5 inches). Second we needed to find the distance the object was from the principle point in this case it was 8.5 inches away. Last we where given that the actual height of the object which was 3980 ft. Then using the calculation for relief displacement D= h*r/H
h =  height of object in image
r = distance from principle point
H = true height of object


We found that the smoke stack was leaning .29 inches away from the principle point. From this information we were then able to correct the image and shift the stack back to its proper placement.

Part two:

Figure 1: DSM stereoscopic image produced in Erdas
of Eau Claire area 

Part two of lab seven introduced us to them few techniques that are commonly used within stereoscopy ( or altering an image to display elevation changes). We would produce two final images from this method one would utilize a digital elevation model which would apply all first return from Lidar dataset and the second image would utilize a digital surface model which was obtained from the last return points of a lidar dataset. The procedure for the model where both the same the only changes where the second image being used either the DSM or the DEM.

To accomplish this we first needed to input the image we wanted to stereoscope and the image used for the model. For the first image it was the ec_city image and the ec_dem image. From here we open the terrain- anaglyph tool and anaglyph generator. Within the anaglyph generator menu we then could select the image we would stereoscope in this case was the eau Claire city photo and the DEM model photo to generate the elevation changes. The output image was then generated and although displayed in figure 1 you can only see the elevation changes with access to 3-d glasses. 

The second image was generated using the same steps however, instead of using the DEM for the model image we used the DSM. The output is shown in figure 2 but as stated before without 3-d glasses you will not be able to see the elevation changes.



Figure 2: DEM stereoscopic image produced in ErDas
of Eau Claire Area

Part three:

Orthorectification is a skill set that every remote sensor should be able to accomplish. It is the process of geometrically correcting an image for all three coordinates (x,y,z). The process although challenging is very helpful for it allows any user to be able to correct their image for analysis in any dimension. For this portion of the lab we had multiple steps to accomplish our goal of a rectified image output. First we needed to start a new project within the program. Then by selecting and referencing a horizontal image with GCP's we would be able to correct a second image and use tie points to bring them together. Once the tie points where placed we then could triangulate the images and orthorectify them. This would produce a final image that was corrected in all three dimensional coordinates.

The process of this was as follows. First import a image to be used for horizontal reference. In this case we used the spot_pan image and a spot_panb image both of which where pancromanic band images that had very high spatial resolution making the GCPs easy to collect. After setting the model category to polynomial based push broom we then needed to chose a projection of the images this case was Nad27(CONUS) with a UTM zone of 11. Now we would be able to collect GPC of the images and reference them to the correct geospatial location. On the left of the collection screen we had our reference image which was the xs_ortho and the right was the image we where trying to alter this case was the spot_pan. GCP collection window can be seen in figure 3. The collection of all points where exactly the same other than after the first three points the system will automatically place the reference point for you but you can alter the location to lower your overall root mean square to aim for the ideal of being below .5. The collection would start by adding a new point and then hitting the collection button (referenced as a crosshairs in the upper right corner).

Figure 3: collection window for GCP and orthorectification

Once you collected the twelve GCP the horizontal reference is now complete and you will move on to set the vertical reference. To set the vertical reference you need to select the reset vertical reference located in the tools part in the upper right portion of the point measurement window. From there we used the DEM option and selected the pal_springs_dem model for our reference. Once selected the z field was now filled in for all of our GCP points we previously collected. After changing the type and usage to full and control the points were now corrected.

The next step was to tie the spot pan image to spot pan b image. To do this we will follow similar steps to the GCP process in the previous steps but will be looking for all locations that overlap in the two images. Once we have collected the tie points we then are able to run the orthorectification of the two images. The output will generate a tied image that is corrected for all three dimensional coordinates. Figure 4

Figure 4: orthorectified output image

Results:

The outputs from this lab produced many useful techniques and skills. From this lab we where able to better understand scaling of an image using both ground and image comparison and image referenced scaling. The second portion of the lab introduced us to the application of area and parameter analysis. This tool set is very useful within remote sensing application for it allows users to be able to measure any area of interest or study are they are trying to analyze.

Last we produced both stereoscopic images which can help user to see elevation changes in the areas surface and learned the process of orthorectification which allows users to be able to geometrically correct any image in all three dimensions.

The outputs of the stereoscopy were very interesting for the DSM and DEM model had very different outcomes. The DEM model produced a very aggressive output of the landscape for it used the first returns of the lidar dataset making the elevation easier to see but not a true representation of the actual landscape. Whereas the DSM, was more subtle in its elevation changes making it harder to see some of the areas but a more accurate representation of the natural landscape.

The output of the orthorectification was a little disappointing for even after all of the work and getting my RMS under .5 the images did not match up very well. They where mostly spatially corrected but around critical areas like rivers and roads the images did not fully line up correctly. Although this time I was not fully successful in the rectification I know that the next time I will need to run a model like this I will be more confident in my ability to produce a useful output image.

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.

 

Lab 6: Geometric Correction

Goals: 

Geometric correction is a key image processing in remote sensing. The two main objectives for this lab was to introduce us to the process of collecting and tying ground control points to images. The second process we will be introduced to will be image rectification which will show us how to utilize our geometrically corrected images to realign other images for analysis. Geometric correction is one of the most important aspects within remote sensing and is a tool that ever geographer should know how to accomplish.

Geometric correction allows for images to be placed back into their correct plainometric x,y location and will format pixels into their correct x,y location. Once images have been rectified and geometrically the user now has the ability to extract reliable surface data such as volumetrics. The majority of the correction deals with correcting the overall alignment of the image placing the image into its correct x,y coordinate but it will also correct for any internal errors such as pitch, roll and yaw changes that skewed the image output. 

Methods:

The two main parts of this lab were a) to learn how to collect ground control points and apply image rectification. then b) to apply the new image rectification to register a newer image and geometrically correct the second with a different process. 

Our first task in lab six was to enhance our skills of collecting ground control points and rectifying a image that is geometrically altered. To accomplish his we must first bring in the image that needs to be geometrically corrected in this case we used a image of the Chicago area that was collected in 2000. We then brought in a higher resolution image of the Chicago area to use for collecting and referencing our ground control points. 

From here we opened the control points tool located under multi spectral with the low level of distortion we are able to use the first order polynomial equation and are only required to collect at least three GCP. Once you have opened the control point collector window you need to chose what method of reference type you will use for this particular application. In this case we are using a reference image so we needed to chose the image layer viewer which allows us to place both the distorted image and the corrected image next to one another to collect GCPs. Once the images have been imported the input image is located on the left and the reference image will be positioned on the right. to collect GCPs you will simply click on the create GCP button at the top of the screen that looks like a scope cross heirs. For the first three points you will need to manually place the GCP in the same location on both images. 

You should look for distinctive features to place GCPs such as intersections of highways that will be distinct location or land forms that have defined edges. even corners of buildings are good locations for GCP. After the first three points are collected the rest will synchronize the GCP onto the input image but you will need to manually move them to lower your r-squared value and enhance the overall accuracy of the rectification. A good r-square is .5 or below this will guarantee your rectification to be accurate and able to apply any analysis from that point forward. 

Once you have reached your goal for a root mean square and feel you have enough GCP collected you can now run the resample and rectify the image for geometric correction.  

Part two of the lab was to apply image to image registration this is the process of that takes an already rectified image such as the one that we just corrected and register the next image to correct it to the same x,y coordinates. This part is very similar to part one where we will take a very distorted image of a area in sierra and collect at least 10 GCP with the referenced sierra image to geometrically run a third degree polynomial interpolation however this time we will run a bi linear interpolation for the resampling methods to generate a smoother and more accurate output image of the color contrast. The process used to geometrically correct the image will be the same process as listed before you open control points in the multi-spectral tool bar and input the reference image with the image layer selection. Once at least 10 GCP are collected you can now run the resample and display your out put image.  

Results:

The results of lab six produced two images of both the Chicago area in the 2000 and Sierra. (figure 1) from this lab we where able to geometrically correct two different images with different degree of distortion and experience the skills needed to rectify an image. The lab was able to introduce us to the application and introduce us to the challenges of generating a root mean square under .5. 

In total the lab was very helpful to introducing us to the tools needed for geometric collection with this new skill set I am now able to correctly align any images to their correct plainemetric x,y coordinates. Form there I will be able to analyze a variety of data outputs including volumetrics of soils, or measurements of land features and many more. 

Sources:

Earth resources observation and science center United States Geological Survey

Illinois Geo-spatial Data Clearing House.

Lab 5: LiDAR Applications and basic tools

Basic LiDAR tools and skills 

Goals/Objectives:

The goals for lab five where broken down into three stages. The three parts where separated to introduce all of the students to different tools and skills utilized with LiDAR data sets. The threes sections where

1) Visualizing point cloud data: this step although short was very useful it allowed all the students to be able to understand more clearly what point cloud data is and what the returns do, by looking at the point cloud data within Erdas.

2) Generate LAS dataset and exploring lidar point cloud: Allowed all the students to manage and manipulate the data within ArcMap to calculate the datset, We were able to first generate a LAS dataset, manipulate and calculate the LAS statistics which are used for quality control and assurance. Project the data to correct the orientation of the data. Lastly we explored the different filters and surface displays to understand the capabilities of the different returns. 

3) Generate different surface models with the LAS dataset: Part three introduced the basic tools used with lidar data including DTM ( Digital terrain Model) , DSM (Digital Surface Model), and hillshade tools all three of which generate different output of the terrain found from models of the LAS data.

Methods:

Part one:

Part one of lab three was the only time in this lab we will use Erdas application. In part one all students were able to investigate all of the points that were generated from the LAS data of eau claire county found around the university of Eau Claire. When in Erdas we needed to upload the points to be able to see them within the program. Once uploaded we where then able to see all of the points including all points from first return to last return. (Figure 1)

Figure 1: point cloud generated and displayed in Erdas Imager to show all of us the returns and
the overall layout of our future LAS dataset.

Part Two:

Generateing LAS data and displaying different surface models within ArcMap was a new toll set that many of us students had not been introduced to before this lab. In order to fulfill the task we first needed to upload our LAS data set and to visualize the outputs. To accomplish seeing the data we needed to set up a few parameters prior to bringing in the data. 

First we all needed to set up a LAS data set within one of our personal lab folders to save both the returns and any models we produce from then on. The LAS dataset is similar in function to a geodatabase for vector data. 

Once the LAS data files have been brought in we now have access to all of the statistics that come with it. These statistics are the main use in quality assurance and quality control for it allows you to investigate the min and max of the Z values if you investigate that the points are not close to actual earth then the data may be altered and need to be recollected. 

The second task we needed to complete before we could display the LAS data was to project all of the points in the correct projection. To determine the correct projection we needed to investigate the metadata closer. Once we investigated the metadata we found the correct coordinate system to be assigned to the data was NAVD 1988 US feet. 



Figure 2: shows the four main features that can
be applied during display of Lidar datasets

The four main displays we were showing in our lab included elevation which could be filtered based on different returns. (figure 2). There was also aspect, slope and contour. All of which are capable of applying filters to distinguish which return you want to display.

The last feature we explored in ArcMap was the ability to look at a selected location in a 2-d and 3-d view both of which can be utilized to distinguish features on a map easier. Including bridge structures and even get a better idea of what a building looks like or slope of a ridge. 

Part 3 

The last task of lab five was to generate four different models using our LAS data. We would produce a DSM, DTM hillshade, and an intensity model. Before we can produce these models we needed to determine the spatial resolution that the images would be produced at. This can be accomplished by finding the nominal pulse spacing this is obtained in the LAS statistics and finding the average of the point spacing. The average nominal spacing was found to be about 1.31. 

Now that we have all of our parameters set we now need to run the different tools to generate a DSM and DTM plus a hillshade model for both outputs. 

The steps for conducting this are very simple you just need to use the conversion tool known as LAS to raster, then by changing the internal functions such as value field cell type and void filling you can create wither a DTM or DSM. The difference between the two are within the cell type. For DSM you will select maximum for this will use the last return which in most cases will be the base of earth and the output will be the true surface outline of the area of interest. 

For DTM you will use minimum cell type and binning for the interpolation this will create an output with the first return pulse. The output is more defined and shows more distinct elevation change along with features more pronounced. (figure 3)

Figure 3: Output image generated from the Raster DTM model
the features are more pronounced but do not necessarily show the true earth structure for it utilized the first return.

 

Results 

The results from this lab allowed us to generate five total photos. The first four are both a DSM and DTM of the Eau Claire area mainly located around the University and downtown area. Both of these outputs also had a hill shade model applied to them to intensify the elevation changes. ( FIGURE 4,5)
The last image that was generated was an intensity image which is similar to a DSM but it will darken the contrast and shading of the black and white tones this application amplifies areas of elevation change allowing users to identify changes easier.


Figure 4: DTM hill shade model output.



In total this lab allowed us to enhance our skills of utilizing Lidar data sets. It also taught us the different application that Erdas can apply to Lidar data sets. We where able to identify the three different display methods including (elevation, contour, slope and aspect). We also where introduced to the different returns filters that can be applied these filters allow users to manipulate what return will show on the display option. You can pick from a variety of returns but the main ones are first return which mainly shows the tops of trees or buildings and last return mostly surface elevation.

Figure 5: DSM hill shade model output

In total this lab was very helpful to learning the potential of Lidar application and learning the tools needed for future projects. With this knowledge of lidar data sets I hope to continue to learn to be able to enhance my ability of remote sensing.

Sources:

Lidar point cloud and Tile Index are from Eau Claire county, 2013
Mastering ArcGIS 6th edition data by Margaret Price 2014