4.1: Image Interpretation
Satellite and aerial imagery interpretation is a critical tool used by geographers and all spatial scientists. And with modern technology such as Google Earth, it has never been easier for society to study the world they live in from space. NASA’s Earth Observatory recently came out with an article that discusses How to Interpret a Satellite Image. For most of the modules in this course, you will be required to analyze satellite or aerial images related to the current module and theme we are studying.
For this assignment, study the aerial image and give a detailed response to the following criteria.
- Describe in detail the patterns, shapes, and textures you observe on the image.
- Interpret and explain the colors (including shadows) you notice about the image and how it helps you critically interpret and understand the image.
- Once you understand which direction is north, discuss how this helps you interpret the lay of the land.
- What was your prior knowledge of the area before analyzing this image?
- What did you find most interesting about this image? Explain why.
The Great Pyramid at Giza, Nazlet El-Semman, Al Haram, Giza, Egypt
When I first look at this imagery, I the Great Pyramid stands out as the largest building amidst many other smaller, rectangular-shaped buildings or tombs, perhaps. Aside from the main pyramid, most of the other structures look like they have endured some weathering—they appear to be a bit crumbly. The lay of the land is only marked with a couple of man-made roads, but one can also see paths of darker sediment where water may have flowed through at some point. At first glance, most of the coloring of this image is very light tan and looks to be mostly desert or sand. Also, it appears to be very sunny so much so that the sunlight is making the lightly colored earth look even closer to white. I cannot actually see any shadows other than on what I interpret to be one on the west wall of the Great Pyramid. To make sure that I have the direction of north correct, I zoomed out to get a better grasp of the area of Egypt, I was looking at and as I did so, it was really interesting to see the large area of desert in the middle of more developed metro area (Cairo) where there was a significant amount of greenery. Using the measuring tool, I discovered that the Great Pyramid is only a little over 5 miles west of the Nile River. To make sure that I have the direction of north correct, I zoomed out to get a better grasp of the area of Egypt, I was looking at and as I did so, it was really interesting to see the large area of desert in the middle of more developed metro area (Cairo) where there was a significant amount of greenery. Using the measuring tool, I discovered that the Great Pyramid is only a little over 5 miles west of the Nile River. I am curious to look at the greater area of Cairo now, and find a documentary on their ancient canal system—assuming they have one. I have looked at this area before in my atlas, and I have watched all of the Indiana Jones movies, but looking at this part of Egypt on satellite imagery shows me the actual scale of objects, such as the Great Sphinx.
Dadaab Refugee Camp, Kenya
First and foremost, I have never heard of this particular refugee camp and to see the shear size of it on satellite imagery is sincerely profound. (How have I never looked at this before?!) I recall from an earlier module of a similar “shanty town” where people were creating maps of the “streets” and labeling them for a variety of reasons, one of which was so that women would know where not to go alone. The patterns and shapes that stand out in this image are of all of the shelters which one cannot really tell what they are made up of from this height. Even when I zoom in and out, I can only say that they are shelters of some sort—-maybe tents of fabric, but probably of whatever material the people can literally get their hands on. I also pick out the patterns of walk paths or roads throughout the camp. They seem to be man-made, not perfectly straight or square, but definitely man-made. Also, I noticed some cloud cover, but the more I look at it, it seems like it might be smoky haze which would make sense because the refugee camp probably uses fire to cook and keep warm at night since it appears to be a fairly arid zone with some areas of shrubs and trees. What I find most interesting in looking at this image is the fact that it came into existence from nothing and it is astounding that over 350,000 people are crowded into an area that seemed to roughly be around 4 square miles (at the most).
Lower Ninth Ward New Orleans, Louisiana
Immediately upon viewing the satellite imagery of the Lower Ninth Ward of New Orleans, I am struck with the pattern of complete organization from this height. You can very easily identify the streets, home lots, neighborhood greenery and then of course the bodies of water that surround three sides of this particular ward, but as I zoom in I can now see the shapes of homes that are no longer on the organized plots of land. In fact, there are entire blocks of land without homes presently, but you can see the textured indention of where the foundation once was. Also, while zoomed in, you can see that the organized streets all look like they need to be repaved, and it looks like this imagery was perhaps taken between 12:30 and 2:00 p.m. judging by the shadows of the trees and standing houses. I can see the green grass and trees as well as patches of brown dirt and I can even see what appears to be some rows of food being grown in backyard gardens. The bodies of water that surround this ward appear to be filled with dirt sediment—-the Mississippi River looks dark brown and the canals looks greenish-brown. Because the ward is located south of a large canal and east of a smaller canal as well as north-north east of the Mississippi River, it is easy to see how this particular area was the hardest hit when Hurricane Katrina drove the waters inland. Prior to looking at this satellite imagery, I have to say that even though I was not living in New Orleans at the time of Hurricane Katrina, it greatly affected my life and what I held dear at the time and about six years ago I was able to drive through the area and even walk around, but the heavy feeling of what happened there still hung in the clamminess of the air.
Mount Saint Helens National Volcanic Monument, Washington
In this satellite imagery, it’s hard not to immediately notice the mix of snow, ice and rocky texture. It’s a fairly surreal image because it’s challenging to draw distinctions in elevation even though you can see a bowl shape in the center. The north side appears to be steeper than the rest of the crater shape as the snow/ice is more solid in that area whereas on the southern rim, you can pick out some rock exposure. What I find really interesting are the different shades of white to gray on the mountain from the top down. I wonder if the color differentiation is because there is more ice on top as opposed to as you move down the slope of the crater, and the ice and snow begin to mix with rock debris all the way down. You can also see that what parts of the mountain are not covered in snow and ice are covered in dark green showing dense forest. It is hard to pick out whether or not there is any cloud cover in this image—they might be a little bit, but not enough to block out the view. It’s really interesting how the snow moves down parts of the mountain in very distinct lines—maybe those are very steep canyons or frozen rivers. I was two years old when Mt. St. Helens exploded, but I do not remember seeing any images at the time. I do, however, recall watching news footage of it when I was perhaps 10 years old or so, and then when I was in my mid-20s, I was able to see the 25th anniversary IMAX film of its eruption which was awesome!
4.2: Managing LiDAR Data Using LAS Datasets
The LAS dataset provides fast access to large volumes of LiDAR and surface data without the need to first convert or import the data. This course teaches how to create a LAS dataset to efficiently organize LiDAR data delivered in individual LAS files. You will learn how to quickly access key information about the LiDAR data and techniques to display LiDAR points in ArcMap. This course assumes that raw LiDAR data has been acquired using airborne systems with the laser scanner pointing downward. By completing this module, students will be able to:
- Create a LAS dataset to simplify access to multiple LAS files.
- Calculate statistics for a LAS dataset to view and understand characteristics of the LiDAR data it references.
- Create a LiDAR intensity image and use it to check for data voids.
- Manually reclassify LiDAR points to correct erroneous classification codes.
- Visualize LiDAR points in 2D and 3D and interactively measure distances and elevation differences.
4.3: Managing LiDAR Using Mosaic Datasets
Mosaic datasets are an ideal option when you want to perform raster-based analysis on LiDAR data or provide end users with fast visualization and dynamic on-the-fly processing capabilities. This course teaches how to create a mosaic dataset to organize LiDAR data, apply techniques to optimize and control data display, and use functions that enhance visualization and generate new information from LiDAR data. This course assumes that raw LiDAR data has been acquired using airborne systems with the laser scanner pointing downward. By completing this module, students will be able to:
- Add LiDAR data and associated datasets to a new mosaic dataset.
- Optimize display speed, control display properties, support user queries, and enable data downloads.
- Create profile graphs and surface representations to analyze elevation and slope in an area of interest.
4.4: Managing LiDAR Data Using Terrain Datasets
LAS datasets are useful for performing quality assurance on lidar data. Once you’ve determined quality standards have been met, creating a terrain dataset to store and organize the lidar data is recommended. Terrain datasets support fast data retrieval and display and allow you to easily generate derivative products, including digital elevation models (DEMs). This course teaches how to create a terrain dataset, then use it to visualize and analyze lidar data. This course assumes that raw lidar data has been acquired using airborne systems with the laser scanner pointing downward. By completing this module, students will be able to:
- Create a terrain dataset and specify properties to meet your project requirements.
- Set display properties and choose appropriate renderers for your 2D and 3D visualization needs.
- Perform steepest path and visibility (line of sight) analyses using a terrain dataset.
- Generate a DEM from a terrain dataset, then derive a slope surface from the DEM.