Module 1: Visual Interpretation

 Module 1: Visual Interpretation

In this weeks lab we learned how to understand what remote sensing does to help us understand our landscape. Also, remote sensing can be passive (i.e. photos) or active (i.e. LiDAR). This is important to know because it helps us know how the data is being represented and how we can read the data. Lastly, we learned that the data can be automatic but it is up to the GIS analyst to understand the data and show how it is represented on a map.

In the lab module we had three objectives we needed to learn. In the first objective we needed to show the tone and texture of a USGS picture. We needed to make the polygons a feature class in the geodatabase. Since the map is not projected the features were not automatically projected. You could choose the "current map" and it is what is currently on the map. The tone of the map is trying to represent how dark or light different portions of the map are to the GIS analyst. The texture of the map is how close or smooth of different areas on the map. The image below shows how I as the GIS analyst saw the different portions of the USGS image.

In the second objective was to take another USGS image to understand how to look at an image and see how one could understand the image by Shape/Size, Shadow, Pattern and Association. First the shape and size features I choose was a building, house, and road. I choose each of these because it was very obvious of the size and shape of the feature on the image. Second the shadow features I choose was a water tower, light poles, and tree. I choose the water tower because it was the easiest to see and the shadow. The light pole was a little harder to see but when looking at the shadow you knew it was the light pole. Lastly, choose the tree because it was obvious to know what it was by the shadow. Third the pattern feature I choose was a parking lot, beach, and neighborhood. The parking lot was the easiest pattern to see because of the parking lines on the asphalt. The beach was a pattern of sand all together creates the beach. The final pattern was a group of houses would create a neighborhood. I thought a pattern could also be multiple houses and not just lines indicating a parking lot. Lastly, the association features I choose was a  hotel and pier. I could tell it was a hotel by the association of a parking lot, multiple balconies, and a pool. All of those identifying features helped me know it was a hotel. The second one was a Pier and that was the location of the feature, being in the water, and the wood planks on the pier. Looking at those identifying features I was able to know it was a pier for Pensacola Beach. The image below shows how I understood the image based off the different features.

The final objective was looking at two different images to see the true color and see how our eyes see different colors. The second part was looking at a False color infrared image to see how those the different colors change. Knowing these changes is important because it shows how remote sensing represents colors from our eyes to an image. An example of this is when you look at the trees in the in the true color image it is green, but when you look at the false color infrared image it shows up as red. The reason for this is because green is our "visible" color and red is the color what the trees give off to the remote sensing tool.