Tag Archives: ArcMap

Land Classification and Land Use

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After the completion of my Geo-referencing tasks (years 1995, 1975, and 1959), I was given the option between more Geo-referencing (1965) or a slightly different route, which consisted of creating a method to classify land types and land uses. If it wasn’t obvious by the title, I chose Geo-referencing 1965…

Land classification is the method of determining what a feature is on imagery purely based on pixel value (pixel value can be interpreted differently depending on the situation). This allows for a colorful rendition and separation, which results in an easy to read and visualize context of where different features are located. Results can vary and are heavily reliant on image quality. The lower quality the image or imagery, the more generalization and inaccuracy of the classifications.

Anyway, land classification can be simple and it can also be quite difficult. If you are using tools that already exist, or software that are built to classify imagery, you can easily begin land classification/land use. If you are using preexisting material it will quickly become a matter of finding the right combination of numbers in order to get the classifications you want. This method is not too difficult, just more tedious in regards to acquiring your result. However, if you approach it from scratch, it will be significantly more engaging. In order to approach it from the bottom up, you have to essentially dissect the process. You have to analyze your imagery, extract pixel values, group the pixel values, combine all of them into a single file, and finally symbolize them based on attribution or pixel value which was recorded earlier. It is much easier said than done.

I am currently approaching the task via already created tools, however if I had a choice in the matter, I would have approached it via the bottom up method and attempted to create it from scratch as there is more learning in that and it is much more appealing to me. Regardless, I am creating info files, or files that contain the numbers, ranges, and classifications I am using to determine good land classifications. In contrast to what I stated earlier, this is quite difficult for me as the imagery is low quality and I am not a fan of continuously typing in ranges until I thread the needle.

The current tool I am using is the reclassify tool that is available through the ESRI suite and it requires the Spatial Analyst extension. This tool allows for the input of a single image, ranges you would like to use to classify the selected image, and output file. After much testing, I am pretty sure there can only be a maximum of 24 classifications (which is probably more than enough). In addition, the tool can be batch ran (as most ESRI tools can be), which means it can be run on multiple images at once. This is a much needed features for many situations, as I presume most times, individuals are not going to classify one image and be done (or at least I am not going to be one and done).

That is an image that was reclassified using the reclassify tool. I am not sure how good of a classification this is as I have not fully grasped the tool yet and every time I give it ranges, it spits out the same generic ranges that I did not input (which is a bit frustrating, but it comes with the territory). I am sure it is human error though and not the tool messing up. I am not sure what the final result is supposed to be, but I will be sure to fill you in once I achieve it (if I ever do…).

Understanding Projections: Geo-referencing

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Recently I started working on the year 1959, which is currently the last set of images I have to work on.

1959 was a unique set compared to 1975 and 1995 for a few reasons. The first reason being related to the significant difference in development, which is obvious due to how far back in time the images were taken. The second reason is probably common among Geo-referencing projects, but I have not encountered it until this year. The set was split into two types of images: Geo-referenced or projected images and non-referenced or non-projected images. The projected images are similar to the images I have completed in 1975 and 1995, they were already projected to the desired coordinate system and allowed for immediate Geo-referencing. The non-referenced images are a little more difficult.

These images would gray out the toolbar and not allow for any Geo-referencing. At first I was a bit confused, but quickly started brainstorming ideas. My first idea came in the form of projecting them to the same coordinate system as the data frame. Unfortunately, due to the size of the images, projecting these images was impossible on the machine I had (16 GB of RAM). Nearly instantly the tools would fail. However, I thought this had to be the reason, so I continued to experiment with different ways to project the images. The next was to project to an intermediate coordinate system, then proceed to the more advanced system in hopes of reducing the burden, but that also fizzled out. It seems that no matter what system you are coming from (although I don’t fully understand geographic transformations), it attempts to perform the full process. Running out of ideas, I tried one final solution. I thought that perhaps the size of the images were the reason and decided to slim them down by splitting them into eight sections. After creating a few scripts that would split every image, which went smoothly, it approached the projection part of the script and crashed nearly instantly (although it may have lasted slightly longer).

 

After none of my ideas worked, I decided to restart the computer…and that worked. Even though the images are not the same coordinate system as the data frame, I can still add control points, fit to display, etc. This was a bit frustrating since it was such a simple solution, but the lesson here is that it is best to try the simplest solution before trying the more difficult ideas.

After talking with a few people, the coordinate system should be irrelevant because as soon as you begin Geo-referencing the image should convert to that of the data frame and even that will not matter in the end. Once the images are completed another individual will tether or mosaic them together and assign a coordinate system that way, which will result in the current coordinate system being overwritten.