Week5: Carbon Sequestration and Tree Cover

This week we had to calculate tree cover from our previous week's raster creation. Last week we had to generalize a raster that was classified into 50 classes (or 25 if ArcMap kept crashing), and generalize it to 3 classes: trees, grass and impervious surfaces. (So that's how it's done!) This week we calculated the area of the tree cover as a percent of the whole, and also did some carbon sequestration calculations. The three resulting maps (shown here) are quite similar. The 2nd and 3rd display, carbon storage and carbon sequestration, are really the same thing multiplied by a different constant.

This week I learned more (than I wanted to know, really) about the Field Calculator, but it was useful. I also discovered how to join the five neighbhorhoods that we focused on, using the Append function. That was useful too.

Last week we had the task of joining 7 smaller rasters into one big file. This didn't go at all well for me until Mari suggested using mosaicking, a technique I had never used before but that was very useful and worked like a charm.

All in all things are going pretty well. Except for the eyestrain this week...

Week 3: Mapping Proximity to Target Population and Asthma Triggers

For the final week of this asthma-related project we had to do a proximity analysis to show which Alameda county hospitals were a) close to census tracts with a high proportion of black residents, the target population (I used more-than-50%-black; most of the census tracts were near South Berkeley and in Oakland), b) close to major roads (buffered by 0.1 mile), and c) close to Toxic Release Inventory (TRI) facilities (buffered by 0.5 miles). All went fairly well until I got to the point of doing Euclidean Distance for each of the inputs (hospitals, high-black-pop, TRI and roads). First I made at least 20 tries to endeavor to figure out which of the "environments" and "analysis" settings for the Euclidean Distance function did what. Eventually I discovered the distance between a mask, which could be the shape of a shapefile if needed, and the raster analysis extent, which is a rectangle. (Perhaps I was told this earlier - but if so, I had forgotten.) I also discovered that if you set the analysis extent to "extent of display", and adjust the display, the distance radii become longer or shorter. It was an instructive, if frustrating, process. I was pretty excited when I finally got it straight, but it was a long road to enlightenment.

Perhaps I shouldn't have shown this map, since it makes glaringly obvious the difference between the outline of the Alameda County boundary shapefile (the western part, where I did the analysis, is in color) and the outline of the Alameda County census tract shapefile (that grey thing sticking out at the bottom is a high-black-percent census tract that extends beyond the Alameda County boundary shapefile that I intersected with the analysis extent). However it does show the areas where the hospitals are close to a combination of high black population, major roads, and TRI facilities (closest hospitals are in the lightest yellow section). I made the black population file the most important (worth 60%) when I did the weighted overlay of the four files (hospitals (10%), TRI (15%), roads (15%) and black population) because the most important thing is for the hospitals to be close to where the target population is.

Fall Week 1: Preparing Data for GIS & Public Health

In this first 3-week section, we will be producing maps of Bay Area demographics as well as asthma hospitalization rates and some pollution indices. As part of the preparation, we had to compile a metadata chart containing the layers we would need to use. I can't for the life of me figure out how to link it to this, so instead here (to the left) is a screen shot of the layers I plan to use; below is a link to the same thing, only bigger.

http://students.uwf.edu/db27/BayArea/Week1ScrShot.jpg

I haven't included anything in this screenshot other than the outlines of the Bay Area counties. I'm still having some problems, with a) having a correct, or complete, roads file - although the roads aren't crucial for the public health part, I think, so far; and b) with the correct projection for the air sampling stations, which so far has eluded me. Maybe by next week...

Week 5: Using LiDAR

This week we learned how to import a .txt file of raw LiDAR point data into ArcMap, convert the data into a raster image in ArcMap and transfer that image back into ERDAS. This wasn't so hard; the difficult parts for me in the challenge part of the lab were

a) figuring out the scale (until I read the .xml metadata file and realized what the projection was I kept getting impossibly small figures for the scale),

b) wondering where on earth I was - when I was still confused about the scale I did discover - by checking the coordinates into Google Earth - that the location must be the Florida coast again and not in the middle of Iran as I thought for a perplexed moment! - and

c) 'reading' the raster image to figure out which was road and which was sand dune. The road, I concluded, was the part at the top right (it shows up in red in this image) with fairly uniform elevation and a longish straight side, but it took a while before my different classifications showed that. I think I got it eventually - it was initially hard to see. With the manual classification I did (I chose not to use a "stretched" continuum of color) I found it easier to find the road. I ended up creating a rather luridly colored image but water shows up clearly, here as blue (because the elevation is near zero) and sand dunes are yellow and orange. The whole process did reinforce for me what a rube I am when it comes to remote sensing. If only I'd taken a full semester of it when I had the opportunity!

Week 4: Supervised Classification

Here is a link to the map that resulted from this week's lab: http://students.uwf.edu/db27/RemoteSensg/Week4.xps
I'm finding RS really interesting even though the software is dismayingly unfriendly to the user (but powerful). This week we classified land cover types from a satellite image, and then recoded some of the land cover types (merging classes to make fewer classes). I had trouble with recoding in the challenge lab although it seemed to go OK in the main lab. Still have questions about the mechanics of this - remote labs are OK but the discussion board for questions works only so far. I'm finding ERDAS to be a sort of Frankensteiny monster - powerful but lurching from side to side unpredictably (not to mention crashing without warning) and so far it's awfullly hard to get it to do what I want. More practice would help. I wish I'd taken remote sensing when I did my geography degree and was actually present in person!

Week 3: Orthorectification

This week we placed control points on images and tried to orthorectify the images by making reference to a topo map (or, in the first part of the lab, to specific points on an image). This was an interesting experience that I was able to repeat many times because of the program crashing repeatedly. I understand the general idea but I had a lot of problems with the software, which makes ArcGIS look like a walk in the park.
The final image is the orthorectified one, with a UTM map grid superimposed. I am not sure whether it is clear that the final image has been orthorectified. I think the software does a lot of useful things, but it currently has some built-in frustrations.
A link to my orthorectified image is at http://students.uwf.edu/db27/RemoteSensg/Week3PensaRectify.xps

Week2: Discerning Differences Using Spectral Bands

This week we looked at an image taken near Puget Sound, Washington and examined the image information, including the pixel histogram and the pixel data, for specific places in the image where pixel values in certain bands were particularly low or high. I still find ERDAS Imagine a frustrating and non-intuitive form of software, but in looking at the image using different wavelength bands I was captivated by the scope for interpretation and visualization. This made it a really interesting lab (when I was not cursing ERDAS). I still have no idea where Map Composer is or whether it (or its logo) even exists within the software, but I did eventually find all the pieces of things I needed to finish the lab.
Links to my images:
http://students.uwf.edu/db27/RemoteSensg/Week2Layout1.xps
http://students.uwf.edu/db27/RemoteSensg/Week2Layout2.xpshttp://students.uwf.edu/db27/RemoteSensg/Week2Layout3.xps