- Week 1: history, resolution, TM
- Week 2: Day1, a closer look at landsat data, including how to get data off earthexplorer. Day 2: lecture on reflectance, data, and stretching. We also spent time looking at applications of remote sensing, particularly in Oz.
- Week 3: tues was lab, wed was sensors and the start of atmospheric corrections. thurs was in-class lab 4 (below)
- Week 4: Continued preprocessing (atmospheric corrections, sensor errors, rectification). Discussed what remote sensing is all about. Ratios.
- Week 5. Filters. Seriously consider watching the three spatial filters videos (first vid). Also.
- Week 6: Digital image processing. Principal Component Analysis. midterm exam.
- Week 7: unsupervised classification. Looked at Earth as Art book.
- Week 8: supervised classification and accuracy assessment.
- Week 9: LIDAR, guest presenter on tuesday: Lidar, fluvial geomorphology, and the Elwah dam removal.
- Week 10. Radar and Hyperspectral data. Presentations.
- tues, last 15 minutes - Kim and Cindy
- Wed, Zach and Bri, Gavin and Lewis, then Charles and Ryan
- Thurs, Tyler and Evan, Camille, Colin and Jame, then Chris and Brendan.
- Lab 1 - Because lab one falls on the first day of class, we'll go a bit light with a little review from airphoto. Work through the "Eye in the Sky" book (available in the lab) and answer question 1 from image. Turn in as a word document on Canvas. 3 points. Please don't write in the books - they are collector's items and can no longer be purchased (though I just found one on ebay out of Oz).
- Lab 2: The lab and the worksheet (turn in worksheet by friday on Canvas. 3 points.)
- Lab 3: the lab and the worksheet (turn in by friday on Canvas. 5 points)
- Lab4 - in class exercise looking at different sensors. Due at the end of class. 2 points.
- Lab 5: Georectification. Due the next monday on Canvas. 5 points.
- Lab 6: Filters and ratios. Due the next monday on Canvas. 6 points.
- lab 7: PCA and unsupervised classification. Due the next friday on Canvas. 8 points. Answer sheet!
- Lab 8: supervised classification. Part one. Due the next monday on Canvas. 6 points.
- Lab 8b: Accuracy assessment of your supervised classification. Due the monday of finals week on Canvas. 8 points.
- Lab 9 - LIDAR. Yup, frickin' planes with frickin' laser beams.
- Split into groups of two. The basic idea is for each group to research a different remote sensing topic. Your choice. Be very specific and clear them with me.
- Get out there and read a bunch of papers and make a 10 minute presentation to give in class (dates to be determined, but toward the end of the quarter).
- Your grade will be based on both the presentation, a list of references (handed in hardcopy when you present), and the relationship between the presentation and references.
- Presentations will be during the last week of class (individual times to be assigned the week before)
- Worth 5 points.
- OK. I'm winging this here. The idea is that there will be a few steps leading to the final lab. Without dumping the whole thing onto you at the last minute.
- The goal - to make two maps showing landuse/landcover change in an area. The trick - you must use two very different sorts of imagery. One must be Landsat 8; the other an aerial photograph. At least 30 years difference in time.
- Step 1 - think of an area that has changed over the past few decades or so. Get on this part Think at least 5 miles by 5 miles.
- Step 2 - get data! The landsat is easy - earthexplorer. You can also get airphotos from there, as well. However, there are many sources for airphotos (including earthexplorer - even google earth).
- Step 3 - get the data into ERDAS and georeferenced if necessary. After lab 5, you will know how to georeference photos in ERDAS. However, I don't care whether you do this in Arc or ERDAS.
- Step 4 - Figure out which landuse/landcover classes exist during both dates (note, there may be ones that are in one image that aren't in the other, if enough change has happened). Follow your book, page 611 - 618.
- Step 5 - digitize the classes from your aerial photographs. Use either arc or erdas. Don't care which! Remember, you will be comparing this with 30m data, so you won't need a TON of detail. Think minimum mapping units. I recommend looking at your classified image to see what you can see there. Then more or less mirror that when digitizing.
- Step 6 - do a supervised classification of your landsat image (we'll get to this later in the quarter).
- Step 7 - compare! And write it up! (more details coming). This will be due on the friday of the last week of classes. Alright. more details. Write up where things changed, how, and why. If you're familiar with Arc, you can do a good chunk of this quantitatively using the spatial analyst tools, zonal, tabulate area tool. Note, for this to work, you will have to convert your vectors to raster. And make sure the class numbers are the same on both rasters (ie. irrigated ag is class 1 in both images. This can easily be fixed when digitizing or using the reclass tool once you have a raster).
- Your final writeup should include. Good (as in readable, don't give me postage stamp images. Better to give multiples than too few) images of both the photo and image, your classifications (as images), and some text detailing changes (step 7) and what worked, didn't work, you would do differently next time, etc.
Some potentially useful links for you: