5: Multispectral data with Python

Due before 11:59pm on May 11

10 points

1. Create a GitHub repository called gisc606-lab5 in your GitHub account using github/codespaces-jupyter as a template repository

2. Open your repository in a GitHub Codespace

3. Create a Jupyter Notebook for and complete each of the following lessons from Earth Lab CU Boulder's Intermediate Earth Data Science textbook

   1. Introduction to Multispectral Remote Sensing Data in Python: intro_multispectral.ipynb

   2. How multispectral imagery is drawn on computers: rendering.ipynb

   3. Learn to Use NAIP Multiband Remote Sensing Images in Python: naip.ipynb

   4. Get NAIP Remote Sensing Data From the Earth Explorer Website: get_naip.ipynb

   5. Work with Landsat Remote Sensing Data in Python: landsat.ipynb

   6. Open and Crop Landsat Remote Sensing Data in Open Source Python: open_landsat.ipynb

   7. Clean Remote Sensing Data in Python - Clouds, Shadows & Cloud Masks: cleaning.ipynb

   8. How to Replace Raster Cell Values with Values from A Different Raster Data Set: replace.ipynb

   9. Find and Download Landsat 8 Remote Sensing Data: download_landsat.ipynb

   10. Practice Opening and Plotting Landsat Data in Python Using Rasterio: practice.ipynb

4. Commit the changes to your repository

5. Once you are complete with all the lesson notebooks, navigate to Lab 5 assignment in Canvas

6. Input the url of your gisc606-lab5 GitHub repository then click submit

7. Optional: Lab 5 extra credit opportunity in Canvas (worth 5 additional points)