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| Created: | Apr 05, 2023 at 6:30 p.m. | |
| Last updated: | Apr 14, 2023 at 8:10 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
This project aims to use remote sensing data from the Landsata database from Google Earth Engine to evaluate the spatial extent changes in the Bear Lake located between the US states of Utah and Idaho. This work is part of a term project submitted to Dr Alfonso Torres-Rua as a requirment to pass the Remote Sensing of Land Surfaces class (CEE6003). More information about the course is provided below. This project uses the geemap Python package (https://github.com/giswqs/geemap) for dealing with the google earth engine datasets. The content of this notebook can be used to:
learn how to retrive the Landsat 8 remote sensed data. The same functions and methodology can also be used to get the data of other Landsat satallites and other satallites such as Sentinel-2, Sentinel-3 and many others. However, slight changes might be required when dealing with other satallites then Landsat.
Learn how to create time lapse images that visulaize changes in some parameters over time.
Learn how to use supervised classification to track the changes in the spatial extent of water bodies such as Bear Lake that is located between the US states of Utah and Idaho.
Learn how to use different functions and tools that are part of the geemap Python package. More information about the geemap Pyhton package can be found at https://github.com/giswqs/geemap and https://github.com/diviningwater/RS_of_Land_Surfaces_laboratory
Course information:
Name: Remote Sensing of Land Surfaces class (CEE6003)
Instructor: Alfonso Torres-Rua (alfonso.torres@usu.edu)
School: Utah State University
Semester: Spring semester 2023
Subject Keywords
Coverage
Spatial
Temporal
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Content
readme.txt
############################## ### Motasem S Abualqumboz ### Utah State University ### Motasem.abualqumboz@usu.edu ############################# Instructions for using the data: ------------------------------ You may choose any file and then click on the download button, or simply double-click on the file to download it. If decided to run the Jupyter notebook in a different computational environment (outside the resource), make sure to have the all the Python packages needed to run the notebook. To run the ArticleCode.ipynb Jupyter notebook from the same resource, you may do the following: 1. Click the "open with" located in the top right corner of this resource. 2. Chose the CUAHSI JupyterHub 3. Choose R – v3.6.1, or any later version of R 4. You will be taken to another window (JupyterHub.cuahsi.org) where the all the files in this resource will be coppied. The table of contect will be to the left. You got a copy of the resource for your use. 5. You may explore the content of the CSV files by double click on them. 6. Double click on the notebook where the Python code is written. You may run the code section by section by choosing the section and clicking on the run button (the play symbol). The sections have to be run in order (Always start from the top). Alternatively, you may click on the "Kernel ---> "Restart Kernel and Run All Cells" or (Run --> Run All Cells) to run the whole notebook.
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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