Repository for Earth Analytics Fall 2023 Grassland Phenology final project, "Using remotely-sensed NDVI to understand temporal and spatial patterns of grassland brown-down in Boulder County, Colorado."
Advyth Ramachandran ([email protected])
PhD Student, Department of Ecology & Evolutionary Biology, University of Colorado, Boulder
This project was conducted as my Final Project for the Earth Analytics Bootcamp Fall 2023 course at the University of Colorado, Boulder. In this project, I aimed to analyze vegetation phenology (specifically, brown-down) from MODIS NDVI data and understand heterogeneity in phenology across grassland community types in Boulder County. Please see the notebook for a full project description.
This code was developed in the Earth Analytics Development Environment which can be installed using the instructions found at this GitHub repository: https://github.com/earthlab/earth-analytics-python-env (DOI: 10.5281/zenodo.1311239). The notebook file requires Jupyter Notebook to run.
There is only one notebook file for this project and cells must be run in order.
I thank Elsa Culler for extensive technical assistance and Katharine Suding for input on project planning.
Modified from the Final Assignment rubric to match the work proposed in this final project. Modifications indicated in italics.
| Description | Maximum Points |
|---|---|
| GITHUB REPOSITORY | 30 |
| Project is stored on GitHub | 3 |
| The repository has a README that introduces the project | 5 |
| The README also explains how to run the code | 5 |
| The README has a DOI badge at the top | 5 |
| The repository has a LICENSE | 2 |
| Repository is organized and there are not multiple versions of the same file in the repository | 5 |
| Repository files have machine and human-readable names | 5 |
| CODE | 120 |
| The code runs all the way through using the instructions from the README | 10 |
| The code follows the PEP-8 style standard | 10 |
| The code is well-documented with comments | 10 |
| The code uses functions and/or loops to be DRY and modular | 10 |
| Any functions have numpy-style docstrings | 10 |
| The code makes use of conditionals to cache data and/or computations, making efficient use of computing resources | 10 |
| The code contains a site map for the vegetation community type data | 10 |
| The code downloads the NDVI raster layer for each timestep | 10 |
| The code correctly calculates the brown-down 10th percentile thresholds for each year | 10 |
| The code harmonizes the polygon/vector and raster data | 10 |
| For each year and vegetation polygon, the brown-down date is calculated as a zonal mean of the brown-down date raster | 10 |
| The code produces at least one (sub)figure displaying the results | 10 |
| Any unfinished components have detailed pseudocode or a flow diagram explaining how they could be finished in the future, and or a complete bug report explaining the problem | up to 90 points, in place of other categories |
| WRITTEN ANALYSIS | 50 |
| The notebook contains a project description | 10 |
| The notebook contains a researched site description | 10 |
| The notebook contains a data description and citation for each data source | 10 |
| The notebook contains a description of how brown-down was calculated | 10 |
| The notebook contains a headline and description for each figure | 10 |