We developed code to analyze spatial and temporal data from the the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite that we acquired from the AppEARS data portal. We focused on investigating forests because they are important for supporting New Zealand's unique biodiversity. In our code, we investigated ways to study deforestation and native forest density. With this work, we hope to find a reliable way of identifying at risk forests.
We chose this challenge because we are originally from New Zealand and care for its native species and biodiversity. To carry out this project, we analyzed maps derived from data from the MODIS satellite that we acquired from the AppEARS data portal. This analysis was built on Python, utilizing core features such as numpy, scipy and matplotlib. With these tools we developed code to interact with the MODIS data to observe and fit trends over time. Although forests are an excellent proxy for biodiversity, the lack of direct biodiversity measures is a limitation of this approach. With more time, we would have addressed this limitation by incorporating data from the Global Biodiversity Information Facility (GBIF) .
We used NASA MODIS data in our analysis.
https://docs.google.com/presentation/d/1hh99-pGbsG9gT8Ea1YR6Jpedb2ZIW2bZhxjwNWdefzI/edit?usp=sharing
Link to code and supporting figures: https://drive.google.com/drive/folders/1TWufSZLP87INiCUloSUa62cobaq984wh?usp=sharing
MODIS data aquired from the AppEARS data protal (https://lpdaacsvc.cr.usgs.gov/appeears/) and the World View data portal (https://worldview.earthdata.nasa.gov/)