2018 Graduate Student Showcase

Degree Program

Geophysics, MS

Major Advisor Name

Nancy Glenn

Type of Submission

Scholarly Poster


High spatial and temporal imagery are necessary to monitor phenological changes in semi-arid regions, but it is challenging to obtain this coverage using public satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) has a repeat interval of one to two days, but coarse spatial resolutions up to 1000 m. Landsat has a higher spatial resolution of 30 m, but a 16-day period. StarFM algorithm combines multi-resolution satellite systems to interpolate data with MODIS temporal and Landsat spatial scales. We use Google Earth Engine (GEE) to preprocess the data. This improved imagery can be leveraged to classify vegetation species with short phenological cycles.

Funding Information

Funding for this project was provided by the BLM under Cooperative Agreement L14AC00371.