Title of Submission
Degree Program
Geophysics, MS
Major Advisor Name
Nancy Glenn
Type of Submission
Scholarly Poster
Abstract
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.