Machine learning algorithms have shown great promise in identifying patterns and making predictions about ecological observations in the field, but they rely on large, high-quality datasets that can be difficult for researchers to integrate into their work. Furthermore, collecting and cleaning such datasets is a mundane and time-consuming task that can take up significant time and resources. In this talk, we’ll present a functional, public, and free web application to process, visualize, and intersect ecological data with online remotely sensed data that contain a wealth of information about environmental factors that can affect ecological processes and animal behavior. In this context of this session, we will focus on use for understanding and predicting distributions and dynamics of agricultural pests, but this tool could support any ecological study with ground-based measurements that could benefit from leveraging satellite information. We encourage participants to bring datasets they would like analyzed in the form of CSV files that include latitude and longitude columns. The web application will generate an annotated table of freely available, but difficult to access, public satellite datasets including a variety of phenological and climate variables. The goal of this presentation is to educate participants about the existence of this application and recruit new users to guide future development including guidance toward usability, integration toward public datasets or models, and methodology to share and collaborate on ongoing research.
