Submitted by Shannon Bayliss on
Tropical forests are “green year-round” – a characteristic that makes it difficult to distinguish phenological patterns (seasonally-related cyclical stages of life cycles) of different forest species. To answer research questions about plant phenology, ecosystem function (for example, carbon cycling), and species’ vulnerabilities to climate change, we have collected remotely-sensed UAV (unmanned aerial vehicles, drones) geospatial imagery of two contrasting forest types on the Island of Hawai’i that differ in their environmental conditions and species’ compositions. Data from this project allow us to ask questions about how different species within the forests are responding to environmental change over time, and the effects these changes may have on large-scale ecosystem processes.
Student will use GIS software to map and identify species’ tree crowns in a tropical forest from LiDAR and visual data.
- Interest and familiarity with ecology, forestry, geography, environmental sciences, or related fields.
- Proficiency in Geographic Information Systems (GIS) software, such as ArcGIS or QGIS, and understanding of spatial data: should be able to use these tools to map tree crowns in a forest, ability to manage spatial data and perform geoprocessing tasks.
- Attention to detail: should demonstrate meticulous approach to data processing to ensure the accuracy and reliability of data.
- Problem-solving skills: should demonstrate ability to troubleshoot and devise creative solutions to challenges that may arise during data processing.
- Communication skills: should demonstrate ability to effectively communicate with team members and to clearly convey and document project progress.
- (optional) Interest in learning or applying Machine Learning analyses