Iosefa Percival
Iosefa Percival is a postdoctoral researcher at the University of Hawaiʻi at Hilo. He develops remote sensing methods for mapping forest structure, carbon, and invasive species using lidar and satellite data. He also develops and maintains open-source geospatial software.
Sessions
Very High Resolution satellite imagery (eg WorldView3) provides insights into Earth surface processes, but suffers from limited spatial/spectral accuracy. While tools exist to correct these errors, there is currently no robust pipeline. Vhrharmonize is a Python library, command-line interface, and QGIS plugin that automates preprocessing and mosaic generation.
We introduce landlensdb, an open-source Python package for managing proximity sensing imagery, including action cameras, 360° cameras, and UAVs, using PostgreSQL/PostGIS. It automates metadata extraction, corrects geolocation errors via road network snapping, and enables scalable spatial-temporal queries and visualization for large-scale geotagged image datasets.
Invasive canopy-smothering vines such as Merremia peltata threaten tropical forests but are difficult to map under persistent cloud cover. This talk presents a SAR-based workflow using Sentinel-1 and open-source geospatial tools to detect and map infestation across Pacific islands, supporting reproducible, transferable monitoring for management.