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

09-01
13:50
5min
Creating Large-Scale Very High Resolution Satellite Mosaics with High Spatial and Spectral Accuracy
Iosefa Percival, Kanoa

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.

Ran1
09-01
15:30
30min
landlensdb: A Python Package for Managing Proximity Sensing Imagery
Iosefa Percival, Narumasa Tsutsumida

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.

Conference Management Room1
09-02
16:00
30min
Mapping invasive canopy-smothering vines in the tropical Pacific using SAR and open-source geospatial tools
Iosefa Percival

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.

Dahlia2