Scientists and kiaʻi loko (fishpond practitioners) have a new tooL – drone lidar to aid their efforts to restore and ensure the resilience of Native Hawaiian fishponds. Researchers from the University of Hawai‘i (UH) and fishpond stewards in Hilo, Hawai‘i recently published a study in the Journal of Remote Sensing highlighting the use of uncrewed aerial vehicles (UAVs) to support integrated coastal zone management, including at cultural heritage sites.
From an article in Eureka Alert.
“We discovered that drones are effective and cost-efficient tools for mapping loko iʻa at the community level, providing kiaʻi loko iʻa with better insights into the timing and locations of flooding and future sea level rise impacts on their fishponds,” said Kainalu Steward, lead author of the study and Earth sciences doctoral student in the UH Mānoa School of Ocean and Earth Science and Technology (SOEST).
Loko iʻa, traditional Hawaiian fishponds located along the coastline, have historically provided sustainable seafood sources. These culturally important sites are undergoing revitalization through community-driven restoration efforts. However, as sea level rise poses a significant climate-induced threat to coastal areas, loko iʻa managers are seeking adaptive strategies to address related concerns such as flooding, water quality, and the viability of native fish species.
King Tides as estimate of future sea level
The researchers’ surveys determined that by 2060, the average sea level along the Keaukaha coastline in Hilo will be similar to the extreme tidal events, known as King Tides, during summer 2023. Steward and Brianna Ninomoto, a master’s student in tropical conservation biology and environmental science at UH Hilo, devised a plan to investigate how future sea level rise will affect loko iʻa by assessing the impacts of the summer 2023 King Tides.
Throughout the summer, including during the extreme high tide events, researchers collected drone imagery in real time and monitored water levels using sensors submerged at each loko iʻa. With this, they compared flooding predicted from drone-derived topography models and more commonly used Light Detection and Ranging (LiDAR)-derived models to the observed flooding documented by drone imagery.
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