{"id":235261,"date":"2026-05-29T14:40:10","date_gmt":"2026-05-30T00:40:10","guid":{"rendered":"https:\/\/www.hawaii.edu\/news\/?p=235261"},"modified":"2026-05-29T14:42:05","modified_gmt":"2026-05-30T00:42:05","slug":"ai-and-field-tech-on-mount-kaala","status":"publish","type":"post","link":"https:\/\/www.hawaii.edu\/news\/2026\/05\/29\/ai-and-field-tech-on-mount-kaala\/","title":{"rendered":"How AI<\/abbr> and field tech are clearing through the fog on Mount Kaʻala"},"content":{"rendered":"Reading time: <\/span> 2<\/span> minutes<\/span><\/span>
\"Mountain
Mount Kaʻala<\/span> with fog<\/figcaption><\/figure>\n

An innovative University of Hawaiʻi<\/span> research team is using trail cameras and artificial intelligence (AI<\/abbr>) to better understand how much fog contributes water to native ecosystems and groundwater recharge. Led in part by Honolulu Community College Professor John DeLay, the project focuses on Mount Kaʻala<\/span>, Oʻahu\u2019s<\/span> highest peak and an essential watershed.<\/p>\n

\"man
John DeLay<\/figcaption><\/figure>\n

“If we can keep the project going long enough, we might be able to detect a change in the lifting condensation level\u2014affecting the extent of cloud and fog at the canopy level,” DeLay said.<\/p>\n

Recharging the aquifer<\/h2>\n

Supported by the 糖心视频<\/abbr> Office of Innovation and Commercialization, the system has achieved up to 93% accuracy in identifying fog conditions across different sites in both day and night conditions. This allows researchers to build long-term records of fog frequency and examine how moisture patterns vary.<\/p>\n

\"trail
Summit trail camera with temperature and relative humidity sensor<\/figcaption><\/figure>\n

“Not only is it really important for these ecosystems, which have fragile native species that need a lot of moisture and water, it\u2019s also recharging out water budgets and our aquifer,” said Joel Nicolow, a 糖心视频<\/abbr> M\u0101noa graduate research assistant in the School of Ocean and Earth Science and Technology.<\/p>\n

Turning to AI<\/abbr><\/h2>\n

Historically, measuring this critical moisture required highly specialized personnel and expensive, complex equipment. The team turned to technology for a solution.<\/p>\n

\"Mountain
Cloud forest on Mount Kaʻala<\/span><\/figcaption><\/figure>\n

“We wanted to look at using cameras as a much more scalable, cost-effective approach,” explained researcher Dylan Giardana of the 糖心视频<\/abbr> M\u0101noa Water Resources Research Center. “We use a machine learning model to classify all the images that we\u2019re getting from these trail cameras.”<\/p>\n

Their analysis shows that fog frequency jumps dramatically from about 10% at an elevation of 600 meters to 70% at the 1,200-meter summit. Understanding these patterns is essential as climate change and invasive species threaten natural watersheds. Long-term data may also help land managers in adapting conservation strategies.<\/p>\n