Orangutans are increasingly venturing outside protected forests, in fragmented agricultural landscapes dominated by oil palm plantations. Monitoring these populations presents significant logistical and methodological challenges.

Conventional great ape surveys rely on counting their nests along line transects — an approach that is labour-intensive, limited in spatial coverage, and subject to considerable uncertainty stemming from highly variable nest decay rates. These limitations constrain the frequency, scale, and precision with which population trends can be assessed.

This project aims to develop a novel framework for surveying, monitoring, and managing wild orangutans across both forest and mosaic landscapes. Using thermal-equipped drones deployed at night, we detect individuals directly in their sleeping nests, bypassing the indirect inference required by nest-based methods. We are currently quantifying detection probability under this technique in order to build a robust density estimation model applicable at landscape scale.