Open Knowledge (FSC OK)

Reduced-impact logging (RIL) is a promising management strategy for biodiversity conservation and carbon sequestration, but incentive mechanisms are hindered by inadequate monitoring methods. We mapped 937 ha of logging infrastructure in a selectively harvested tropical forest to inform a scalable approach to measuring the impacts of discrete management practices (hauling, skidding, and felling). We used a lidar-derived disturbance model to map all skid trails and haul roads within 26 months of the selective harvest of six blocks of dipterocarp forest in five industrial concessions in East Kalimantan, Indonesia. Lidar maps of logging impacts (220 ha) agreed well with ground-based maps (total of 217 ha, RMS error of 6 ha or 3%), but skid trail positions agreed only 59% of the time. Due to rapid forest regeneration, total lidar-derived haul road area was 31% smaller than road area measured in the field; agreement was higher for lidar collections within a year of the harvest. Maps of carbon density generated from Fourier transforms of lidar height profiles estimated skidding and felling biomass losses to within 1–5% of ground-based measurements. Lidar-derived skidding and hauling impact zones covered only 69% of the permitted harvest area; the remaining areas showed no signs of logging disturbance, and available biophysical data did not explain their location. These results emphasize the need for more extensive mapping of logging infrastructure to capture spatial variability in skid trail density and hitherto undetected no-impact zones. While a ground-based GPS is recommended as the most affordable method for wide-scale infrastructure mapping, aerial lidar is an effective tool for remotely quantifying the extent of logging impacts in tropical forests.