Forests in their later stages of development attain attributes that support biodiversity and provide a variety of ecological benefits (e.g. clean water and carbon storage). Despite their values, old-growth forests are declining worldwide in part due to anthropogenic pressures. A persistent challenge to managing and conserving old growth forest has been establishing a reliable method for measuring old-growth values across large landscapes at an appropriately fine ecological and spatial scale. Using data from a community-managed forest in central British Columbia, Canada, an Aerial Laser Scanning (ALS) based metric was developed, using a random forest modeling framework, to predict an old-growth index across the forest. Using this old-growth index, we estimated that forests with “Very-high” old-growth values cover 14.7% of the study area (18,183.2 ha), and that only 25% (4,545.9 ha) of this “very-high” old growth value areas are current inside designated old-growth management areas (OGMAs). Additionally, the forests with “very-high” old-growth values that are currently inside OGMAs are fragmented, as only 1 out of 40 OGMAs have more than 50% of its area covered by forests with “Very-high” old growth value. This research provides a clear ecological indicator that uses fine-scale remotely sensed data to measure old-growth and assess its conservation status within reserves. While the index developed is specific to the study site, the framework, is generic enough to be adapted to other forest types and ecosystems. More importantly, the identification of the amount and location of old-growth forests over the landscape can aid in the management and conservation of this rare resource and its services.
