Tracking the Creation of Tropical Forest Canopy Gaps with UAV Computer Vision Remote Sensing

Abstract:

The formation of canopy gaps is fundamental for shaping forest structure and is an important component of ecosystem function. Recent time-series of airborne LIDAR have shown great promise for improving understanding of the spatial distribution and size of forest gaps. However, such work typically looks at gap formation across multiple years and important intra-annual variation in gap dynamics remains unknown. Here we present findings on the intra-annual dynamics of canopy gap formation within the 50 ha forest dynamics plot of Barro Colorado Island (BCI), Panama based on unmanned aerial vehicle (UAV) remote sensing. High-resolution imagery (7 cm GSD) over the 50 ha plot was obtained regularly (≈ every 10 days) beginning October 2014 using a UAV equipped with a point and shoot camera. Imagery was processed into three-dimensional (3D) digital surface models (DSMs) using automated computer vision structure from motion / photogrammetric methods. New gaps that formed between each UAV flight were identified by subtracting DSMs between each interval and identifying areas of large deviation. A total of 48 new gaps were detected from 2014-10-02 to 2015-07-23, with sizes ranging from less than 20 m² to greater than 350 m². The creation of new gaps was also evaluated across wet and dry seasons with 4.5 new gaps detected per month in the dry season (Jan. – May) and 5.2 per month outside the dry season (Oct. – Jan. & May – July). The incidence of gap formation was positively correlated with ground-surveyed liana stem density (R² = 0.77, p < 0.001) at the 1 hectare scale. Further research will consider the role of climate in predicting gap formation frequency as well as site history and other edaphic factors. Future satellite missions capable of observing vegetation structure at greater extents and frequencies than airborne observations will be greatly enhanced by the high spatial and temporal resolution bridging scale made possible by UAV remote sensing.