Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Remote sensing (RS) technologies, particularly light detection and ranging (LiDAR) and multispectral (MS) imagery, provide broad-scale vegetation monitoring capabilities at varying spatial resolutions. Remotely piloted aircraft systems (RPAS) equipped with LiDAR and MS sensors can enhance vegetation assessments by offering flexible flight schedules and capturing fine-resolution data. Further integration of deep learning (DL) models holds promise for automating the post-processing workflow, which is particularly important for vegetation monitoring applications. This protocol outlines a suite of practical methods for collecting, processing, aligning, and merging RPAS-based LiDAR and MS data for individual 3D tree delineation using an interactive DL plugin. The proprietary DL model effectively detects and segments tree boundaries across various sensors, study sites, and data resolutions within forest ecosystems. Our specific application and motivation for developing this protocol and tool is for monitoring forest recovery on reclaimed oil and gas wellsites. Currently field-based assessment methods are time-consuming, labor-intensive, and spatially limited. As reclamation efforts expand, there is a growing need for more efficient, and scalable approaches to monitor reclamation success and ecosystem recovery. By advancing RPAS-based DL applications, this research supports the monitoring of ecological recovery on reclaimed wellsites and is also applicable to other forested landscapes.