## 1 Introduction
Technology advances also make it possible for UASs to obtain an accurate survey of ground control tie-in points (GCPs) even under a dense forest canopy. Surveying GCPs is historically a time and labor-intensive task but necessary for obtaining useful inventory measurements. Real-time kinematics (RTK) technology, which relays positional data to UASs during flight via a fixed dual-frequency ground station, also has precision deficiencies in dense forest environments [22]. This limitation has been overcome with post-processing kinematic (PPK) technology, which uses an on-board GPS sensor to simultaneously trigger the camera and capture the bounding coordinates of each image at sub-decimeter positional accuracies [23]. The combination of precision and rapid deployment capabilities now provides the means to produce multi-image datasets with pixel resolutions of a few centimeters. To analyze the land cover change surrounding planned disturbances, UAS imagery has been used with the 30+year-old RS technique [24] of geographic object-based image analysis (GEOBIA) to classify the phenological conditions of discrete forests for damage and regeneration assessments following disturbances. Typically, land inventory studies that have used GEOBIA employed object-based classification (OBC) methods as opposed to pixel-based classification (PBC) methods since the image objects are comprised of multiple pixels [24]. Examples of timber filling events inventoried with UAS-derived OBC were documented by [PERSON] [25] and [PERSON] et al. [26]; each study examined previously cut sites that were under restoration. [PERSON] [25] was able to inventory four peatbog species (Waterlogged Bare Peat, Tussoks, Peat Moss, and European White Birch) with a 91% overall accuracy (OA) and individual class accuracies of 84-95%. [PERSON] et al. [26] inventoried several conifer species, such as the Douglas Fir, Western Larch, Western White Pine, Lodgepole Pine, Engelmann Spruce, and White Spruce at 5, 10, and 15 years since planting. Three land covers (LCs) were classified to assess seral succession performance conifer, deciduous, and bare ground, where the analysis achieved an OA of 86-95% between age groups. For fire events, UAS-derived OBC has also been studied extensively. [PERSON] (2017) employed OBC for the post-fire inventory of two successional pine species (black and scots pine) within an oak-dominant Mediterranean forest. [PERSON] et al. [PERSON] et al. (2019) leveraged OBC for inventory jack pine succession following a wildfire in northern Michigan, with OAs of 98%. Both [PERSON] et al. [PERSON] et al. (2018) and [PERSON] (2018) performed manual OBC for inventory the phenological condition of forests following fire events. [PERSON] (2018) digitized three classes of post-fire damage severity, in addition to target tree species identification using image segmentation. [PERSON] (2018) constructed a three-class vegetation height map and pine regeneration stand map through the visual interpretation and digitization of image objects. [PERSON] et al. [PERSON] et al. (2019) achieved an 84.3% OA for a three-class vegetation burn severity inventory. [PERSON] (2018) performed image differencing of burn classes _following_ Paradise, CA wildfires with multiple OBC maps. [PERSON] et al. [PERSON] et al. (2019) also performed three-class OBC of burn severity following wildfires in the southern plains. [PERSON] et al. [PERSON] et al. (2019) assessed tropical peatland burns with OBC of LC and achieved a 96% OA when compared to verified high-resolution manned imagery. While a range of OAs and individual class accuracies have been demonstrated, much of the research that has been done has focused on assessing the damage (burn severity or gap size/frequency) and conducting an inventory of land covers (heights, species, and percent coverage) solely after the disturbance events; some of the studies have used satellite or manned aircraft imagery for pre-disturbance datasets [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2019).