## 1 Introduction  
To combat destructive disturbance events, such as wildfire, land managers are increasingly implementing frequent low-intensity planned disturbances, such as selective timber harvest and prescribed burn \"treatments\", to remove overgrown and non-native vegetation fuel loads in a controlled setting. Planned disturbance treatments like these belong to a methodology of forest management known in the industry as natural disturbance-based management or NDBM. NDBM treatments can significantly reduce damage from _unplanned_ disturbances, as well as produce healthy, diverse, and resilient forest ecosystems [1, 2]. Despite decades of research demonstrating the benefits of both prescribed burning and selective cutting, these treatments are inherently risky and can unintentionally accelerate forest degradation if done incorrectly [1]. Therefore, it is necessary when applying NDBM to regularly monitor changes to the physical environment to determine if the desired outcome was achieved and adjust practices accordingly [3]. To monitor changes, land managers must inventory the treated area both prior to and regularly after the planned disturbance event [4]. While there are many treatment efficacy and vegetation health indices, land cover is commonly used and proves to be a good indicator of a variety of forest health metrics [5, 6]. To identify and document the cause-and-effect of NDBM, it is important to accurately document the _before_ condition, and subsequently, the vegetation removed by NDBM; these steps are critical for developing the best practices for future applications of NDBM treatments. Traditionally, land cover inventories involve manual ground-based methods, which are time-intensive, laborious, and even dangerous in some areas [7]. More recently, land managers have relied upon conventional aerial imaging platforms, such as manned aircraft or satellite networks to conduct inventories [8], both of which have limits. Manned aircraft operations can also be dangerous and are very expensive [9]. Satellite imagery often lacks the temporal (return rate) and spatial (pixel size) resolution needed for this scale of disturbance [10]. Many image-collecting satellites only return to an area every few weeks and are legally limited to pixel sizes of \(\geq\)30 cm; these limitations negate the benefits of their capability to cover large areas, such as multiple watersheds, to produce landscape-level land cover classification [11]. While this resolution is useful for disastrous _unplanned_ disturbances, many planned disturbances are conducted at plot-level scales, where coarse spatial and temporal resolutions of traditional remote sensing platforms cannot provide the detail needed to derive useful land cover assessments [12]. Manned aircraft can provide more appropriate spatial resolutions but are often cost prohibitive for discrete planned disturbances [9; 10; 13]. Fortunately, drones or unmanned aerial systems (UASs) are now commonly used as a data collection tool in many industries and are useful given their ability to collect high-spatial-resolution imagery nearly on-demand [14]. UASs have been recognized as a cost-effective and efficient means to collect precise aerial imagery for repeated analyses of disturbances [4; 9; 10; 13; 15; 16; 17; 18; 19]. UASs have the potential to serve as flexible, inexpensive, and efficient aerial surveying platforms for land managers to conduct inventories before and after planned disturbance events [20; 21]. Despite their demonstrated capability as an effective geospatial data collection tool, the integration of UASs in the forestry community has, until recently, been limited. With previous UAS technologies, the vast sizes of many forests could not be covered given the endurance limits of UASs. Today, advancements in UAS design, battery technology, and mission planning software make it easier than ever to survey large areas precisely and efficiently.