Recent advancements in unmanned aerial systems and GPS technology, allowing for centimeter precision without ground-based surveys, have been groundbreaking for applications in the field of forestry. As this technology becomes integrated into forest management approaches, it is important to consider the implementation of proper safety and data collection strategies. The creation of such documentation is beneficial, because it allows for those aspiring to create a UAS program to learn from others’ experiences, without bearing the consequences of past blunders associated with the development of these practices. When establishing a UAS program, it is pertinent to deeply research the necessary equipment, create documentation that establishes operational norms, and develop standards for in-field operations. Regarding multispectral vs. RGB sensor payloads, the sensor selection should be based upon what type of information is desired from the imagery acquired. It is also important to consider the methods for obtaining the most precise geolocation linked to the aerial imagery collected by the sensor. While selecting the proper UAS platform and sensor are key to establishing a UAS operation, other logistical strategies, such as flight crew training and operational planning, are equally important. Following the acquisition of proper equipment, further preparations must be made in order to ensure safe and efficient operations. The creation of crew resource management and safety management system documentation is an integral part of any successful UAS program. Standard operating procedure documents for individual tasks and undertakings are also a necessity. Standardized practices for the scheduling, communication, and management of the UAS fleet must also be formulated. Once field operations are set in motion, the continuous improvement of the documentation and best practices is paramount.

Unmanned Aerial Systems (UAS) serve as an excellent remote-sensing platform to fulfill an aerial imagery data collection niche previously unattainable in forestry by satellites and manned aircraft. However, for UAS-derived data to be spatially representative, a precise network of ground control points (GCP) is often required, which can be tedious and limit the logistical benefits of UAS rapid deployment capabilities, especially in densely forested areas. Therefore, methods for efficient data collection without GCPs are highly desired in UAS remote sensing. Here, we demonstrate the use of postprocessing kinematic (PPK) technology to obtain subcentimeter precision in datasets of forested areas without the need for placing GCPs. We evaluated two key measures, positional variability and time efficiency, of the PPK technology by comparing them to traditional GCP methods. Results show that PPK displays consistently higher positional precision than traditional GCP approaches. Moreover, PPK surveys and processing take less time to complete than traditional GCP methods and require fewer logistical steps, especially in image acquisition. The time and resource savings with PPK as compared to GCP processing are undeniable. We conclude that PPK technology provides a practical means to produce precise aerial forest surveys.

This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed t-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.