## 5 Conclusions  
Quantifying planned disturbance is an important component of any ecosystem management plan that allows land managers to objectively determine the efficacy of their treatment and adjust the frequency and severity of NDBM treatments accordingly. To do this, inventories of the land cover must be collected both _before_ and repeatedly _after_ the disturbance event. This way, vegetation recovery can be determined through the proper inventory of land cover present _before_ the disturbance event, what was removed as a result of the disturbance treatment, and what grew back _after_ the disturbance. Traditionally, obtaining a pre-disturbance inventory has been challenging because disturbances are often _un_planned events and thus do not enable temporally precise image datasets. Traditional remote sensing methods, such as satellite and manned aircraft, are not practical due to the resolution and expense. _Planned_ disturbances present an opportunity to utilize the high temporal and spatial resolution afforded by UASs to obtain a pre-disturbance inventory, which, combined with regular and repeated _post_-disturbance inventories, support robust land management practices. This study documented the application of land cover surveys for prescribed prairie burns and selective timber harvests, which are common _planned_ disturbances in the CHF region. The prairie burn sites were less sensitive to atmospheric, sensor, and mission planning parameters than the forested sites during the data collection. The forested sites were successfully surveyed with the UAS by increasing the flight altitude, shutter speed, and the number of images collected outside the study area. The prairie burn site was successfully processed with default photogrammetric parameters, while the forest site required reducing the scale for keypoint identification and an alternative calibration method to produce quality georeferenced orthomosaics. Both land covers achieved overall accuracies between 80.8% and 97.1% and proportional efficacies between 70.2% and 93.6%. While the variance in overall accuracies for the prairie site outperformed the forested site, the difference between the land cover types was not significant. Overall, the methods of data collection, processing, and classification produced high-quality land cover assessments for two common types of _planned_ ecological disturbances within the CHF region. In future studies, researchers can build on these findings by examining optimal temporal resolutions for post-disturbance inventories, verifying land covers through field sampling, using field samples to train classifier algorithms, and experimenting with different segmentation parameters. Determining the optimal frequency and duration of surveys following a planned disturbance treatment would enhance the usability of this method and deserves future research attention. Ground truthing through field sample collection is another recommendation to further validate these methods and determine the accuracy of the land cover classifications. Ground truthing would also enable field samples to be projected onto the imagery, which can be used to support the collection of classifier training samples. Another area deserving further research is to experiment with minimum segment sizes to identify the optimal segmentation parameters for the ultra-high-resolution UAS imagery GEOBIA, especially in forested sites where distinguishing tree crowns fully would likely enhance the classification accuracy. Furthermore, filtering vegetation height with a digital surface model would be an important elaboration on the procedures demonstrated by this study. Myriad applications exist that could benefit from the workflow developed in this study. For example, land cover remediation following construction, mining, or landscaping projects could employ a similar workflow to objectively determine vegetation recovery.