## 4 Discussion
### Objective 2: Data Classification Parameters  
Using the high-quality georeferenced orthomosaics generated in the previous step, supervised data classification methods were used to produce a series of land cover classification maps surrounding both the PWA prescribed prairie burn and the Volz selective timber harvest sites (Figures 10 and 11). This was done by adjusting the segmentation parameters that affected the spatial, spectral, and size details of the output segmented objects. As [PERSON] et al. [24] noted, \"determining an optimal segmentation parameter is a heuristic, subjective, challenging, and time-intensive trial-and-error process\" (p. 7). Looking at the resulting classifications, it appears that this process might be to blame for not achieving even higher overall and individual classification accuracies. While the \"optimal\" parameters for the cm-level resolution of images used in this study were assumed to be suitable for all datasets collected with the same sensor, this was decided after processing the PWA datasets. In hindsight, each iteration of the segmentations tried should have been classified to determine which parameters led to the best initial classification accuracies before segmenting the rest of the datasets. Furthermore, the timber harvest datasets would likely require different parameters, especially for the spectral detail and minimum segment size, to better delineate large tree crowns and distinguish felled timber stem colors from bare ground in canopy gaps.  
Before collecting training samples, classification schemas were developed for both land covers, with specific and appropriate classes for each. When monitoring the vegetation loss and recovery surrounding the prescribed burns, it was important to know the proportions of no vegetation growth (bare ground), fuel (litter), healthy and/or unconsumed vegetation (green vegetation), and vegetation consumed by the fire (burned vegetation) [41]. By quantifying these areas both _before_ and _after_ the disturbance, land managers had an objective determination of the prescribed burn efficacy as it relates to the native ecosystem vitality. For selective timber harvests, it was important to monitor the proportions of no visible growth and/or felled stems (bare ground and woody debris), regenerative growth (understory vegetation), and well-established growth (mature canopy), with woody debris being a function of wildlife habitat and forest structure [80]. While in some ways, quantifying the volume of these classes in terms of biomass would be more desirable for forest managers, measuring the \"area\" of each land cover still provided a valuable insight into the patterns and rates of regeneration as they relate to harvest techniques [6; 10; 25; 26; 37; 70].  
Next, sample polygons were collected for each segmented image to train the machine-learning classification algorithm. Provided that the recommended sample size was equal across all classes with \(\geq\)50 per class [24], 65 samples were used for the PWA classifications. While this worked well for the PWA datasets, Volz required 80 samples per class to produce quality land cover classifications. This was likely due to the similar spectral profiles of the mature canopy and understory vegetation, combined with less-than-optimal segmentation parameters for the given land cover. Furthermore, distinguishing between understory vegetation and mature canopy proved difficult from visual interpretation alone. As a result, each Volz classification displayed instances of a ring around some tree crowns that were misclassified as understory vegetation. Layering a 3D digital surface model (DSM), which was a generated output from photogrammetric processing, could help differentiate the understory from the canopy through height filtering [27; 56; 71].  
Despite the number of samples collected, the machine-learning algorithm that performed the best and, therefore, was selected for the final land cover classifications in this study, was the support vector machine (SVM).