## 4 Discussion
In addition, more arid areas, such as the CDT Chihuahuan Desert, Arizona/New Mexico Mountains and Plateau, Wyoming Basin, and the PCT Mojave Basin and Range, are also missing PhenoCCam validation data. While the MODIS NDVI estimates were not validated with PhenoCCam data in this study, in [PERSON] et al. 's [77] study various sensing methods for forest phenology were tested and correlation (r > 0.93) was found between the in situ PhenoCCam data and MODIS estimates. To support the more accurate assessment of vegetation dynamics, funding and installation of additional PhenoCams within a variety of ecosystems is required. As technology advances and infrared spectrometry becomes more widely implemented at PhenoCCam sites, using in situ NDVI estimates could improve the validation accuracy. The Hansen Forest Loss analysis revealed that forest loss events were heavily driven by wildfire, particularly along the western trails. This study documented the prevalence of wildfire on the PCT and the increased frequency and severity of these events over the past few decades [108]. The loss of tree cover due to the increased occurrence of wildfires is well documented in California [102]. Wildfire also devastated portions of the CDT during the study period. For example, the Calf Canyon/Hermits Peak Fire in 2022 was the largest and most destructive wildfire in New Mexico's history and impacted land cover as well as air quality [49]. PCT and CDT ecoregions such as the mid-elevation Southern Cascades and the Northern Sierra Nevada have a long history of recurring wildfires [46; 109]. After low to moderate burn events, robust growth of understory species can lead to increased biodiversity. However, the increased severity of recent fires coupled with warmer and drier climate conditions can slow ecosystem recovery and may require habitat restoration efforts [110]. The LandTrendr recovery analysis demonstrated the lasting impact of forest disturbance. While some locations recovered quickly, the multiple years being required to reach stability after severe forest disturbance is consistent with the analyses by [PERSON] et al. [83] and [PERSON] et al. [111]. While multiple spectral indices performed strongly for the temporal segmentation, the NDVI was unable to capture forest disturbance at four sites and the TCB failed at thirteen sites. While the NDVI is a standard index of vegetation \"greenness\" with broad applicability, spectral indices using the SWIR band have shown increased sensitivity in leaf-on conifer canopies [112]. Indeed, SWIR-based indices such as the TCW and NBR exhibit stronger performance for forest disturbance detection [113] and exhibited the best fit with lower RMSE values in this study. Locations with faster recovery were associated with smaller disturbances and less severe wildfires [104]. In locations with severe wildfire occurrences, such as the Southern and Baja California Pine-Oak Mountains, a shift to a more herbaceous grassland habitat occurred. Prior research by [PERSON] et al. [53] also indicates that the presence of long-needle timber litter is correlated with the occurrence of high-intensity wildfires in Central California. While many of the spectral indices performed well, the interpretation of a single vegetation index may pose issues for accurately detecting certain forest disturbance types [114]. In addition, the LandTrendr model was applied using the median reflectance values across a 1000 m study area, which may fail to capture the loss of forest species scattered throughout the canopy, such as the Eastern Hemlock. Another potential limitation of this study is the compositing timestep used by the LandTrendr fitting algorithm, as lower temporal resolutions may miss inter-seasonal variability at shorter time intervals [115; 116]. For example, numerous locations experiencing low-severity wildfires (MTBS data) did not appear within the annual Hansen Forest Loss data, potentially due to recovery within less than one year.