Mountain pine bark beetles (MPBB, Dendroctonus ponderosae) are a primary driver of tree mortality in pine-dominant North American forests. To characterize the spatiotemporal dynamics of MPBB infestations in lodgepole pine forests of north central Colorado, we analyzed Landsat spectral trends for 2005 and 2009. Using a stratified random sampling design (N = 1,021), we classified land cover trajectories to identify stable forests, infestation, fire, clear-cutting, and regrowth sites. A Random Forest (RF) classifier was developed to detect infestation presence and predict mortality severity. The model achieved high classification accuracies of 96% (2005) and 97% (2009), while the regression for mortality severity yielded a strong fit (R² = 0.878) with a low Root Mean Square Error (RMSE = 0.1425). A rigorous topographic analysis revealed that infestation risk is strongly non-random: South-facing slopes exhibited 9.0 times higher odds of infestation compared to north-facing slopes, likely driven by solar insolation and water stress. However, limitations remain in detecting low-severity mortality (<25% canopy loss) and distinguishing species-specific responses in mixed stands. A simple binary classification of "disturbed" versus "undisturbed, " as is the primary output of many earlier studies, is insufficient for prioritizing management actions. To address these challenges, we propose integrating multi-source data fusion (e.g., Landsat and Sentinel-2), leveraging UAV-based sub-pixel validation, and utilizing phenological metrics from Harmonized Landsat-Sentinel (HLS) data. These advanced approaches, combined with the RF modeling demonstrated, offer a pathway for more precise, early-warning monitoring of forest health in complex topographic landscapes. In addition, this approach can be used for future studies designed to track the location of trees that have developed self-immunity to the beetles with the hope of reforesting with seedlings of these resistant trees.