The southeastern United States is one of the most productive forestry regions in the world, encompassing approximately 100 million ha of forest land, about 87% of which is privately owned. Any alteration in this region's duration or rate of forest recovery has consequential economic and ecological ramifications. Despite the need for forest recovery monitoring in this region, a spatially comprehensive evaluation of forest spectral recovery through time has not yet been conducted. Remote sensing analysis via cloud-computing platforms allows for evaluating southeastern forest recovery at spatiotemporal scales not attainable with traditional methods. Forest productivity is assessed in this study using spectral metrics of southern yellow pine recovery following stand-replacing disturbance. An annual cloudfree (1984-2021) Landsat time series intersecting ten southeastern states was constructed using the Google Earth Engine API. Southern yellow pine stands were detected using the National Land Cover Database (NLCD) evergreen class, and pixels with a rapidly changing spectrotemporal profile, suggesting stand-replacing disturbance, were found using the Landscape Change Monitoring System (LCMS) Fast Loss product. Spectral recovery metrics for 3,654 randomly selected stands in 14 Level 3 EPA Ecoregions were derived from their 38-year time series of Normalized Burn Ratio (NBR) values using the Detecting Breakpoints and Estimating Segments in Trend (DBEST) change detection algorithm. Recovery metrics characterizing the rate (NBRregrowth), duration (Y2R), and magnitude (K-shift) of recovery from stand-replacing disturbances occurring between 1989 and 2011 were evaluated to identify long-term and wide-scale changes in forest recovery using linear regression and spatial statistics respectively. Sampled stands typically recover 35% higher in NBR than pre-disturbance and, on average, spectrally recover within seven years of disturbance. Recovery rate is shown to be increasing over time; temporal slope estimates for NBRregrowth suggest a 33% increase in early recovery rate between 1984 and 2011. Similarly, recovery duration measured with Y2R decreased by 43% during the study period with significant spatial variation. Results suggest that the magnitude of change in stand condition between rotations has decreased by 21% during the study period, has substantial regional divisions in high and low magnitude recovery between coastal and inland stands, and low NBR value sites have the most potential to increase their NBR value. Observed spatiotemporal patterns of spectral recovery suggest that changes in management interventions, atmospheric CO2, and climate over time have changed regional productivity. Results from this study will aid the understanding of changing productivity in southern yellow pine and will inform the management, monitoring, and modeling of this ecologically and economically important forest ecosystem. / Master of Science / The Southeast United States contains approximately 100 million hectares of forest land and is one of the world's most productive regions for commercial forestry. Forest managers and those who model the effects of different types of forest land on the changing climate need up-to-date information about how productive these forests are at removing carbon and producing wood and how that productivity differs across space and time. In this study, we evaluate the productivity of southern yellow pine stands by measuring stand recovery attributes from a disturbance that removes the majority or all of the trees in the stand. This is accomplished by locating 3,654 of randomly selected disturbed pine stands through ten southeastern states using freely available national data products derived from Landsat satellite imagery, namely a combination of the National Land Cover Database (NLCD) and the Landscape Change Monitoring System (LCMS), which provide information about the type of forest, and the year and severity of disturbance respectively. Annual Landsat satellite imagery from 1984 to 2021 is used to create a series of values over time for each stand representing the stand condition each year using an index called the Normalized Burn Ratio (NBR). A change detection algorithm called DBEST is applied to each stands NBR values to find the timing of disturbance and recovery, which is used to create three metrics characterizing the rate (NBRregrowth), duration (Y2R), and magnitude (K-shift) of recovery. We evaluated how these metrics change through time using linear regression and how they differ across space using regression residuals and spatial statistics. Across the region, stands typically increase in recovery rate, decrease in recovery duration, and decrease in recovery magnitude. On average, stands recover within seven years of disturbance and to a higher NBR value than pre-disturbance. However, there is significant spatial variation in this metric throughout the Southeast. The results indicate that stands with a lower vegetation condition, measured with NBR, before the disturbance had the most significant gain in stand condition after recovery, and stands with initially higher vegetation condition did not increase as much after recovery.