A combination of factors including land use change and fire suppression has resulted in the loss of pine savanna habitats across the southeastern U.S., affecting many avian species dependent on these habitats. However, due to the ephemeral nature of the habitat requirements of many pine savanna species (e.g., habitat is only present for a couple of years after a fire), targeted management of such habitats can be challenging. Moreover, the growing numbers of imperiled pine savanna species can make prioritizing management difficult. One potential tool to better inform management of pine savanna species is satellite imagery. Sentinel-2 satellite imagery data provides an instantaneous snapshot of habitat quality at a high resolution and across a large geographic area, which may make it more efficient than traditional, ground-based vegetation surveying. Thus, the objectives of my research were to 1) evaluate the use of remote sensing technology to predict habitat occupancy for pine savanna species, and 2) use satellite imagery-based models to inform multispecies management in a pine savanna habitat. To meet my objectives, I conducted point count surveys and built predictive models for three pine savanna bird species: Bachman's Sparrow (BACS; Peuacea aestivalis), Northern Bobwhite (NOBO; Colinus virginianus), and Red-Cockaded Woodpecker (RCW; Dryobates borealis) across Georgia. I assessed the performance of satellite imagery in predicting habitat occupancy of these pine savanna species and its potential for multispecies management. I found that models created using satellite imagery habitat metric data performed well at predicting the occupancy of all three species as measured by the Area Under the Receiver Operating Characteristic Curve: BACS=0.84, NOBO=0.87, RCW=0.76 (with values between 0.7-1 defined as acceptable or good predictive capacity). For BACS and NOBO, I was able to compare these satellite imagery models to field-based models, and satellite models performed better than those using traditional vegetation survey data (BACS=0.80, NOBO=0.79). Moreover, I found that satellite imagery data provided useful insights into the potential for multispecies management within the pine savanna habitats of Georgia. Finally, I found differences in the habitat selected by BACS, NOBO, and RCW, and that BACS may exhibit spatial variations in habitat use. The results of this study have significant implications for the conservation of pine savanna species, demonstrating that satellite imagery can allow users to build reliable occupancy models and inform multispecies management without intensive vegetation surveying. / Master of Science / Land-use changes have resulted in the disruption of natural disturbances such as fires, resulting in the loss of pine savanna habitats throughout the southeastern U.S. Although many of the species that occupy these habitats are experiencing rapid population declines, habitat for pine savanna species can be challenging to manage. Without reoccurring fire, pine savanna habitat can become unsuitable for obligate species within short periods of time, forcing these species to disperse to newly disturbed habitats. The transient nature of the preferred habitat of pine savanna species makes targeting management for these species difficult, as it can be challenging to locate exactly where occupied habitats exist. Furthermore, as the number of pine savanna species that are declining is large, prioritizing management of these species can be difficult especially given limited conservation funding. One potential tool to better inform the management of pine savanna species is satellite imagery. Satellite imagery can capture habitat information across broad areas, at fine resolutions, and at frequent intervals, potentially making satellite imagery more efficient than conducting field vegetation surveys on the ground for gaining information on habitat suitability. Thus, the objectives of my research were to 1) determine if satellite imagery can effectively predict the habitats occupied by pine savanna species (habitat occupancy), and 2) use satellite imagery-based models to inform the simultaneous management of multiple species (multispecies management) in a pine savanna habitat. To meet these objectives, I conducted surveys and built predictive models for three pine savanna bird species: Bachman's sparrow (BACS; Peuacea aestivalis), Northern Bobwhite (NOBO; Colinus virginianus), and Red-Cockaded Woodpecker (RCW; Dryobates borealis) in Georgia. I found models informed by satellite imagery performed well at predicting habitats occupied for all three species. Furthermore, models developed using satellite imagery performed better at predicting the habitats occupied by pine savanna species than models developed using on the ground vegetation surveys. I also found that satellite imagery data provided useful insights into strategies to manage pine savanna species simultaneously. I found evidence that BACS, NOBO, and RCW may have contrasting habitat needs and that BACS may use habitat differently between sites in Georgia. The results of this study demonstrate that satellite imagery can be used to predict the habitats occupied by pine savanna species and inform multispecies management without surveying vegetation on the ground, which is a more efficient use of time and funding.