All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.