Wildfires negatively affect the atmosphere and ecological environment. The rapid identification of smoke is helpful for early fire detection and positioning, which are significant for fire early warning, fire point tracing, and atmospheric environment monitoring. The purpose of this research is the establishment of a smoke detection framework with which to carry out smoke identification, concentration inversion and the extraction of the smoke concentration center to realize fire source identification and positioning. The spectral characteristics and variation pattern of smoke were first studied and analyzed based on a physical correlation model and laboratory experiments. Moreover, the spectral variation of the vegetation background was measured by the Mahalanobis distance (MD), and MD-based smoke identification and concentration inversion were carried out. Then, the extraction of the smoke concentration center and fire source positioning were realized based on the Laplace operator. Finally, the application and verification of the proposed method were carried out on spaceborne data of forest smoke in Daxing’anling, China, and British Columbia, Canada. The results show that: (1) At the significance level α = 0.1%, the overall accuracy of smoke recognition based on satellite images was 91.30%, and the Kappa coefficient was 81.69%. (2) The retrieved smoke concentration was in line with the visual interpretation results. (3) The fire point location error was 23.05 ± 4.14 m (less than 2 pixels). The results indicate that the proposed MD-based smoke detection model can effectively realize smoke pixel identification and concentration inversion. The proposed smoke concentration center identification method can accurately locate the fire source and provide positioning services to trace the source of wildfires in forest fire emergencies.