Elastic backscatter lidar has proven to be a powerful tool for high-sensitivity long-range detection and tracking of airborne aerosol plumes and in its application to the characterization of chemical/biological (CB) aerosol agent release events. Advanced sensor platforms and networks are now integrating lidar systems in order to improve situational awareness and increase early warning in the event of a CB attack. These system-of-systems architectures are part of an overarching strategy known as Integrated Early Warning (IEW). Lidar is playing a key role in IEW systems by providing a standoff detection layer that has the potential to provide the greatest impact on improving situational awareness and increasing early warning. Despite the sensitivity of elastic backscatter lidar to CB pathogens in the respirable range, it has proven difficult to discriminate threat plumes from variations in the aerosol background caused by kinetic events, vehicle movement and other conflict-related actions. A backscatter lidar that could differentiate between these ubiquitous background plumes and deliberately disseminated threat plumes would significantly enhance the capability of IEW systems to provide accurate and timely threat information to battlefield commanders. The Chemical, Biological, Radiological, and Nuclear Directorate of the Pentagon Force Protection Agency (PFPA-CBRN) deployed a continuously operating elastic backscatter lidar that monitored the urban atmosphere around the Pentagon. Statistical features (size, shape, location, intensity and duration) were calculated for naturally occurring plumes and compared to plumes generated from nearby simulant disseminations. This early work suggested that simulant plumes displayed spatial and temporal variations that could be exploited to discriminate them from typical background plumes. Since this initial PFPA program, a significant amount of research has gone into machine learning algorithms for a variety of tasks including image classification of remote sensing data. This work has demonstrated the potential and efficacy of active learning for challenging remote sensing data processing scenarios. Continued development of graphical processing units (GPU), along with increasingly larger datasets, has led to the resurgence of 2-D convolutional neural net (CNN) implementations for a variety of image classification and machine learning applications using deep learning. These machine learning implementations are now surpassing human-level performance for some image classification tasks. The deployment of elastic backscatter lidar systems will continue to expand the amount of data presently available for aerosol plumes. Using these larger datasets, coupled with recently developed machine learning algorithms, provides an opportunity to utilize the differences in spatial and temporal plume features to discriminate between potential threats and more benign environmental plumes.