Harmful algal blooms (HAB)s are an increasing threat to freshwater quality, public health, and aquatic ecosystems, costing New York State millions of dollars in annual damages. Yet the frequency, magnitude, and duration of HABs is poorly documented for inland freshwater lakes and ponds. Current field-based sampling followed by laboratory analysis to detect and monitor HABs is expensive, labor-intensive, and slow, delaying critical management decisions. The utility of satellite-based multispectral remote sensing to rapidly detect, monitor, and forecast HABs has been demonstrated at large oceanographic scales; however, low spatial and spectral resolution and inadequate revisit time severely limit the usefulness of satellite-based remote sensing techniques for inland freshwater ponds and lakes. We conducted a pilot study aimed at assessing the utility of efficient low-cost unmanned autonomous vehicle systems and spectral sensors for the rapid real-time detection and monitoring of HABs. The research resulted in the production of chlorophyll-a and cyanobacteria concentration maps and the development of a hyperspectral calibration methodology. This new state-of-the-art research methodology will allow for targeted assessment, monitoring, and design of HABs management plans that can be adapted for other impacted water bodies in New York State and implemented by managers at the NYSDEC, NYSDOH, and NYSDAM.