There is a dearth of accessible information and innovative tools to map and obtain biomass data for analysis, conservation, and sustainable resource management of intertidal vegetation in the united…

Nearview LLC received a $149,999 Project Grant award from the National Oceanic and Atmospheric Administration under the Small Business Innovation Research Program. The grant funds the development of training data using unoccupied aerial systems to map and estimate biomass of intertidal vegetation at small spatial scales. Nearview will use drones to capture imagery at a 2-meter resolution to bridge data between small and large-scale ecological and remote sensing data. Artificial intelligence and machine learning techniques will be applied to accurately classify and quantify macroalgae biomass. This 6-month feasibility study will assess using drone imagery for quality control, co-registration, and validation of satellite or aerial data to better integrate biological and physical data for analysis, conservation, and sustainable management of coastal vegetation resources.

There is a dearth of accessible information and innovative tools to map and obtain biomass data for analysis, conservation, and sustainable resource management of intertidal vegetation in the united states and globally. A major technical problem is the integration of biological and physical data at small spatial scales (2 meters). We propose unoccupied aerial systems (UAS) can be used for quality control, CO-registration, training, and validation of satellite or high-altitude or space-based aerial imagery. Specifically, we propose to develop training data using unoccupied aerial systems (UAS) to bridge the GAP between small- and large-scale ecological and remote sensing data, and to use artificial intelligence (AI) and machine learning (ML) to accurately map, classify, and estimate biomass of intertidal vegetation such as macroalgae. This phase i study will assess the feasibility