Drones, Remote Sensing, and Machine Learning

The use of drones equipped with lightweight sensors and powerful photogram­me­try software is changing approaches to data collection and analysis for envi­ron­men­tal sites. There are multiple sensors that can be mounted on drones that provide information on restoration sites. These sensors include multi­spec­tral, high resolution digital, LiDAR, thermal sensors, and hyper­spec­tral sensors. Drones can provide views not otherwise possible (multi­spec­tral data) and allow for greater volumes of data collection than traditional field methods. Drones are not a one size fits all approach to data collection, but when paired with traditional methods, can go beyond traditional sampling schemes and unlock insights at multiple scales.

Multiple case studies will be presented on the use of drones and various sensors paired with machine learning models that are used to evaluate site topography and erosion, map and monitor native and invasive species, assess ecosystem health, and track restoration progress. Drones provide an opportunity to gain additional insights for site char­ac­ter­is­tics and allow for data collection on a much greater scale than traditional field work. By leveraging drone-derived data and machine learning, we can scale back the amount of traditional field data necessary while increasing the area of monitoring. 

Presented by Brendan Brown, PWS, and Andrew Reicks, GISP, CFM

Moderated by Jackie Mosher , PE, PMP, BCEE 

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