Got Point Clouds: Characterizing Canopy Structure With Active and Passive Sensors

Unmanned Aerial Systems (UAS) provide the means to acquire highly customized aerial data at local scale with a multitude of sensors. UAS allow us to obtain affordably repeated observations of canopy structure for agricultural and natural resources applications by using passive optical sensors, such as cameras and photogrammetric techniques, and active sensors, such as lidar (Light Detection and Ranging). The objectives of this presentation are to: (1) offer a brief overview of UAS used for agriculture and natural resources studies, (2) describe experiences in conducting agriculture phenotyping and forest vegetation measurements, and (3) give details on the methodology developed for image and lidar data processing for characterizing the three dimensional structure of plant canopies. The UAS types used for this purpose included rotary platforms, such as quadcopters, hexacopters, and octocopters, with a payload capacity of up to 19 lbs. The sensors that collected data over two crop seasons include multispectral cameras in the visible color spectrum and near infrared, and UAS-lidar. For ground reference data we used terrestrial lidar scanners and field measurements. Results comparing UAS and terrestrial measurements show high correlation and open new areas of scientific investigation of crop canopies previously not possible with affordable techniques.