MESA Lab presented two research posters at UAS MAPPING RENO 2014 (ASPRS)

November 1, 2014

http://uasreno.org/ (news coverage) 530 attendants, among them, 200 ASPRS members. (ARPRS: American Society for Photogrammetry and Remote Sensing)

MESA Lab Director Prof. YangQuan Chen and Ph.D. candidate Tiebiao Zhao,  attended this important event with two research posters presented for two days (Oct. 20 to Oct. 22). Details of the posters are in the end of this post. (MESA LAB new ASPRS members: Tiebiao, Brendan Smith and Dr. Chen).

UC Merced has a strong presence in this historical event. Jefferson Laird, CITRIS @ UCMERCED and Erin Mutch, SPARC @ UCMERCED attended the event too.

Prof. Josh Viers and YangQuan Chen has a partnership meeting on Monday Oct. 20th, 2014 with UN Reno School of Engineering Dean Maragakis and Lieutenant Colonel Warren “Bum” Rapp (Ret) of UNR's Nevada Institute for Unmanned Systems (NIAS).

We have met some old friends and got some new friends. A well worthwhile trip!

=== Poster-1  title/author/abstract

UC Merced Scientific Data Drone Research (I):  Environmental DNA (eDNA) Sampling Water Drone

Brendan Smith, Ph.D. Student, Mechatronics, Embedded Systems and Automation Lab, School of Engineering, University of California, Merced

PI: Prof. YangQuan Chen, E: yqchen@ieee.org, UC Merced; Co-PI: Prof. Mike Miller, UC Davis.

ABSTRACT: The DNA in genetic material that all organisms shed into their environment through feces, mucus and urine is known as environmental DNA (eDNA) which can be used to detect aquatic organisms. To make effective surveillance and early detection of invasive aquatic species (both plants and animals), millions of dollars have been spent trying to conduct early detection – rapid response, but the early detection is difficult when access is limited and the species are cryptic. Currently, manual water sample and A/D data collection are a bulk of these costs, thus a low-cost solution must be explored. This project is aimed to develop low-cost and potentially high resolution alternative to current methods via the use of Unmanned Aerial Systems (UAS). Hence, eDNA-drones are an intelligent solution with smart sensing strategies developed throughout this project. This project is a multidisciplinary effort funded and fostered by the CITRIS Intelligent Infrastructure Initiative to provide the basis for a collaboration with our partners in environmental conservation (The Nature Conservancy, U.S. Fish and Wildlife Service), and be supportive to our targeted proposals to the NSF, FWS, and DoD SERDP programs.

Acknowledgements: This project is supported by CITRIS seed grant (2014-2015). Special thanks go to Joshua Viers for constant support and Mark Lutz for securing the land space for setting up the dedicated “UC Merced Scientific Data Drone Test Site” since July 2014. http://mechatronics.ucmerced.edu/scientific-data-drone-test-site

=== Poster-2  title/author/abstract

UC Merced Scientific Data Drone Research (II):  Small Plot Crop Water Stress Early Detection Using Low Cost Multi-Spectral Remote Sensing  Data Drones with Simplified Radiometric Calibration

Authors: Tiebiao Zhao, Ph.D. Candidate, with undergraduate researchers: Alejandro Sanchez, Andreas Anderson, Yoni Shchemelinin, PI: Prof. YangQuan Chen, Director, MESA (Mechatronics, Embedded Systems and Automation) Lab, University of California at Merced, Merced, CA 95343, USA

Corresponding author: Professor YangQuan Chen, yangquan.chen@ucmerced.edu  https://twitter.com/YangQuanChen

MESA LAB @ UC MERCED Scientific Data Drone Test Site: http://mechatronics.ucmerced.edu/scientific-data-drone-test-site

ABSTRACT: In Agriculture, early detection of water stress is important to reduce crop yield losses. Traditionally, water stress measurement is done by field scouts analyzing representative leaf samples, and/or by analysis of hyper-spectral imagery from satellites or aircraft.

Recently, unmanned aerial vehicles (UAVs) have demonstrated potential in this field. The ability to cheaply capture on-demand, high-resolution, multispectral (NIR/RGB) imagery has the potential to revolutionize precision agriculture – it offers higher resolution data than any other method, at a fraction of the cost of manned aircraft. However, there is still a considerable gap between the promises of the technology, and farmers’ needs. This project aims to fill that gap by developing easy-to-use, low-cost imaging UAVs, as well improving methods for extracting actionable information from the imagery.

The project is financially supported in part by the UC ANR Competitive Grant Award No. 13-2628. 2014-2019. "Evaluating and extending the use of small, multi-rotor unmanned aerial vehicles (UAV's) as a crop monitoring tool". PI: David Doll of UCCE Merced County.

Special thanks go to Brandon Stark for help in radiometric calibration, camera settings and raw pre-processing; Brendan Smith for help in synchronization of camera triggers; Larry A. Burrow and Andrew Ray of UCCE Merced for sharing field experience.