when: Tuesday 2 May 2017, 10-11am
where: Inria-Paris, A115
Speaker: Zeshi Zheng, Graduate Student Research Assistant, University of California, Berkeley
Title: Moderate-resolution Snow Water Equivalent Estimation for Mountainous Area Using Wireless-sensor Networks and Remote-sensing Data
Abstract: My research focuses on near real-time snow water equivalent estimation in mountainous area by integrating various information sources from recent technology; e.g. ground measurements from wireless-sensor networks (WSNs) and remote-sensing data, such as Lidar and satellite images. Remote-sensing datasets are capable of capturing the spatial distribution of snow coverages. However, most satellite platforms can only provide a scene daily or every few weeks. Also the current technology does not allow us to estimate snow quantity from snow coverage in real time. The dense in-situ measurements from the wireless-sensor networks compensate the above drawbacks in remote-sensing data, as they provide snow-depths at topographically varied locations in the mountains and they have a much higher temporal resolution than remote-sensing platforms. By combining the WSNs and remote-sensing data using statistical methods, we are able to produce accurate snow water equivalent estimate in real time.
The talk will focus on introducing the k-nearest neighbors + Gaussian process method that was developed, with a focus on modeling, and spatial database tools that have been used, and discussions of the results.
Bio: Zeshi Zheng (http://ucwater.org/person/zeshizheng) is a graduate student in the Department of Civil and Environmental Engineering at UC Berkeley. Zeshi has been working on water-related scientific and technical issues of the state of California since 2013. His current research is focusing on using cutting-edge computer science technologies and statistical methods to synchronize the remotely sensed spatial data and ground measurements and create real-time spatial snowpack information systems of headwater regions. Zeshi earned dual degrees of B.S. in mechanical engineering and civil engineering from Shanghai Jiaotong University and University of Michigan at Ann Arbor. He earned M.S. in Civil Systems at the University of California, Berkeley.
