Professor Qinghua Guo
Institute of Botany, Chinese Academy of Sciences
With the rapid development of remote sensing, sensor, and computer science technologies, ecosystem research has entered a new area of digital ecosystem. Digital ecosystem enables better data mining to understand its functions and dynamics. Traditional field measurement for quantifying digital ecosystem is time-consuming and labor-intensive. Optical remote sensing is fraught with “saturation effect”. By contrast, light detection and ranging (LiDAR) can acquire highly precise three-dimensional data with strong penetration ability in various working conditions, which lays good foundation for quantifying digital ecosystem and data mining. However, challenges are existed in two aspects: 1) how to develop multi-scale lidar system for quantifying digital ecosystem; 2) how to do data mining with the collected data in digital ecosystem. In this study, we introduce the hardware development of backpack lidar system, mobile lidar system, unmanned aerial vehicle lidar system, crop phenotyping system, and a crowdsource mobile application. With the aid of these hardware systems, we demonstrate the data mining applications in digital forest ecosystem, agriculture system, grassland system, and urban system. Finally, prospects and perspectives are given, including crowdsource data collection, multi-source data fusion, deep learning and hybrid modelling, graph theory and network analysis.