梁欣廉
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Date of Birth:1980-03-09
E-Mail:
Date of Employment:2020-12-01
School/Department:State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing
Education Level:With Certificate of Graduation for Doctorate Study
Business Address:State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing
Gender:Male
Contact Information:xinlian_liang@hotmail.com
Status:Employed
Alma Mater:Aalto University
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Close-range sensing observes targets at a target-to-sensor distance ranging from a non-contact short range up to several hundred meters. It is a rapidly evolving research area that provides unprecedented capabilities to achieve insights on physical, biophysical, and ecological processes. It also collects field reference for the calibration and verification of remote sensing observations. Thus, close-range sensing acquires new knowledge and high-resolution in-situ observations to create a pathway for airborne or satellite remote sensing to revealing terrestrial processes in large areas. Recent close-range sensing has become a new interdisciplinary field that comprises data sciences, sensor and platform technologies, machine learning, physically-based modeling, robotics, and artificial intelligence (AI). The application of close-range sensing in forests has experienced rapid evolution in the past decade. Along with pushing new hardware and software developments, close-range sensing is creating new opportunities and solutions for tackling critical challenges in in-situ forest investigations, such as the cost and efficiency of field measurements to support regional and global applications, the use of the observations for reliable and accurate parameter estimations for broader scale modeling, and the use of high spatial-, temporal-, and spectrum-resolution datasets for improving our understanding of forest processes. The increasing variety and accumulating volume of close-range sensing data also raised new demands on innovative methods and best practices for data collection, preprocessing, and processing in order to meet the expectations of different application scenarios.
This special issue invites submissions of studies on close-range sensing with forest ecosystems being the focus subjects, and encourages discussions on creating or strengthening links between close-range sensing and airborne and satellite remote sensing observations. The topics include, but are not limited to:
—Demonstration of the capacity and limitations of close-range sensing systems to observe and monitor the environment
—Application of new analysis methods, including machine and deep learning approaches, to close-range sensing observations for attribute estimation for broader scale models and for high-resolution monitoring at site scales
—Combining different close-range sensing data and approaches for new knowledge
—Synergetic use of data acquired from close-range sensing with airborne and satellite remote sensing observations for large-area applications, e.g., through automated in-situ investigations
—Insights into the use of close-range sensing systems and analysis approaches for understanding terrestrial carbon functioning, climate change, and biodiversity
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Prof. Dr. Xinlian Liang received the Doctor degrees with honors in Geoinformatics from Aalto University, Espoo, Finland, in 2013. In 2016, he received a title of Docent at the University of Helsinki, Helsinki, Finland. He joined the Wuhan University in 2021. Previously, he was a research manager in the Finnish Geospatial Research Institute (FGI).
Dr. Liang's main research interest lies in the quantitatively understanding forest 3D structures, characteristics, and their roles in nature such as the carbon and nutrient fluxes, the energy and water cycles, as well as the biodiversity and climate changes. In past twenty years, he had collected rich experiences on forest digitization with a wide spectrum of technologies including the laser scanning, optical images, as well as multi- and hyper-spectral sensors, from terrestrial, mobile, UAV, aerial, and satellite perspectives.
His references in science are represented by a series of highly citied papers, e.g., ESI hot/highly cited paper (0.1% top cited paper in Geosciences, in ISPRS Journal 2016, first author; 1% top cited paper in Geosciences, in ISPRS Journal 2018, first author), Google scholar matrix paper (in IEEE TGRS 2012, first author), and most downloaded paper (in IEEE TGRS 2015, first author). He has an H-index 32 (ISI) and 40 (Google Scholar). Dr. Liang's publication list includes over 100 peer reviewed papers (90+ in ISI Web of Science). Since 2016, he has supervised 20+ Postdoc, PhD, and master students. Dr. Liang was listed in the World's Top 2% Scientists ranking by Stanford University in 2021 and 2022 (Top 1% in 2022).
Dr. Liang is actively involved in the international research community. He was the Chair of the Working Group III/1 'Thematic Information Extraction', the International Society for Photogrammetry and Remote Sensing (ISPRS), 2016-2022. Since 2022 he is chairing the Working Group III/1 'Remote Sensing Data Processing and Understanding'. He is the Associated Editor of Forest Ecosystems and Editor of Geo-spatial Information Science as well as Remote Sensing (all web of science listed). He is also the Guest Editor of Remote Sensing of Environment.
Personal website：https://scholar.google.com/citations?user=foWAGwYAAAAJ&hl=en&oi=ao
