Pang Yong

Prof.  Pang Yong

Organization: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry

Research category: Forest Management

Research field: Forest remote sensing

E-mail: pangy@caf.ac.cn

Main work

Prof. Pang Yong received the PhD degree of cartography and geographic information system from the Institute of Remote Sensing Applications, Chinese Academy of Science in 2006. He did post doc research in spaceborne LiDAR at the Colorado State University during 2006-2008. He was visiting professor or guest research scientist at University of Maryland (2012), University of British Columbia (2015-2016) and Swiss Federal Institute for Forest Snow and Landscape Research WSL 2018). Prof. Pang Yong is currently a professor at the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry. His research interests include LiDAR remote sensing and applications in forestry, data fusion for forest parameters estimation, forest change and carbon mapping.

Key research projects

  • 2023~2026: PI, "eco2adapt: Regulation Mechanisms of Ecosystem Resilience and Adaptive Forest Management, funded by the Ministry of Science and Technology", China (MOST) and European Union Horizon Programme

  • 2021~2024: PI, "China-ESA Forest Observation (CEFO)", Dragon V Project, funded by the Ministry of Science and Technology, China (MOST) and European Space Agency (ESA)

  • 2017~2020: PI, "Growth and Yield Prediction of Larch Plantation at Multi-scales", funded by the Ministry of Science and Technology, China (MOST)

  • 2016~2019: PI, "Forest biodiversity monitoring using high resolution remote sensing", funded by the Natural Science Foundation of China (NSFC)

  • 2012~2015: PI, "Algorithms Development for Global Forest Biomass and Carbon Storage Estimation using Remote Sensing Technologies", funded by the National High Technology Research and Development Program of China (863 program)

  • 2011~2013: PI, "Multi-temporal observations from spaceborne Lidar for temperate forest parameters estimation", funded by the Natural Science Foundation of China (NSFC)

  • 2011~2013: Co-PI, "Forest Cover and Carbon Mapping in the Greater Mekong Subregion and Malaysia", funded by Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet)

  • 2007~2010: Co-PI, "Model and method for vegetation parameters inversion from InSAR and Lidar fusion", funded by the Major State Fundamental Research Development Program (973 program)

  • 2007~2009: PI, "Forest parameters estimation from waveform lidar data", funded by the National High Technology Research and Development Program of China (863 program)

  • 2007~2009: PI, "Forest parameters estimation from spaceborne lidar and multi-angle optical data", funded by the Natural Science Foundation of China (NSFC)

  • 2004~2007: Co-PI, Investigation of potential lidar applications in forestry, responsible for lidar data processing and software analysis for forest parameters inversion, funded by State Forestry Administration Bureau of China

  • 2002~2005: Co-PI, Quantified remote sensing on forest resources, responsible for lidar data processing and forest parameters inversion, funded by The National High Technology Research and Development Program of China (863 program)

  • 2002~2003: PI, Forest recovery monitoring over burned area using multi-date Landsat TM and JERS SAR data, funded by Chinese Academy of Forestry (CAF)

  • 2001~2005: Co-PI, Image Radar processing system and forest parameters inversion, funded by The National High Technology Research and Development Program of China (863 program)

  • 2000~2003: main participation, Desertification and dust storm monitor techniques with spaceborne remote sensing data, funded by Ministry of Science and Technology of China (MOST)

Awards & achievements

  • 2020, Chinese Leadership Young Scientist of Forestry and Grassland

  • 2013, Chinese Forestry Youth Science and Technology Award

  • 2012, Distinguished Young Scholars of the Chinese Academy of Forestry

  • 2009, Second-class National Prize for Progress in Science and Technology for "Forest resource remote sensing monitoring and operational applications"

Published articles & books

Articles

  • Xiong H, Pang Y*, Jia W, Bai Y. 2024. Forest stand delineation using airborne LiDAR and hyperspectral data. Silva Fennica vol. 58 no. 2 article id 23014. 18 p.

  • Du L, Pang Y*. 2024. Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data. Plant Phenomics, 6:0145.

  • Jia W, Pang Y*, Tortini R. 2024. The influence of BRDF effects and representativeness of training data on tree species classification using multi-flightline airborne hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 207: 245.

  • Meng S, Pang Y*, Huang K, Li Z. 2023. A patch filling method for thematic map refinement: a case study on forest cover mapping in the Greater Mekong Subregion and Malaysia. GIScience & Remote Sensing, 60(1)-2252225: 1-21.

  • Du L, Pang Y*, Wang Q, Huang C, Bai Y, Chen D, Lu W, Kong D. 2023. A LiDAR biomass index -based approach for tree- and plo -level biomass mapping over forest farms using 3D point clouds, Remote Sensing of Environment, 290(113543): 1-17.

  • He L, Pang Y*, Zhang Z, Liang X, Chen B. 2023. ICESat-2 Data Classification and Estimation of Terrain Height and Canopy Height. International Journal of Applied Earth Observation and Geoinformation, 118, 103233, 1-14.

  • Jia W, Pang Y*. 2023. Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions. Journal of Forest Research.

  • Meng S, Pang Y*, Huang C, Li Z. 2022. Improved forest cover mapping by harmonizing multiple land cover products over China. GIScience & Remote Sensing, 59(1): 1570-1597.

  • Yu T, Pang Y*, Liang X, Jia W, Bai Y, Fan Y, Chen D, Liu X, Deng G, Li C, Sun X, Zhang Z, Jia W, Zhao Z, Wang X. 2022. China's larch stock volume estimation using Sentinel-2 and LiDAR data. Geo-spatial Information Science.

  • Pang Y*, Wang W, Du L, Zhang Z, Liang X, Li Y, Wang Z. 2021. Nystrӧm-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation. International Journal of Digital Earth.

  • Wang Q, Pang Y*, Chen D, Liang X, Lu J. 2021. Lidar biomass index: A novel solution for tree-level biomass estimation using 3D crown information. Forest Ecology and Management. 499. 119542. 10.1016/j.foreco.2021.119542.

  • Pang Y, Liang X , Jia W, Si L, Yan G, Shi J. 2021. The comprehensive airborne remote sensing experiment in Saihanba forest farm. National Remote Sensing Bulletin, 25(4): 904-917.

  • Ma Z, Pang Y*, Wang D, Liang X, Chen B, Lu H, Weinacker H, Koch B. 2020. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sens, 12, 1078.

  • Jia W, Pang Y*, Tortini R, Schläpfer D, Li Z, Roujean J-L. 2020. A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery Over Forested Areas with Rugged Topography. Remote Sensing, 12(3):432.

  • Chen B, Pang Y*, Li Z, Lu H, North P, Rosette J, Yan M. 2020. Forest signal detection for photon counting LiDAR using Random Forest. Remote Sensing Letters, 11:1, 37-46.

  • Chen B, Pang Y*, Li Z, Lu H, Liu L, North P, Rosette J. 2019. Ground and Top of Canopy Extraction from Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area. IEEE Geoscience and Remote Sensing Letters.

  • Liu L, Pang Y*, Li Z, Si L, Liao S. 2017. Combining Airborne and Terrestrial Laser Scanning Technologies to Measure Forest Understory Volume. Forests, 8(4):111.

  • Liu L, Coops N, Aven N, Pang Y. 2017. Mapping Urban Tree Species Using Integrated Airborne Hyperspectral and LiDAR Remote Sensing Data. Remote Sensing of Environment, 200:170-182.

  • Pang Y, Li Z, Ju H, Lu H, Jia W, Si L, Guo Y, Liu Q, Li S, Liu L, Xie B, Tan B, Dian Y. 2016. LiCHy: The CAF's LiDAR, CCD and Hyperspectral Integrated Airborne Observation System. Remote Sensing, 8(5):398.

  • Lu H, Pang Y, Li Z, Chen B. 2015. An Automatic Range Ambiguity Solution in High-Repetition-Rate Airborne Laser Scanner Using Priori Terrain Prediction. IEEE Geoscience and Remote Sensing Letters.

  • Zhao D, Pang D, Li Z, Liu L. 2014. Isolating individual trees in a closed coniferous forest using small footprint lidar data. International Journal of Remote Sensing, 35:20, 7199-7218.

  • Wang Q, Pang Y, Li Z, Chen E, Sun G, Tan B. 2013. Improvement and Application of the Conifer Forest Multiangular Hybrid GORT Model MGeoSAIL. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 51(10): 5047-5059.

  • Xu G, Pang Y, Li Z, Zhao D, Li D. 2013. Classifying land cover based on calibrated full-waveform airborne light detection and ranging data. CHINESE OPTICS LETTERS, 11(8): 082801-1.

  • Zhao D, Pang Y, Li Z, Sun G. 2013. Filling invalid values in a lidar-derived canopy height model with morphological crown control. International Journal of Remote Sensing, 34:13, 4636-4654.

  • Yong P, M. Lefsky, Sun G, Jon R. 2011. Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar. Remote Sensing of Environment, Vol. 115, No.11, 2798-2809.

  • Pang, Y., Lefsky, M., Andersen, H., Miller, M., & Sherrill, K. 2008. Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return lidar data. Canadian Journal of Remote Sensing, 34, 471-484.