Chen Erxue

Prof.  Chen Erxue

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

Research category: Forest Management

Research field: Forest remote sensing; radar forest application; forest resource monitoring with remote sensing

E-mail: chenerx@ifrit.ac.cn

Main work

Prof. Chen Erxue, PhD supervisor and director of Forest Remote Sensing Technology and Application Research Department. In 2002 and 2004, he was a short-term visiting scholar in SARMAP, Switzerland, and the Institute for earth observation of the European Space Agency. Currently, he is a member of Quantitative Remote Sensing Professional Committee of China, Remote Sensing Application Association and a member of User Committee of China Remote Sensing Satellite Ground Station and Aerial Remote Sensing System. He was Outstanding Youth of Chinese Academy of Forestry. He was the PI of one NSFC key project, two 863 projects; He has won 2 second prizes of National Science and Technology progress and 5 provincial and ministerial science and technology progress awards.

In the past five years, Prof. Chen Erxue led two national key research and development projects: "Research on key technologies of plantation resources Monitoring" and "Forest above-ground biomass estimation technology by integrating multi-source Remote Sensing", and published 17 corresponding author papers, including 14 SCI/EI papers; Participated in the publication of 4 monographs, issued 2 group standards, 1 national standards, and obtained 7 national invention patents.

A "three-stage" terrain radiation correction method using RPC model for polarimetric SAR data was proposed for forest monitoring in mountain regions; The multi-layer scattering model of interferometric SAR was developed, and the retrieval accuracy of forest height was improved by realizing that the observation calculation and theoretical model of InSAR coherence obey the same assumption. Prof. Chen Erxue developed new forest mean height estimation method using dual-frequency interferometric SAR and new method for vertical structure parameter estimation from tomographic SAR data. These new methods have improved the automatic quantitative processing level of SAR data and the quantitative inversion accuracy of forest parameters, promoting the progress of radar remote sensing technology in forestry applications.

Key research projects

  • Forest above-ground biomass estimation technology using multi-source remote sensing data, 2023.12-2027.11.

  • Forestry application flight and verification of the aviation system of High-resolution Earth Observation System, 2020.04-2022.12.

  • Research on Key technologies for monitoring plantation resources, 2017.07-2021.06.

  • Demonstration of forest resource survey application of High-resolution Earth Observation System, 2011.1-2015.12.

  • Land Remote Sensing application research of multi-dimensional microwave imaging, 2009.1-2012.12.

  • Comprehensive retrieval technology of ecological and environmental parameters of multi-frequency and multi-spectral remote sensing data, 2009.1-2010.12.

Awards & achievements

  • Liang Xi Forestry Science and Technology Award Second Prize for Science and Technology Progress, 2022, Forest intelligent monitoring technology in fine scale based on space-air-ground stereo remote sensing observation.

  • Grand Prize of surveying and mapping Science and Technology Progress, 2018, Object-oriented Highly reliable SAR Processing System.

  • Second Prize of National Science and Technology Progress, 2018, High resolution Remote sensing Forestry Application Technology and Service Platform.

  • Major Science and Technology Achievement Award of the Chinese Academy of Forestry, 2017, High resolution Remote sensing Forestry Application Technology and Service Platform.

  • First prize of geographic information technology progress , 2017, High resolution Remote sensing Forestry Application Technology and Service Platform.

  • Second prize of surveying and mapping science and technology Progress, 2011, Forest Resources Remote Sensing Monitoring Quantitative Comprehensive Processing and business operation System.

  • Second Prize of National Science and Technology Progress, 2009, Remote Sensing Monitoring Technology and Operational application of Forest Resources.

Published articles & books

  • Zhao L, Chen E X, Li Z Y, et al. 2023. Radiometric Terrain Correction Method Based on RPC Model for Polarimetric SAR Data. Remote Sensing, 15: 1909.

  • Li W M, Zhang Y, Zhang J D, et al. 2023. Tropical forest AGB estimation based on structure parameters extracted by TomoSAR. International Journal of Applied Earth Observation and Geoinformation, 121:103369.

  • Ding X Y, Chen E X, Zhao L, et al. 2023. Comparison and evaluation of several methods for estimating the average density of total forest volume in forest farm, Journal of Beijing Forestry University, 45(2),11-23.

  • Ding X Y, Chen E X, Li Z Y, et al. 2023. A review of remote sensing application in national forest inventory, Journal of Nanjing Forestry University (Natural Science Edition), 47(1), 1-12

  • Zhao L, Chen E X, Li Z Y, et al. 2022. The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data. Remote Sensing, 14(3): 595.

  • Xu K P, Zhao L, Chen E X, et al. 2022. Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data. Remote Sensing, 14(13): 3070.

  • Zhao L, Chen E X, Li Z Y, et al. 2021. A New Approach for Forest Height Inversion Using X-Band Single-Pass InSAR Coherence Data.IEEE Transactions on Geoscience and Remote Sensing, 60: 1-18.

  • Wan X X, Li Z Y, Chen E X, et al. 2020. Forest Above Ground Biomass Estimation Using Multi-Features Extracted by Fitting Vertical Backscattered Power Profile of Tomographic SAR. Remote Sensing, 13(2):186.

  • Zhao L, Chen E X, Li Z Y, et al. 2017. Three-Step Semi-Empirical Radiometric Terrain Correction Approach for PolSAR Data Applied to Forested Areas. Remote Sensing, 9(3):269

  • Zhang W, Li Z Y, Chen E X, et al. 2017. Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion. Remote Sensing, 9(6):591.