Development course of forestry remote sensing in China

  • role: First author第一作者
  • Affiliation:

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email:Zengyuan.li@caf.ac.cn
  • Introduction:1959,E-mail: Zengyuan.li@caf.ac.cn
LI Zengyuan12,  
  • Affiliation:

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

CHEN Erxue1

Resümee

According to the progressing characteristics of research project achievements and industry applications of forestry remote sensing, the development course of China's forestry remote sensing in the past 70 years (1951—2020) is divided into three periods and reviewed. 1951—1980 was the phase of remote sensing application by visual interpretation based on aerial photo. During this phase, China has established forest inventory technology system combining aerial photography and comprehensive ground survey. 1981—2000 was the pioneering and innovative developing phase of satellite remote sensing. For the first time, the satellite remote sensing digital image processing system for forest resource inventory was developed, and some major breakthroughs were made in key technical fields such as renewable resource inventory, series thematic map production, and ecological benefit evaluation using remote sensing. Meanwhile, the application fields have been expanded to the remote sensing inventory and monitoring fields of wetland resources, desertification and desertification land, forestry disasters, etc. 2001—2020 is the phase of rapid development of quantitative remote sensing and initiative construction of comprehensive application service platform. Through in-depth research on the basic theory of forestry remote sensing application and quantitative remote sensing technology, China has promoted the rapid development of quantitative remote sensing technology and designed a comprehensive forestry monitoring technology system, and established a comprehensive forestry remote sensing application service platform. In the end, we put forwards some suggestions for the future development of scientific research and application of forestry remote sensing, in order to meet the new requirements and tasks faced by the forestry and grassland sectors in a new era.

Schlüsselwort

China;forestry remote sensing;development course;three periods;high resolution remote sensing

References

  1. 1.
    Bai L N and Li Z Y. 1995. Preliminary research of expert system for forest type classification of ERS - 1 SAR image. Remote Sensing Technology and Application, 10(2): 69-72
  2. 2.
    Chen E X and Li Z Y. 2004. Study on ENVISAT ASAR image geolocation method. Journal of Image and Graphics, 9(8): 991-996
  3. 3.
    Chen E X and Li Z Y. 2006. The algorithm for direct geo location of space borne SAR imagery based on slant angle coordinate transformation. Chinese High Technology Letters, 16(10): 1082-1086
  4. 4.
    Chen E X, Li Z Y, Pang Y and Tian X. 2007b. Polarimetric synthetic aperture radar interferometry based mean tree height extraction technique. Scientia Silvae Sinicae, 43(4): 66-70
  5. 5.
    Chen E X, Li Z Y, Tan B X, Liang Y Z and Zhang Z L. 2007a. Validation of statistic based forest types classification methods using hyperspectral data. Scientia Silvae Sinicae, 43(1): 84-89
  6. 6.
    Cheng H Z, Liu X T, Pang Z H, Yu R A and Zhao Q K. 1992. Disaster division of dendrolimus superans butler with aid of space remote sensing technology. Journal of Northeast Forestry University, 20(5): 25-32
  7. 7.
    Dai C D. 1992. Detecting vegetation damage by remote sensing. Journal of Natural Disasters, 1(2): 40-46
  8. 8.
    Ding S C. 1995. Reviews and prospects on Sichuan forest aerial apping and forestry remote sensing (to be continued). Sichuan Forestry Exploration and Design, (4): 11-15
  9. 9.
    Fan F Y, Chen E X, Liu Q W, Pang Y, Li S M and Zhao F. 2010. Forest mean height extraction based on the low-density airborne LiDAR and CCD data. Forest Research, 23(2): 151-156
  10. 10.
    Feng Y M, Li Z Y and Deng G. 2007. Quantitative estimation for forest stand crown of different stand densities by remote sensing. Scientia Silvae Sinicae, 43(1): 90-94
  11. 11.
    Feng Y M, Li Z Y and Zhang X. 2006. Estimating forest stand crown based on high spatial resolution image. Scientia Silvae Sinicae, 42(5): 110-113
  12. 12.
    He Q S, Chen E X, Cao C X, Liu Q W and Pang Y. 2009. A study of forest parameters mapping technique using airborne LIDAR data. Advances in Earth Science, 24(7): 748-755
  13. 13.
    Ji P, Yi H R and Bai L N. 1993. Artificial Neural Network to Identify Abnormally High Temperature Points in NOAA Data. The Progress of Monitoring and Evaluation of Natural Disasters by Remote Sensing. Beijing: Science and Technology Press: 48-52
  14. 14.
    Li F, Ma Y, Zhang X, Yu X W, Feng P F and Zhang M B. 2014b. Research and design of a forest management mobile service cloud platform for the natural forest protection project//Proceedings of 2014 International Conference on Future Communication Technology and Engineering. Shenzhen: Science and Engineering Research Center
  15. 15.
    Li F, Ma Y, Zhang X, Zhang M B, Yu X W and Feng P F. 2014a. Intelligent management platform of forestry based on LBS cloud services//Proceedings of 2014 International Conference on Environmental Engineering and Computer Application. Hong Kong, China: Science and Engineering Research Center
  16. 16.
    Li L Y. 1991. Application and development of remote sensing technology in forestry. Remote Sensing of Environment China, 6(3): 191-194
  17. 17.
    Li X W, Guo H D, Li Z and Wang C L. 2005. Method study of vegetation height estimation using SIR-C dual frequency polarimetric SAR interferometry data. Chinese High Technology Letters, 15(7): 79-84
  18. 18.
    Li Z X. 1990. Retrospect and prospect of forestry remote sensing. Yunnan Forest Investigation. Forest Inventory and Planning, (2): 35-36 (李芝喜. 1990. 林业遥感的回顾与展望. 云南林业调查规划, (2): 35-36)
  19. 19.
    Li Z Y, Che X J, Liu M, Bai L N and Tan B X. 1994. Preliminary research on the application of ERS–1 SAR in forest. Forest Research, 7(6): 692-696
  20. 20.
    Li Z Y and Chen E X. 2019. Technology and Method of Forest Parameters Inversion by Synthetic Aperture Radar. Beijing: Science Press: 61-65
  21. 21.
    Li Z Y, Chen E X, Gao Z H, Qin X L, Wu H G and Xia C Z. 2013. Current development status and proposals for national forest remote sensing techniques and applications. Bulletin of Chinese Academy of Sciences, 28: 132-144
  22. 22.
    Li Z Y, Gao Z H, Li F, Wu H G, Zhang X, Chen E X, Zhang H Q, Qin X L and Xia C Z. 2015. Construction and application of high-resolution forestry remote sensing application demonstration system. Satellite Application, (3): 25-30
  23. 23.
    Li Z Y, Liu Q H, Yan G J, Wang J D, Niu Z, Jiang L M and Chen E X. 2019b. Quantitative Remote Sensing Model and Inversion of Complex Surface. Beijing: Science Press
  24. 24.
    Li Z Y, Pang Y and Chen E X. 2003. Regional forest mapping using ERS SAR interferometric technology. Geography and Geo-information Science, 19(4): 66-70
  25. 25.
    Li Z Y, Qin X L, Gao Z H, Deng G and Chen E X. 2018. Research on forestry application of high resolution remote sensing. Satellite Application, (11): 61-65
  26. 26.
    Lin H, Ning X B and Lü Y. 2004. Compiling the standing volume table of Chinese fir based on the high-resolution satellite image. Scientia Silvae Sinicae, 40(4): 33-39
  27. 27.
    Lin H, Tong X D and Huang Z Y. 2002. A review on remote sensing's application, puzzle and prospect in forestry. Remote Sensing Information, (1): 39-43, 51
  28. 28.
    Liu L J, Pang Y, Fan W Y, Li Z Y and Li M Z. 2011. Integration of airborne hyperspectral CASI and SASI data for tree species mapping in the boreal forest, China. Remote Sensing Technology and Application, 26(2): 129-136
  29. 29.
    Liu Q W, Li Z Y, Chen E X, Pang Y, Li S M and Tian X. 2011. Feature analysis of LIDAR waveforms from forest canopies. Science China Earth Sciences, 54(8): 1206-1214
  30. 30.
    Liu Q W, Li Z Y, Chen E X, Pang Y, Tian X and Cao C X. 2010. Estimating biomass of individual trees using point cloud data of airborne. Chinese High Technology Letters, 20(7): 765-770
  31. 31.
    Liu X S, Huang J W and Ju H B. 2010. Research progress in the methods and applications of individual tree crown's automatic extraction by high spatial resolution remote sensing. Journal of Zhejiang Forestry College, 27(1): 126-133
  32. 32.
    Ma A P. 2019. “Arme” forestry with modern high-resolution remote sensing technology. China Rural Science and Technology, (1): 38-40
  33. 33.
    Ma Y, Li F, Zhang X and Zhang M B. 2015. The design and implementation of a forestry cloud service platform//Proceedings of International Conference on Computer Science and Systems Engineering. Hong Kong, China: [s.
  34. 34.
    Mo Q, Chen Z B, Xie S Q, Chen M J and Xia C Z. 2017. Developing a system for monitoring high resolution forestry ecological projects. Journal of Zhejiang Agriculture and Forestry University, 34(4): 737-742
  35. 35.
    Pang Y, Huang K B, Li Z Y, Qin X L andChen E X. 2011. Forest aboveground biomass analysis using remote sensing in the greater Mekong subregion. Resources Science, 33(10): 1863-1869
  36. 36.
    Pang Y, Sun G Q and Li Z Y. 2006a. Large footprint lidar waveform modelling of forest spatial patterns. Journal of Remote Sensing, 10(1): 97-103
  37. 37.
    Pang Y, Yu X F, Li Z Y, Sun G Q, Chen E X and Tan B X. 2006b. Waveform length extraction from ICEsat GLAS data and forest application analysis. Scientia Silvae Sinicae, 42(7): 137-140
  38. 38.
    Pang Y, Zhao F, Li Z Y, Zhou S F, Deng G, Liu Q W and Chen E X. 2008. Forest height inversion using airborne lidar technology. Journal of remote sensing, 12(1): 152-158
  39. 39.
    Qin X L, Li Z Y and Yi H R. 2005. Extraction method of tree crown using high-resolution satellite image. Remote Sensing Technology and Application, 20(2): 228-232
  40. 40.
    Sun S H. 2000. China's forestry remote sensing in the new century. Satellite Application, 8(2): 43-45, 50
  41. 41.
    Sun X and Tan B X. 2012. A study of estimating method for forest LAI using hyperspectral remote sensing. Journal of Chinese Urban Forestry, 10(4): 1-4
  42. 42.
    Tan B X, Li Z Y, Chen E X and Pang Y. 2005. Preprocessing of EO-1 hyperion hyperspectral data. Remote Sensing Information, (6): 36-41
  43. 43.
    Tan B X, Li Z Y, Chen E X, Pang Y and Lei Y C. 2006. Estimating forest crown closure using Hyperion hyperspectral data. Journal of Beijing Forestry University, 28(3): 95-101
  44. 44.
    Tong Q X, Tang C and Li H G. 1999. A creative action: the second experiment of Teng Chong aerial remote sensing. Geo-Information Science, 1(1): 67-75
  45. 45.
    Tong Q X, Zheng L F, Wang J N, Wang X J, Dong W D, Hu Y M and Dang S X. 1997. Study on imaging spectrometer remote sensing information for wetland vegetation. Journal of Remote Sensing, 1(1): 50-57
  46. 46.
    Wu C Z, Feng Y M, Shu Q T, Li Z Y, Wu H G and Che T T. 2011. Designing and realizing the forestry sub-compartment remote sensing division system based on high-spatial resolution images. Journal of Zhejiang Forestry College, 28(1): 40-45
  47. 47.
    Wu H G. 1995. Application of satellite remote sensing technology in the assessment of forest damage. World Forestry Research, 8(2): 24-29
  48. 48.
    Wu J Y and Ni J. 1995. Spectral characteristics of the pine leaves damaged by pine moth and a model for detecting the damage early. Journal of Remote Sensing, 10(4): 250-258
  49. 49.
    Xiong Y Q and Wu J P. 2007. Tree-crown area detection algorithm for high spatial resolution remote-sensing image. Geography and GEO-Information Science, 23(6): 30-33
  50. 50.
    Xu G H. 1994. Application and prospect on remote sensing and resources and environment information system. Remote Sensing of Environment China, 19(4): 241-246
  51. 51.
    Yang C J, Chen D Q and Wei Y M. 1999. Application of remote sensing and GIS in monitoring and management of forest disease and insect damage. Journal of Catastrophology, 14(1): 6-10
  52. 52.
    Yang X G, Fan W Y and Yu Y. 2010. Leaf chlorophyll content retrieval from hyperspectral remote sensing images. Journal of Northeast Forestry University, 38(6): 123-124, 135
  53. 53.
    Yi H R, Bai L N and Ji P. 1994. Research method for remote sensing image processing using expert system. Forest Research, 7(1): 13-19
  54. 54.
    Yi H R and Ji P. 1998. The methods of evaluating burned area of forest fire by using remote sensing. Remote Sensing Technology and Application, 13(2): 10-14
  55. 55.
    Zeng Q W and Wu H G. 2009. Development of hyperspectral remote sensing application in forest species identification. Forest Resources Management, (5): 109-114
  56. 56.
    Zhang B. 1996. Application of remote sensing technology on research of the wetland in China. Remote Sensing Technology and Application, 11(1): 67-71
  57. 57.
    Zhang G L, Feng Y M, Jia J H, Wu H G and Li Z Y. 2010. Technology study in division of forest resources based on the high-spatial resolution remote sensing images. Remote Sensing Technology and Application, 25(1): 132-137
  58. 58.
    Zhang H. 2002. D-InSAR and PolInSAR: Methods and Applications. Beijing: Graduate School of Chinese Academy of Sciences: 60-114
  59. 59.
    Zhang S Q. 2002. Introduction to China wetland science database. Scientia Geographica Sinica, 22(2): 189
  60. 60.
    Zhang Y X. 2008. Study on Updating Technology of Forest Phase Diagram Based on Spot Data. Beijing: China Forestry Press
  61. 61.
    Zhang Y X and Wang Z X. 2007. Research on Application of Remote Sensing Technology in Forest Resources Inventory. Beijing: China Forestry Press: 16-21
  62. 62.
    Zhao F, Pang Y, Li Z Y, Zhang H Q, Feng W and Liu Q W. 2009. Extraction of individual tree height using a combination of aerial digital camera imagery and LiDAR. Scientia Silvae Sinicae, 45(10): 81-87
  63. 63.
    Zhao X W. 1983. Application of remote sensing in forestry. Forest Science and Technology, (4): 25-27
  64. 64.
    Zhao X W. 1995. Monitoring and Assessment of Remote Sensing on Forest Fire. Beijing: China Forestry Press: 1-11
  65. 65.
    Zhou G Y, Xiong T, Zhang W J and Yang J. 2009. Forest height measurements based on polarimetric SAR interferometry. Journal of Tsinghua University (Science and Technology), 49(4): 510-513
  66. 66.
    Zhu Z D. 1984. The principles and methods for compilling the map of desertification of China. Journal of Desert Research, 4(1): 3-15
  67. 67.
    Zhu Z D. 1989. Desertification and Its Control in China. Beijing: Science Press: 18-86
  68. 68.
    Zhu Z D and Liu S. 1984. The concept of desertification and the differentiation of its development. Journal of Desert Research, 4(3): 2-8

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