Volume 43 Issue 6
Dec.  2023
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(6): 1176-1193 Open Access
TIAN Zhenpeng, ZHOU Wei, YUAN Jingyi, LIU Xiaoqiang, YE Su, POUDEL Krishna, HIMES Austin, RENNINGER Heidi, WANG Jiaxin, MA Qin. Forest Canopy Height Mapping Based on Multi-source Remote Sensing Data (in Chinese). Chinese Journal of Space Science, 2023, 43(6): 1176-1193 doi: 10.11728/cjss2023.06.2023-0074
Citation: TIAN Zhenpeng, ZHOU Wei, YUAN Jingyi, LIU Xiaoqiang, YE Su, POUDEL Krishna, HIMES Austin, RENNINGER Heidi, WANG Jiaxin, MA Qin. Forest Canopy Height Mapping Based on Multi-source Remote Sensing Data (in Chinese). Chinese Journal of Space Science, 2023, 43(6): 1176-1193 doi: 10.11728/cjss2023.06.2023-0074
shu

Forest Canopy Height Mapping Based on Multi-source Remote Sensing Data

doi: 10.11728/cjss2023.06.2023-0074 cstr: 32142.14.cjss2023.06.2023-0074
  • 1.

    School of Geographical Sciences, Nanjing Normal University, Nanjing 210023

  • 2.

    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023

  • 3.

    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023

  • 4.

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093

  • 5.

    School of Environment and Resources, Zhejiang University, Hangzhou 310058

  • 6.

    Department of Forestry, Mississippi State University, Starkville 39762

  • Received Date: 2023-07-17
  • Rev Recd Date: 2023-10-27
    • Available Online: 2023-12-04
    Abstract
  • Accurate estimation of spatially continuous forest canopy height is crucial for quantifying forest carbon stocks, understanding forest ecosystems, and making forest management and restoration policies. Spaceborne Light Detection and Ranging (LiDAR) can measure forest canopy height over laser footprints at semi-global the coverage, which provides a promising data source for estimating forest canopy height at national to global scales. This study used the random forest regression method to map forest canopy height by fusing Ice, Cloud and land Elevation Satellite-2 (ICESat-2) Advanced Topographic Laser Altimeter System (ATLAS) measurements and Landsat-8 images, combined with terrain and climatic features, and other data to generate forest canopy height products of the maximum (Hmax) and mean height (Hmean) values at 30 meter resolution across Mississippi State of America in 2020. The results show that the mean and standard deviation of Hmax in forest area is 24.14 m and 4.24 m respectively. For the Hmean, the mean and standard deviation of Hmean in forest area were 12.04 m and 2.59 m respectively. The estimated Hmax and Hmean across Mississippi agree well with airborne measurements (Hmax: $ {R}^{2} $ = 0.486, $ {H}_{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}} $ = 4.532 m; Hmean: $ {R}^{2} $ = 0.467, $ {H}_{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}} $ = 2.848 m). In this study, the difference and ratio of the maximum and average values of canopy height were used to reflect the vertical structure complexity of the forest canopy. The differences of different geographical divisions, forest types, planted forests and natural forests were compared, and it was found that the complexity of loess hilly areas, deciduous forests, wetland forests and natural forests in the study area was higher. In addition, the canopy height mapping scheme proposed in this study for non-mountain plantations is of guiding significance for forest management, species diversity conservation and “carbon neutrality” assessment in the in the Yangtze River Delta and other areas dominated by non-mountain planted forest of China.

     

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