Xu Li, Yu Jinge, Shu Qingtai, Luo Shaolong, Zhou Wenwu, Duan Dandan
Faculty of College of Soil and Water Conservation, Southwest Forestry University, Kunming, China.
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Front Plant Sci. 2024 Dec 11;15:1428268. doi: 10.3389/fpls.2024.1428268. eCollection 2024.
Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is of great significance for forest quality assessment and carbon cycle. To solve the problem of discontinuous data of GEDI footprints, this study mapped different echo indexes in the footprints to the surface by inverse distance weighted interpolation method, and verified the influence of different number of footprints on the interpolation results. Random forest algorithm was chosen to estimate the spruce-fir biomass combined with the parameters provided by GEDI and 138 spruce-fir sample plots in Shangri-La. The results show that: (1) By extracting different numbers of GEDI footprints and visualize it, the study revealed that a higher number of footprints correlates with a denser distribution and a more pronounced stripe phenomenon. (2) The prediction accuracy improves as the number of GEDI footprints decreases. The group with the highest R, lowest RMSE and lowest MAE was the footprint extracted every 100 shots, and the footprint extracted every 10 shots had the worst prediction effect. (3) The biomass of spruce-fir inverted by random forest ranged from 51.33 t/hm to 179.83 t/hm, with an average of 101.98 t/hm. The total value was 3035.29 × 10 t/hm. This study shows that the number and distribution of GEDI footprints will have a certain impact on the interpolation mapping to the surface information and presents a methodological reference for selecting the appropriate number of GEDI footprints to derive various vertical structure parameters of forest ecosystems.
基于GEDI星载激光雷达数据估算区域尺度森林生物量对森林质量评估和碳循环具有重要意义。为解决GEDI足迹数据不连续的问题，本研究采用反距离加权插值法将足迹中的不同回波指数映射到地表，并验证了不同数量足迹对插值结果的影响。结合GEDI提供的参数和香格里拉138个云杉-冷杉样地，选择随机森林算法估算云杉-冷杉生物量。结果表明：(1) 通过提取不同数量的GEDI足迹并进行可视化，研究发现足迹数量越多，分布越密集，条带现象越明显。(2) 随着GEDI足迹数量的减少，预测精度提高。相关系数R最高、均方根误差RMSE最低和平均绝对误差MAE最低的组是每100次发射提取的足迹，每10次发射提取的足迹预测效果最差。(3) 随机森林反演的云杉-冷杉生物量范围为51.33 t/hm至179.83 t/hm，平均为101.98 t/hm。总量为3035.29× $10^4$ t/hm。本研究表明，GEDI足迹的数量和分布对地表信息的插值制图会产生一定影响，并为选择合适数量的GEDI足迹以推导森林生态系统的各种垂直结构参数提供了方法参考。
