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Forest aboveground biomass estimation combining ICESat-2 and GEDI spaceborne LiDAR data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：mengge@aircas.ac.cn
  • Introduction：孟鸽，研究方向为植被生态遥感。E-mail： mengge@aircas.ac.cn
MENG Ge12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhaodan@aircas.ac.cn
  • Introduction：赵旦，研究方向为森林生态系统遥感。E-mail： zhaodan@aircas.ac.cn
ZHAO Dan12*,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

XU Cong12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

CHEN Junhua12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

LI Xiuwen12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

ZHENG Zhaoju1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Insititute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

ZENG Yuan12

Resümee

Forest Aboveground Biomass (AGB) plays an important role in the study of carbon cycle and global change. Spaceborne LiDAR can provide information about forest vertical structures that is advantageous in AGB estimation, among which ICESat-2 and GEDI are the latest available spaceborne data. In this study, we investigated the applicability of ICESat-2 and GEDI for forest AGB estimation at regional scale, and analyzed the effect of data fusion of ICESat-2 and GEDI to find an optimal method to map the spatial distribution of forest AGB accurately in Zhejiang Province.First, we built footprint-level forest AGB estimation models by stepwise regression in the typical study area of Gutian Mountain based on ICEsat-2 and GEDI spaceborne LiDAR data, respectively. Then, combined with MODIS data and ASTER GDEM terrain information, forest AGB estimation models with spatial continuity at 250m pixel scale for different forest types were constructed by Random Forest algorithm throughout Zhejiang Province. Estimation results were validated using 40 forest AGB field plots. Finally, by comparing validation results of AGB estimation based on ICESat-2 or GEDI solely and the combination of the two spaceborne LiDAR data, the optimal method of forest AGB scaling was selected and the spatial distribution of forest AGB of the year 2020 was mapped in Zhejiang Province.The accuracy of segment-level forest AGB estimation based on ICESat-2 (R2=0.7057, RMSE=0.3571 ln(t/ha)) outmatches footprint-level forest AGB estimation based on GEDI (R2=0.5186, RMSE=0.2805 ln(t/ha)) in the typical study area of Gutian Mountain. Validation accuracy of forest AGB estimation result based on ICEsat-2 (R2=0.59, RMSE=31.2525 t/ha) is superior to GEDI (R2=0.4113, RMSE=39.2652 t/ha) in Zhejiang Province. The difference of forest AGB estimation performance between ICESat-2 and GEDI is mainly related to elevation, validation accuracy based on GEDI is higher when filtering footprints that are acquired in high elevation areas with an elevation threshold of 600m (R2=0.5387, RMSE=25.4017 t/ha). Combining ICESat-2 and GEDI data (elevation ≤ 600 m) to build scaling model is the optimal method to estimate forest AGB in Zhejiang Province (R2=0.678, RMSE=27.3592 t/ha).We have obtained a reliable estimation of forest AGB in Zhejiang Province based on ICESat-2 and GEDI data, which is a significant practice of regional scale forest AGB estimation. Our study can provide an effective method for forest carbon dynamic and sequestration potential monitoring using spaceborne LiDAR data.

Schlüsselwort

remote sensing;forest aboveground biomass;ICESat-2;GEDI;stepwise regression;Random Forest;scaling extrapolation;Zhejiang Province

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