干旱区研究

Arid Zone Research >

2023 , Vol. 40 >Issue 9: 1472 - 1483

DOI: https://doi.org/10.13866/j.azr.2023.09.11

Plant Ecology

Study on using deep learning method to retrieve the biomass of natural Picea forest from GEDI data

  • Danyang SUN ,
  • Jianxin WEI ,
  • Liao YANG ,
  • Jie WANG ,
  • Yuqi TANG ,
  • DILIXIATI Babierjiang
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  • 1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, Xinjiang, China
    2. Xinjiang LiDAR Application Engineering Technology Research Center, Urumqi 830002, Xinjiang, China
    3. Xinjiang Uygur Autonomous Regions Natural Resources Information Center, Urumqi 830002, Xinjiang, China
    4. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    5. School of Geographical Sciences, China West Normal University, Nanchong 637002, Sichuan, China

Received date: 2023-04-01

  Revised date: 2023-05-16

  Online published: 2023-09-28

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Abstract

As the largest carbon reservoir on land, forests play a crucial role in human life and development. Understanding the dynamic changes in forest resources and modernizing their sustainable development is currently a significant research focus. This study focuses on natural Picea forests in the Tianshan Mountains and uses ground measurement data, helicopter airborne LiDAR point cloud data, and Global Ecosystem Dynamics Investigation (GEDI) data to construct a multisource fusion data framework. By utilizing deep learning algorithms within the AutoKeras framework, the study aims to predict the regression model of multiple relative height quantiles of GEDI data and their aboveground biomass in the study area, thereby validating the feasibility of GEDI data for large-scale aboveground biomass retrieval. The main conclusions are as follows: (1) GEDI data are highly feasible for estimating forest aboveground biomass. Through automated deep learning algorithms and training and verification sets, the overall data achieve a coefficient of determination (R2) of 0.69, 0.63, and 0.67, respectively, along with a mean absolute error of 3.73 mg·hm-2, 4.22 mg·hm-2, and 3.89 mg·hm-2, demonstrating high prediction accuracy. (2) Helicopter LiDAR, an intermediate technology for estimating aboveground biomass using GEDI data, exhibits a single tree recognition accuracy of over 0.75 across the study area. The study successfully utilizes multimodal data fusion to quantitatively describe the structural parameters of the single tree foundation in the study area while verifying the potential of GEDI data for obtaining forest aboveground biomass. Moreover, the study provides a theoretical basis for estimating carbon sources and sinks, biomass, stock, forest management, biodiversity protection, and other projects in similar areas, offering essential guidance, and fundamental data support.

Key words： natural Picea forest; GEDI; LiDAR; aboveground biomass; deep learning

Cite this article

Danyang SUN , Jianxin WEI , Liao YANG , Jie WANG , Yuqi TANG , DILIXIATI Babierjiang . Study on using deep learning method to retrieve the biomass of natural Picea forest from GEDI data[J]. Arid Zone Research, 2023 , 40(9) : 1472 -1483 . DOI: 10.13866/j.azr.2023.09.11

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