干旱区研究

干旱区研究 >

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

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

植物生态

深度学习方法下GEDI数据的天然云杉林地上生物量反演

  • 孙丹阳 ,
  • 魏建新 ,
  • 杨辽 ,
  • 王杰 ,
  • 唐宇琪 ,
  • 巴比尔江·迪力夏提
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  • 1.新疆大学地理与遥感科学学院，新疆 乌鲁木齐 830017
    2.新疆激光雷达应用工程技术研究中心，新疆 乌鲁木齐 830002
    3.新疆维吾尔自治区自然资源信息中心，新疆 乌鲁木齐 830002
    4.中国科学院新疆生态与地理研究所，荒漠与绿洲生态国家重点实验室，新疆 乌鲁木齐 830011
    5.西华师范大学地理科学学院，四川 南充 637002
孙丹阳（1998-），女，硕士研究生，研究方向为地信工程. E-mail: 1063209224@qq.com

收稿日期: 2023-04-01

  修回日期: 2023-05-16

  网络出版日期: 2023-09-28

基金资助

新疆维吾尔自治区财政拨款项目(65000022P00790610001Q)

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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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摘要

森林作为陆地最大碳库，对人类的生活与发展至关重要，精准掌握森林资源动态变化并对其进行现代化可持续发展已成为当下研究热点。本文以天山山脉的天然云杉林为研究对象，利用地面实测数据、直升机机载激光雷达点云数据以及全球生态系统动态调查激光雷达（Global Ecosystem Dynamics Investigation，GEDI）数据，构建多源融合数据框架，通过使用AutoKeras框架下的深度学习算法，实现GEDI数据的多个相对高度百分位数（Relative Height Percentile，RH）与其光斑内地上生物量的回归模型预测，验证GEDI数据在较大范围的地上生物量反演方面的可行性，主要结论如下：（1） GEDI数据用于森林地上生物量估测研究具有较高可行性，通过自动化深度学习算法，训练集、验证集、整体数据的决定系数（Coefficient of Determination，R2）分别为0.69、0.63和0.67，平均绝对误差（Mean Absolute Error，MAE）分为3.73 mg·hm-2、4.22 mg·hm-2和3.89 mg·hm-2，具有较高的预测精度。（2） 直升机激光雷达作为GEDI数据估算地上生物量的中间技术，整个研究区内的单木识别准确率高于0.75。最终本次研究通过多模态数据融合，定量化描述研究区单木基础结构参数的同时，验证GEDI数据在获取森林地上生物量方面的潜力，也为相近区域大面积的森林碳源汇、生物量、蓄积量估算、森林管理与经营、生物多样性保护等多个项目研究提供理论基础，具有一定的指导意义和基础数据支撑作用。

关键词： 天然云杉林; GEDI; LiDAR; 地上生物量; 深度学习

本文引用格式

孙丹阳 , 魏建新 , 杨辽 , 王杰 , 唐宇琪 , 巴比尔江·迪力夏提 . 深度学习方法下GEDI数据的天然云杉林地上生物量反演[J]. 干旱区研究, 2023 , 40(9) : 1472 -1483 . DOI: 10.13866/j.azr.2023.09.11

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

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