植物生态学报 ›› 2015, Vol. 39 ›› Issue (4): 309-321.DOI: 10.17521/cjpe.2015.0030

• •    下一篇

基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算

徐婷, 曹林, 申鑫, 佘光辉*()   

  1. 南京林业大学南方现代林业协同创新中心, 南京 210037
  • 收稿日期:2014-10-11 接受日期:2015-02-15 出版日期:2015-04-01 发布日期:2015-04-21
  • 通讯作者: 佘光辉
  • 作者简介:

    # 共同第一作者

  • 基金资助:
    国家自然科学基金青年科学基金项目(31400492)、“863”国家高技术研究发展计划子课题(2013AA12A302)和江苏高校优势学科建设工程资助项目(PAPD)

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

XU Ting, CAO Lin, SHEN Xin, SHE Guang-Hui*()   

  1. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
  • Received:2014-10-11 Accepted:2015-02-15 Online:2015-04-01 Published:2015-04-21
  • Contact: Guang-Hui SHE
  • About author:

    # Co-first authors

摘要:

快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R2为0.88; 地下生物量: R2为0.92)优于OLI生物量估算模型(地上生物量: R2为0.73; 地下生物量: R2为0.81)和LiDAR生物量估算模型(地上生物量: R2为0.86; 地下生物量: R2为0.83)。

关键词: 森林生物量估算, 亚热带森林, OLI多光谱数据, 机载小光斑激光雷达数据, 逐步回归

Abstract: <i>Aims</i>

Estimating forest biomass at regional scale with high accuracy is among the pressing challenges in evaluating ecosystem functions and characteristics (e.g., carbon storage).

<i>Methods</i>

This study is based on airborne small-footprint discrete-return LiDAR data, Landsat 8 OLI multispectral data, and in situ measurements from 55 forest plots in Yushan, Changshu, Jiangsu Province. A total of 87 independent variables (53 from OLI metrics and 34 from LiDAR metrics) were used in the Pearson correlation analysis for estimating aboveground (WA) and belowground (WB) biomass by identifying the significant independent variables. Three independent models by using OLI, LiDAR and their combinations (i.e., the combo model) were established through stepwise regression analysis.

<i>Important findings</i>

The correlation coefficients of determination (R2) for WA and WB models are greater than 0.4. The R2 seemed much higher when the estimations were type-specific (e.g., coniferous, broad-leaf and mixed forest), with R2 of >0.67. The Combo model by forest type yielded an R2 of 0.88 for WA and 0.92 for WB, while the OLI-based model had R2 of 0.73 and 0.81 for WA and WB, respectively. The LiDAR-based model has R2 of 0.86 and 0.83 for WA and WB, respectively.

Key words: estimate of forest biomass, subtropical forest, OLI multispectral data, air borne small-footprint LiDAR data, stepwise regression