Chin J Plan Ecolo ›› 2015, Vol. 39 ›› Issue (4): 309-321.DOI: 10.17521/cjpe.2015.0030

Special Issue: 遥感生态学

• Orginal Article •     Next Articles

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

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).


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