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Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data

  • role： First author第一作者
  • Affiliation：

    Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

  • Email：liuhao_njfu@hotmail.com
  • Introduction：刘浩，1994年生，男，硕士研究生，研究方向为激光雷达森林参数反演。E-mail：liuhao_njfu@hotmail.com
Hao LIU,  
  • Affiliation：

    Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

Zhengnan ZHANG,  
  • role： Corresponding author通信作者
  • Affiliation：

    Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

  • Email：lincao@njfu.edu.cn
  • Introduction：曹林，1983年生，男，副教授，研究方向为遥感森林资源监测。E-mail：lincao@njfu.edu.cn
Lin CAO*

Resümee

China is a country with the largest plantation area in the world. Timely, quantitatively, and accurately acquiring planted forest stand characteristics is the key to monitoring and managing forest plantation, which contributes to the global carbon cycle. This study was conducted at a north subtropical coastal plain forest plantation to estimate forest stand characteristics using light detection and ranging (LiDAR) point clouds combined with 55 field-measured data. First, Canopy Height Distribution (CHD) and Foliage Profile (FP) were extracted and used to obtain Weibull parameters by fitting a Weibull density function to the profiles (Suite 1). Second, Height-Related Metrics (HRM) and Density-Related Metrics (DRM) (Suite 2) were directly extracted from the LiDAR point clouds. Finally, multiple regression models were constructed to predict the stand characteristics (i.e., stand density, diameter of breast height (DBH), basal area, Lorey’s tree height, volume, and aboveground biomass) on the basis of the field-measured data and two suites of metrics. Results showed that (1) the accuracies of the forest stand characteristics that use point cloud (Suite 2) and canopy vertical profile metrics simultaneously (Suite 1) were relatively higher than that only using point cloud metrics (Suite 2) ( ΔAdjusted R2=0—0.13, ΔrRMSE=0.08%—3.65%). (2) For all the forest stand characteristic estimations, the models of Lorey’s tree height (Adjusted R2=0.85, rRMSE=7.66%) and volume (Adjusted R2=0.84, rRMSE=14.27%) demonstrated the highest accuracies, followed by the aboveground biomass (Adjusted R2=0.78, rRMSE=14.15%), basal area (Adjusted R2=0.73, rRMSE=14.70%), and mean DBH (Adjusted R2=0.64, rRMSE=15.05%). Stand density exhibited the lowest accuracies (Adjusted R2=0.58, rRMSE=26.16%). (3) The Weibull function was appropriate for describing the vertical stand canopy structure of planted forests. The Weibull metrics can be favorable in effectively improving the estimation accuracy of the forest stand characteristics. This study provided an effective approach in estimating the forest stand characteristics that use the LiDAR point clouds by combining vertical profile and point cloud metrics. This study also proved that the vertical profile metrics can help in increasing the accuracy of the predictive models and provide the mechanism for interpreting the forest stand vertical structure.

Schlüsselwort

lidar;canopy vertical structure profiles;Weibull;foliage profile;point clouds metrics

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