New approach to calculating tree height at the regional scale

Congrong Li,Jindi Wang,Jinling Song

doi:10.1186/s40663-021-00300-4

Abstract

BackgroundDetermining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.ResultsThe semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.ConclusionsThe semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

Highlights

Tree height is one of the main forest vertical structural parameters, and it can reflect the overall state of the forest structure
geometric-optical mutual shadowing (GOMS) model and inversion strategy The GOMS model was constructed based on the LiStrahler geometric-optical model (Li and Strahler 1992), which assumes that the reflectance of a pixel can be modeled as a sum of the reflectance of its individual scene components weighted by their respective areas within the pixel (Li and Strahler 1985) and that the vegetation canopy bidirectional reflectance distribution function (BRDF) characteristics at the pixel scale can be explained by the geometric-optical principle
The results showed that the field-measured tree height and the extracted single-tree height based on the canopy height model (CHM) data have a high correlation, with an R2 equals value of 0.72 (Fig. 6)

Summary

Introduction

Tree height is one of the main forest vertical structural parameters, and it can reflect the overall state of the forest structure. Richard (Carmean and Lenthall 1989; Payandeh 1974; Payandeh and Wang 1994a), Logistic (Chen et al 1998; Nigh and Sit 1996; Thrower and Goudie 1992; Wang and Klinka 1995), and Weibull (Payandeh and Wang 1994b; Yang et al 1978) are the most frequently used statistical models to estimate tree height These statistical models are primarily based on field measurements, and obtaining the stand age and DBH at the regional scale is unfeasible. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale This method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. We combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale

Methods

Results

Discussion

Conclusion