Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data

Yoshio Awaya,Tomoaki Takahashi

doi:10.3390/rs9060572

Yoshio Awaya, Tomoaki Takahashi

Open Access

https://doi.org/10.3390/rs9060572

Copy DOI

Abstract

Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV.

Highlights

Biomass and stem volume (SV) are important variables for forestry and carbon balance studies.The biomass and carbon stocks in forests are important indicators of their productive capacity, energy potential, and capacity to sequester carbon [1]
The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than total dry biomass (TDB) for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV
This study revealed that TDB would be a better measure than SV for conifer biomass mapping, and SV would be better than TDB for deciduous broadleaf biomass mapping owing to their higher model correlation coefficients and validation results compared to another variable (Figures 5 and 6)

Summary

Introduction

Biomass and stem volume (SV) are important variables for forestry and carbon balance studies. The biomass and carbon stocks in forests are important indicators of their productive capacity, energy potential, and capacity to sequester carbon [1]. Biomass is a pool of atmospheric carbon fixed by plants and ranges widely based on tree size in large areas. Stem volume (SV) is basic piece of information for informing lumber production in forest management. The stem volume of a tree is the principal commercial product of forests as the stem contains a large proportion of the biomass of a tree [2].

Results

Discussion

Conclusion