A Method for Estimating Forest Aboveground Biomass at the Plot Scale Combining the Horizontal Distribution Model of Biomass and Sampling Technique

Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development.

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A Novel Approach to Characterizing Crown Vertical Profile Shapes Using Terrestrial Laser Scanning (TLS)

Wang

Sun

Jia

et al. 2023

Remote Sensing

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Crown vertical profiles (CVP) play an essential role in stand biomass and forest fire prediction. Traditionally, due to measurement difficulties, CVP models developed based on a small number of individual trees are not convincing. Terrestrial laser scanning (TLS) provides new insights for researching trees’ CVPs. However, there is a limited understanding of the ability to accurately describe CVPs with TLS. In this study, we propose a new approach to automatically extract the crown radius (CR) at different heights and confirm the correctness and effectiveness of the proposed approach with field measurement data from 30 destructively harvested sample trees. We then applied the approach to extract the CR from 283 trees in 6 sample plots to develop a two-level nonlinear mixed-effects (NLME) model for the CVP. The results of the study showed that the average extraction accuracy of the CR when the proposed approach was applied was 90.12%, with differences in the extraction accuracies at different relative depths into the crown (RDINC) ranges. The TLS-based extracted CR strongly correlated with the field-measured CR, with an R2 of 0.93. Compared with the base model, the two-level NLME model has significantly improved the prediction accuracy, with Ra2 increasing by 13.8% and RMSE decreasing by 23.46%. All our research has demonstrated that TLS has great potential for accurately extracting CRs, which would provide a novel way to nondestructively measure the crown structure. Moreover, our research lays the foundation for the future development of CVP models using TLS at a regional scale.

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A Method for Estimating Forest Aboveground Biomass at the Plot Scale Combining the Horizontal Distribution Model of Biomass and Sampling Technique

Cited by 3 publications

References 45 publications

Characterising spatial effects of individual tree and component biomass for three typical tree species in Yunnan, China

Characterising spatial effects of individual tree and component biomass for three typical tree species in Yunnan, China

Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China

A Novel Approach to Characterizing Crown Vertical Profile Shapes Using Terrestrial Laser Scanning (TLS)

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