Affiliation:
1. College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
2. Key Laboratory of Low-Carbon Green Agriculture in Northwestern China, Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Northwest A & F University, Yangling 712100, China
Abstract
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.
Funder
National Natural Science Foundation of China
State Key Laboratory Foundation of China
Reference82 articles.
1. Singh, A., Kushwaha, S.K.P., Nandy, S., Padalia, H., Ghosh, S., Srivastava, A., and Kumari, N. (2023). Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning. Remote Sens., 15.
2. Forest Floor Fluxes Drive Differences in the Carbon Balance of Contrasting Boreal Forest Stands;Chi;Agric. For. Meteorol.,2021
3. Mineral Soil Carbon Fluxes in Forests and Implications for Carbon Balance Assessments;Buchholz;GCB Bioenergy,2014
4. (2023, March 20). Primary Productivity of the Biosphere|SpringerLink. Available online: https://link.springer.com/book/10.1007/978-3-642-80913-2.
5. Predicting the Responses of Forest Distribution and Aboveground Biomass to Climate Change under RCP Scenarios in Southern China;Dai;Glob. Chang. Biol.,2016
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献