Carbon Emission Prediction Following Pinus koraiensis Plantation Surface Fuel Combustion Based on Carbon Consumption Analysis

The accurate measurement of surface fire carbon emissions is critical for assessing their impact on carbon sinks and role in climate change. This study aims to investigate the relationships between surface fire behaviour characteristics and carbon consumption for Pinus koraiensis plantation forests and construct a carbon consumption prediction model. A total of 288 combustion experiments were conducted on a laboratory burning bed using varying fuel loads, moisture contents, and slope conditions, with measurements taken of surface fire behaviour characteristics and the carbon content of combustion ash. The Byram fireline intensity model was reintegrated to build a predictive model for fuel combustion carbon consumption, and the model parameters were adjusted based on the results of the combustion experiments. The direct use of the Byram fireline intensity model parameters predicted surface fire carbon consumption in Pinus koraiensis plantation forests with significant errors (R2 = 0.75; MAE = 0.197 kg m−2; MRE = 66.76%). After the parameters were modified using the combustion experiment data, the new model yielded R2 = 0.75, MAE = 0.087 kg m−2, and MRE = 28.28%. This study significantly improved the accuracy of the new model in predicting the carbon consumption of surface fuel combustion in Pinus koraiensis plantation forests.

Authors

Institutions

Publication Details

Journal
Forests
Published
2025-04-24
DOI
https://doi.org/10.3390/f16050726
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Carbon Emission Prediction Following Pinus koraiensis Plantation Surface Fuel Combustion Based on Carbon Consumption Analysis

Jibin Ning, Lixuan Wang, Guang Yang, Daotong Geng et al.
Forests
Fire effects on ecosystems
article

Carbon Emission Prediction Following Pinus koraiensis Plantation Surface Fuel Combustion Based on Carbon Consumption Analysis

Jibin Ning, Lixuan Wang, Guang Yang, Daotong Geng, Hongzhou Yu, Shangjiong Ma
article en

Abstract

The accurate measurement of surface fire carbon emissions is critical for assessing their impact on carbon sinks and role in climate change. This study aims to investigate the relationships between surface fire behaviour characteristics and carbon consumption for Pinus koraiensis plantation forests and construct a carbon consumption prediction model. A total of 288 combustion experiments were conducted on a laboratory burning bed using varying fuel loads, moisture contents, and slope conditions, with measurements taken of surface fire behaviour characteristics and the carbon content of combustion ash. The Byram fireline intensity model was reintegrated to build a predictive model for fuel combustion carbon consumption, and the model parameters were adjusted based on the results of the combustion experiments. The direct use of the Byram fireline intensity model parameters predicted surface fire carbon consumption in Pinus koraiensis plantation forests with significant errors (R2 = 0.75; MAE = 0.197 kg m−2; MRE = 66.76%). After the parameters were modified using the combustion experiment data, the new model yielded R2 = 0.75, MAE = 0.087 kg m−2, and MRE = 28.28%. This study significantly improved the accuracy of the new model in predicting the carbon consumption of surface fuel combustion in Pinus koraiensis plantation forests.

ForestsVol. 16(5)
Northeast Forestry University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 91%
Fire effects on ecosystems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.