Understanding green house gases emission dynamics from forest fires in Thailand using predictive models
文献类型：期刊论文
| 作者 | Shahzad, Fahad2,5; Mehmood, Kaleem2,6; Anees, Shoaib Ahmad1; Adnan, Muhammad7; Hussain, Khadim6; Khan, Waseem Razzaq8; Shah, Munawar9; Jamjareegulgarn, Punyawi10; Oliveira, Manuela3,4; Borges, Jose G.11 |
| 刊名 | GLOBAL AND PLANETARY CHANGE
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 257页码:105236 |
| 关键词 | Forest fires Greenhouse gas emissions Machine learning Net primary productivity Fire management strategies |
| ISSN号 | 0921-8181 |
| DOI | 10.1016/j.gloplacha.2025.105236 |
| 产权排序 | 7 |
| 文献子类 | Article |
| 英文摘要 | Forest fires are a major driver of carbon emissions, particularly in tropical regions where climate variability and land use practices intensify their frequency and impact. This study investigates the spatiotemporal trends and emission dynamics of forest fires across Thailand's three dominant vegetation types- Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Grassland over three climatic seasons (Dry, Hot, and Wet) in the period 2001-2023. Using the Mann-Kendall trend test and Sen's Slope estimator, we observed significant declines in burnt area during the Dry season in EBF and Grasslands, with no consistent trend in DBF. Fire-vegetation interactions revealed seasonally specific effects: positive correlations between fire count and Net Primary Productivity (NPP) were detected in the Wet and the hot seasons in the case of DBF and Grasslands, respectively. Emission analysis showed that CO2 was the dominant greenhouse gas released, with the Dry season contributing to most emissions, although Hot season emissions have increased over time. Machine learning models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) explained over 78 % of the variance in CO2 emissions on test data (R-2 = 0.79 for RF, 0.78 for XGBoost), despite higher Root Mean Square Error (RMSE) values (similar to 550) on unseen data. The Shapley Additive Explanations (SHAP) analysis identified wind components and solar radiation as key predictive variables. Central, Northeastern, and Northern Thailand emerged as emission hotspots. These findings improve our understanding of emission dynamics from tropical fires and underscore the need for region-specific mitigation strategies to inform carbon inventories and climate policy. |
| URL标识 | 查看原文 |
| WOS关键词 | SPATIAL-DISTRIBUTION ; CARBON EMISSIONS ; HIGH-RESOLUTION ; SOUTHEAST-ASIA ; BIOMASS ; DENSITY ; CHINA ; PRODUCTIVITY ; REGRESSION ; IMPACT |
| WOS研究方向 | Physical Geography ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001643143600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219668]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Mehmood, Kaleem; Oliveira, Manuela |
| 作者单位 | 1.Univ Agr, Dept Forestry, Dera Ismail Khan 29050, Pakistan; 2.Beijing Forestry Univ, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China; 3.Univ Evora, Dept Math, Evora, Portugal; 4.Univ Evora, Ctr Res Math & Its Applicat, Evora, Portugal; 5.Beijing Forestry Univ, State Forestry & Grassland Adm Key Lab Forest Reso, Beijing 100083, Peoples R China; 6.Univ Swat, Inst Forest Sci, Main Campus Charbagh, Swat 19120, Pakistan; 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 8.Univ Putra Malaysia, Fac Forestry & Environm, Dept Forestry Sci & Biodivers, Serdang 43400, Malaysia; 9.Inst Space Technol, Dept Space Sci, Space Educ & GNSS Lab, NCGSA, Islamabad 44000, Pakistan; 10.King Mongkuts Inst Technol Ladkrabang, Prince Chumphon Campus, Chumphon 86160, Thailand; |
| 推荐引用方式 GB/T 7714 | Shahzad, Fahad,Mehmood, Kaleem,Anees, Shoaib Ahmad,et al. Understanding green house gases emission dynamics from forest fires in Thailand using predictive models[J]. GLOBAL AND PLANETARY CHANGE,2026,257:105236. |
| APA | Shahzad, Fahad.,Mehmood, Kaleem.,Anees, Shoaib Ahmad.,Adnan, Muhammad.,Hussain, Khadim.,...&Borges, Jose G..(2026).Understanding green house gases emission dynamics from forest fires in Thailand using predictive models.GLOBAL AND PLANETARY CHANGE,257,105236. |
| MLA | Shahzad, Fahad,et al."Understanding green house gases emission dynamics from forest fires in Thailand using predictive models".GLOBAL AND PLANETARY CHANGE 257(2026):105236. |
入库方式： OAI收割
来源：地理科学与资源研究所
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