Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan
文献类型：期刊论文
| 作者 | Shahzad, Fahad4; Mehmood, Kaleem6,7; Anees, Shoaib Ahmad8; Adnan, Muhammad1; Haidar, Ijlal7; Jabbarov, Umarbek2; Yaxshimuratov, Murodjon3; Oliveira, Manuela5 |
| 刊名 | CARBON BALANCE AND MANAGEMENT
 |
| 出版日期 | 2026-02-06 |
| 卷号 | 21期号:1页码:54 |
| 关键词 | Forest fire Greenhouse gas emissions Remote sensing Burn indices Machine learning Ecosystem resilience |
| ISSN号 | 1750-0680 |
| DOI | 10.1186/s13021-026-00410-y |
| 产权排序 | 6 |
| 文献子类 | Article |
| 英文摘要 | This study investigates the spatial variability of forest fire intensity, burn indices, ecosystem productivity, and Greenhouse Gas (GHG) emissions in Pakistan from 2001 to 2023. Using satellite-derived burn indices such as SAVI, LST, NMDI, LSWI, NBR, and MSAVI2, the study examines the relationship between forest fires and net primary productivity (NPP) across diverse ecological regions. The analysis reveals that northern Pakistan, particularly Khyber Pakhtunkhwa and Gilgit-Baltistan, experiences high fire intensity, resulting in significant reductions in NPP and increased emissions of COx, NOx, and CH4. Central and southern Pakistan, including the arid regions of Balochistan and Sindh, exhibit lower fire intensity but remain vulnerable due to climate-driven dry conditions. The study also applies the Delta NPP/Delta Burn approach to evaluate how changes in burn indices correspond to shifts in NPP, revealing that small increases in fire intensity can lead to substantial ecosystem productivity loss. Additionally, a comparative analysis of Random Forest (RF) and XGBoost machine learning models for fire prediction found RF to be the more accurate model, achieving 88.0% accuracy and a 93.8% AUC score. These findings underscore the importance of developing region-specific fire management strategies to mitigate the ecological and environmental impacts of wildfires. The study highlights the critical need for improved fire prediction, early warning systems, and long-term monitoring of post-fire ecosystem recovery. By drawing comparisons with global research, this study contributes to understanding the broader implications of forest fires on carbon dynamics and ecosystem productivity, providing valuable insights for future fire management policies in Pakistan. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE-CHANGE ; BURN SEVERITY ; WILDFIRE ; CARBON ; MODEL ; SATELLITE ; MODIS ; AREA |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001712377600001 |
| 出版者 | BMC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221242]  |
| 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.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Mamun Univ, Dept Accounting & Business Adm, Khiva, Uzbekistan; 3.Urgench State Univ, Dept Chem, Urgench, Uzbekistan 4.Beijing Forestry Univ, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China; 5.Univ Evora, Ctr Res Math & its Applicat, Dept Math, Evora, Portugal; 6.Beijing Forestry Univ, Key Lab Silviculture & Conservat, Minist Educ, Beijing 100083, Peoples R China; 7.Univ Swat, Inst Forest Sci, Main Campus Charbagh, Swat 19120, Pakistan; 8.Univ Agr, Dept Forestry, Dera Ismail Khan 29050, Pakistan; |
| 推荐引用方式 GB/T 7714 | Shahzad, Fahad,Mehmood, Kaleem,Anees, Shoaib Ahmad,et al. Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan[J]. CARBON BALANCE AND MANAGEMENT,2026,21(1):54. |
| APA | Shahzad, Fahad.,Mehmood, Kaleem.,Anees, Shoaib Ahmad.,Adnan, Muhammad.,Haidar, Ijlal.,...&Oliveira, Manuela.(2026).Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan.CARBON BALANCE AND MANAGEMENT,21(1),54. |
| MLA | Shahzad, Fahad,et al."Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan".CARBON BALANCE AND MANAGEMENT 21.1(2026):54. |
入库方式： OAI收割
来源：地理科学与资源研究所
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