气候变化领域集成服务门户(CCPortal): Assessing Chilgoza Pine ( Pinus gerardiana ) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies

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DOI	10.1016/j.tfp.2024.100521
	Assessing Chilgoza Pine ( Pinus gerardiana ) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies
	Mehmood, Kaleem; Anees, Shoaib Ahmad; Luo, Mi; Akram, Muhammad; Zubair, Muhammad; Khan, Khalid Ali; Khan, Waseem Razzaq
发表日期	2024
EISSN	2666-7193
起始页码	16
卷号	16
英文摘要	Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza ( Pinus gerardiana ) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman ' s Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high -risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.
英文关键词	Forest fires; Difference normalized burn ratio; Machine learning; Predictive modeling; Fire risk assessment
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001218622600001
来源期刊	TREES FORESTS AND PEOPLE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305864
作者单位	Beijing Forestry University; Beijing Forestry University; Beijing Forestry University; Nanning Normal University; Beijing Forestry University; Bahauddin Zakariya University; King Khalid University; King Khalid University; Universiti Putra Malaysia; Universiti Putra Malaysia; Universiti Putra Malaysia
推荐引用方式 GB/T 7714	Mehmood, Kaleem,Anees, Shoaib Ahmad,Luo, Mi,et al. Assessing Chilgoza Pine ( Pinus gerardiana ) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies[J],2024,16.
APA	Mehmood, Kaleem.,Anees, Shoaib Ahmad.,Luo, Mi.,Akram, Muhammad.,Zubair, Muhammad.,...&Khan, Waseem Razzaq.(2024).Assessing Chilgoza Pine ( Pinus gerardiana ) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies.TREES FORESTS AND PEOPLE,16.
MLA	Mehmood, Kaleem,et al."Assessing Chilgoza Pine ( Pinus gerardiana ) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies".TREES FORESTS AND PEOPLE 16(2024).