气候变化领域集成服务门户(CCPortal): Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

CCPortal

DOI	10.3390/rs16091517
	Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria
	Zikiou, Nadia; Rushmeier, Holly; Capel, Manuel I.; Kandakji, Tarek; Rios, Nelson; Lahdir, Mourad
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	9
卷号	16 期号:9
英文摘要	Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.
英文关键词	fire severity classification; Sentinel-2; Planetscope; spectral indices; machine learning; SVM; CNN; semi-supervised learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001220111200001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290376
作者单位	Yale University; Yale University; University of Granada; Yale University; Yale University
推荐引用方式 GB/T 7714	Zikiou, Nadia,Rushmeier, Holly,Capel, Manuel I.,et al. Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria[J],2024,16(9).
APA	Zikiou, Nadia,Rushmeier, Holly,Capel, Manuel I.,Kandakji, Tarek,Rios, Nelson,&Lahdir, Mourad.(2024).Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria.REMOTE SENSING,16(9).
MLA	Zikiou, Nadia,et al."Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria".REMOTE SENSING 16.9(2024).