Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Van, Linh Nguyen; Tran, Vinh Ngoc; Nguyen, Giang V.; Yeon, Minho; Do, May Thi-Tuyet; Lee, Giha

doi:10.1016/j.ecoinf.2024.102601

Ecological Informatics

2024

DOI: 10.1016/j.ecoinf.2024.102601

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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Linh Nguyen Van,

Vinh Ngoc Tran,

Giang V. Nguyen

et al.

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Cited by 5 publications

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IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection

Huang,

Chen,

Heidari

et al. 2024

iScience

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IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection

Huang,

Chen,

Heidari

et al. 2024

iScience

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Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

Kim,

Park,

Shin

et al. 2024

Hydrology

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The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

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Application of Getis-Ord Correlation Index (Gi) for Burned Area Detection Improvement in Mediterranean Ecosystems (Southern Italy and Sardinia) Using Sentinel-2 Data

Lanorte,

Nolè,

Cillis

2024

Remote Sensing

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This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities.

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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Cited by 5 publications

References 56 publications

IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection

IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection

Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

Application of Getis-Ord Correlation Index (Gi) for Burned Area Detection Improvement in Mediterranean Ecosystems (Southern Italy and Sardinia) Using Sentinel-2 Data

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