Co2 Emission Trends and Risk Zone Mapping of Forest Fires in Subtropical and Moist Temperate Forests of Pakistan

Mannan, Abdul; Feng, Zongwei; Ahmad, A; Beckline, Mukete; Liu, J; Shah, Shipra; Amir, Muhammad; Ammara, U; Ullah, Tariq Saif

doi:10.15666/aeer/1702_29833002

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…8.1). Recent mega fires in Australia (Nolan et al 2020), Brazil (BBC 2019), the United States, Greece (CNN 2020;Smith et al 2019), and Indonesia have not only destroyed ecosystems but have also triggered climate change through carbon emission (Mannan et al 2017). A rise in global temperature by 2°C has contributed to increased frequency of forest fire, though only 3% of all forest fires have been caused naturally (Hirschberger 2016)-the majority of them have been sparked off by anthropogenic activities (FAO 2007).HKH, known as the Third Pole, or the Water Tower, is likely to face an increase in the frequency of forest fires as it is a region sensitive and vulnerable to climate change.…”

mentioning

confidence: 99%

Forest Fire Detection and Monitoring

Thapa

Chitale

Pradhan

et al. 2021

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

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mentioning

confidence: 99%

Forest Fire Detection and Monitoring

Thapa

Chitale

Pradhan

et al. 2021

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

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“…Fires damage millions of acres of forest annually, causing economic losses. Brazil, Australia, America, and Canada have all experienced devastating forest fres [2,3].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass

Prakash,

Neelakandan,

Tamilselvi

et al. 2023

International Journal of Intelligent Systems

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Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence-based forest fire warning system to prevent major disasters. This work aims to present an overview of vision-based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception-v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision-makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN-RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no-fire photos, the proposed RBFN-RAISR fire detection approach achieves 97.55% accuracy, 93.33% F-Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one-of-a-kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

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“…Millions of hectares of forest have been destroyed by wildfires in recent years (Bhujel, Maskey-Byanju et al 2017, Mannan, Feng et al 2017, Nolan, Boer et al 2020). Nature's balance is protected by forests.…”

Section: Introductionmentioning

confidence: 99%

Fire images classification using high order statistical features

Harkat¹,

Nascimento²,

Bernardino³

et al. 2022

Advances in Forest Fire Research 2022

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Wildfires and forest fires have devastated millions of hectares of forest across the world over the years. Computer vision-based fire classification, which classifies fire pixels from non-fire pixels in image or video datasets, has gained popularity as a result of recent innovations. A conventional machine learning-based approach or a deep learning-based approach can be used to distinguish fire pixels from an image or video. Deep learning is currently the most prominent method for detecting forest fires. Although deep learning algorithms can handle large volumes of data, typically ignore the differences in complexity among training samples, limiting the performance of training models. Moreover, in real-world fire scenarios, deep learning techniques with little data and features underperform. The present study utilizes a machine learning-based approach for extracting features of higher-order statistical methods from pre-processed images from publicly available datasets: Corsican and FLAME, and a private dataset: Firefront Gestosa. It should be noted that handling multidimensional data to train a classifier in machine learning applications is complex. This issue is addressed through feature selection, which eliminates duplicate or irrelevant data that has an effect on the model's performance. A greedy feature selection criterion is adopted in this study to select the most significant features for classification while reducing computational costs. The Support Vector Machine (SVM) is a conventional machine classifier that works on discriminative features input obtained using the MIFS, feature selection technique. The SVM uses a Radial Basis Function (RBF) kernel to classify fire and non-fire pixels, and the model's performance is assessed using assessment metrics like overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean.

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Co2 Emission Trends and Risk Zone Mapping of Forest Fires in Subtropical and Moist Temperate Forests of Pakistan

Cited by 5 publications

References 36 publications

Forest Fire Detection and Monitoring

Forest Fire Detection and Monitoring

Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass

Fire images classification using high order statistical features

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