A survey on systematic approaches in managing forest fires

Dhall, Aditya; Dhasade, Akash; Nalwade, Ashwin; V.K, Mohan Raj; Kulkarni, Vinay

doi:10.1016/j.apgeog.2020.102266

Cited by 31 publications

(10 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Taking the normalized model of Himawari-8 as a reference and using the data collected by FY-4A to evaluate the accuracy of the model, the result showed that R 2 reached 0.7542. Comparing the results calculated by the random forest normalization method of the literature [6] to the data of the same period, the indicators of the MEMN method are better than those of the RF method. This result means that the MEMN method based on Himawari-8 is more applicable to the FY-4A sensor, and the normalized effect is better.…”

Section: Discussionmentioning

confidence: 92%

“…The measured values of factors such as emissivity, solar zenith angle, slope, aspect, elevation, and brightness temperature are collected. The predictive values of brightness temperature are calculated by the MEMN model and the RF model in the literature [6]. The accuracy between the two models was compared and evaluated based on three indicators: coefficient of determination (R 2 ), mean absolute error (MAE), and root mean square error (RMSE).…”

Section: Comparison Of the Accuracy Of Normalization Methodsmentioning

confidence: 99%

“…Ground-monitoring refers to the manual monitoring of forest fires by establishing ground observation towers or using ground measuring instruments [5]. Its advantages are low cost, accurate positioning, and real-time detection, but groundbased measurement instruments may suffer from limited surveillance ranges and are not suitable for very large areas such as forests [6]. Space-monitoring refers to remote sensing monitoring of forest fires using satellites.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

Feng

Hua-shun

Zhang³

et al. 2022

Sustainability

View full text Add to dashboard Cite

This paper points out the shortcomings of existing normalization methods, and proposes a brightness temperature inversion normalization method for multi-source remote sensing monitoring of forest fires. This method can satisfy both radiation normalization and observation angle normalization, and reduce the discrepancies in forest fire monitoring between multi-source sensors. The study was based on Himawari-8 data; the longitude, latitude, solar zenith angle, solar azimuth angle, emissivity, slope, aspect, elevation, and brightness temperature values were collected as modeling parameters. The mixed-effects brightness temperature inversion normalization (MEMN) model based on FY-4A and Himawari-8 satellite sensors is fitted by multiple stepwise regression and mixed-effects modeling methods. The results show that, when the model is tested by Himawari-8 data, the coefficient of determination (R2) reaches 0.8418, and when it is tested by FY-4A data, R2 reaches 0.8045. At the same time, through comparison and analysis, the accuracy of the MEMN method is higher than that of the random forest normalization method (RF) (R2=0.7318), the pseudo-invariant feature method (PIF) (R2=0.7264), and the automatic control scatter regression method (ASCR) (R2=0.6841). The MEMN model can not only reduce the discrepancies in forest fire monitoring owing to different satellite sensors between FY-4A and Himawari-8, but also improve the accuracy and timeliness of forest fire monitoring.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Comparison Of the Accuracy Of Normalization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

Feng

Hua-shun

Zhang³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The early detection of forest fires allows for prompt action and emergency response. This helps in quickly controlling the fire and reducing the damage and loss caused by the fire [5][6][7][8]. At present, there are many ways to detect forest fires.…”

Section: Introductionmentioning

confidence: 99%

CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM

Xu,

Li,

Zhang

et al. 2024

Fire

View full text Add to dashboard Cite

In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%.

show abstract

“…Different aspects of fire in the wildland context, such as intensity of the fire, direction of spread, and the rate of fire spread in the wildland, are the main desired quantities, which are modeled in accordance to conditions that affect the wildfire, e.g., weather, topography, vegetation, etc. (Dhall, 2020). A flow diagram is presented in Fig.…”

Section: Introductionmentioning

confidence: 99%

Wildfire and evacuation simulation: An overview of research, development, and practice

Beyki¹,

Santiago²,

Laím³

et al. 2022

Advances in Forest Fire Research 2022

View full text Add to dashboard Cite

Wildfires have been growing dramatically over the past decades due to climate changes, global warming, droughts and forest landscape, and vegetation changes. Increasing losses due to wildfires have been a significant concern; therefore, protective measures are being taken to mitigate the risk of health hazards and even deaths. This paper assesses and reviews the approaches to fire spread and propagation simulation to predict the fire spread characteristics. These simulations are essential to determine the evacuation necessity and requirement. The assessment of evacuation triggers is investigated according to the available literature and studies on simulation and computational fluid dynamic (CFD) modeling of wildfire and fire spread are reviewed. In addition, the modeling and simulation of evacuation decisions, residents' evacuation, traffic patterns, and the evacuation trigger models are presented and discussed. All mentioned simulations are modeled on different scales, i. e. microscale, mesoscale and macroscale, based on the desired criteria assessment of each case study. These approaches are distinguished basically by their ability to model an area of certain extent, i. e. scale, and their accuracy in attaining the desired, analysis, data and prediction, i. e. detail level, and selecting one approach over the others is always a trade-off between these two criteria.

show abstract

A survey on systematic approaches in managing forest fires

Cited by 31 publications

References 56 publications

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM

Wildfire and evacuation simulation: An overview of research, development, and practice

Contact Info

Product

Resources

About