Application of GIS, remote sensing and GPS technologies for forest fire monitoring tasks

Semeniaka, V.Yu.; Zatserkovny, V.; Ilchenko, A.; Trofymenko, P.; Nikolaienko, O.

doi:10.3997/2214-4609.20215k2062

Cited by 1 publication

(1 citation statement)

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“…Through the analysis and processing of remote sensing images, vegetation conditions and environmental changes can be monitored in a timely manner. This process effectively identifies areas at risk of wildfires, providing a scientific basis for making decisions related to disaster prevention and mitigation [5,6].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework

Li,

Ren,

Tian

2024

Int. J. Interact. Mob. Technol.

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With the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures.

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Section: Introductionmentioning

confidence: 99%