All-Weather Forest Fire Automatic Monitoring and Early Warning Application Based on Multi-Source Remote Sensing Data: Case Study of Yunnan

Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches prima...

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Vydáno v:	Fire (Basel, Switzerland) Ročník 8; číslo 9; s. 344
Hlavní autoři:	Gao, Boyang, Jia, Weiwei, Wang, Qiang, Yang, Guang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2025
Témata:	Algorithms all-weather forest fire monitoring Artificial satellites in remote sensing Automation China Data augmentation Data mining Data processing Data sources data-driven scheduling technology Deep learning Environmental monitoring Forest & brush fires Forest fire detection Forest fires Greenhouse gases integrated data–algorithm–visualization framework Landsat Localization Low temperature Machine learning Marking and tracking techniques Methods Monitoring multi-source remote sensing data Remote sensing Satellite imagery Satellites Statistical analysis Transfer learning U-Net deep learning algorithm Unmanned aerial vehicles Vegetation Visualization Weather Wildfires China Yunnan China
ISSN:	2571-6255, 2571-6255
On-line přístup:	Získat plný text
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