Post-fire resurveys reveal predictability of long-term conifer recruitment in severely burned California dry forests

Tortorelli, Claire M.; Young, Derek J.N.; Reilly, Matthew J.; Butz, Ramona J.; Safford, Hugh D.; Venuti, Nina E.; Welch, Kevin R.; Latimer, Andrew M.

doi:10.1016/j.foreco.2024.122100

Forest Ecology and Management

2024

DOI: 10.1016/j.foreco.2024.122100

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Post-fire resurveys reveal predictability of long-term conifer recruitment in severely burned California dry forests

Claire M. Tortorelli,

Derek J.N. Young,

Matthew J. Reilly

et al.

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IoT Networks and ML in Fire Ecology Innovations and Impacts

Badiger,

C.,

Shetty

et al. 2024

Advances in Environmental Engineering and Green Technologies

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Fire ecology has seen great progress due to the combination of machine learning (ML) and networks connected to the Internet of Things (IoT). It has significantly boosted our chances of monitoring, predicting, and even containing these fires more effectively. This chapter investigates the incorporation of the Internet of Things (IoT) into the framework of fire ecology studies using machine learning. It focuses on several technical advances, real-world applications, and the main impacts of wildfire suppression over the last few years. Through IoT sensors, drones, and sateIoT sensors, drones, and satellites, along with highly developed ML algorithms and big data utilization, immediately collect and analyze data, leading to early fire identification, and precise fire behaviour prediction. This chapter also discusses the difficulties and limitations of these technologies, as well as offering perspectives on future advancements and possible enhancements in the sector.

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IoT Networks and ML in Fire Ecology Innovations and Impacts

Badiger,

C.,

Shetty

et al. 2024

Advances in Environmental Engineering and Green Technologies

View full text Add to dashboard Cite

show abstract

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Post-fire resurveys reveal predictability of long-term conifer recruitment in severely burned California dry forests

Cited by 1 publication

References 34 publications

IoT Networks and ML in Fire Ecology Innovations and Impacts

IoT Networks and ML in Fire Ecology Innovations and Impacts

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