Detection of Forest Burned Area Using a Spatiotemporal Algorithm Based on Spectral Index Time Series Data

Timely and accurate wildfire detection is critical for ecological monitoring and disaster response. Using a model to handle both long-term time series and multiscale spatial features is challenging. In this study, we propose a novel burned area detection algorithm based on the CL-UNet (ConvLSTM-U-Ne...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 18; S. 28971 - 28985
Hauptverfasser:	Li, Yunhao, Liu, Jinxiu, Maeda, Eduardo Eiji, Li, Xuecao, Pellikka, Petri, Heiskanen, Janne
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Burned area convolutional long short-term memory (ConvLSTM) Convolutional neural networks Data models Disaster management Ecological monitoring Forest fire detection Forest fires Forestry Indexes Long short-term memory Mathematical models Monitoring near real-time disturbance detection Predictive models Recall Remote sensing Root-mean-square errors satellite image time series Satellite imagery Spatiotemporal phenomena Springs Time series Time series analysis Wildfires
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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