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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 28971 - 28985
Main Authors: Li, Yunhao, Liu, Jinxiu, Maeda, Eduardo Eiji, Li, Xuecao, Pellikka, Petri, Heiskanen, Janne
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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-Net) model. CL-UNet integrates convolutional long short-term memory (ConvLSTM) units with a multiscale encoder-decoder architecture to capture both spatial and temporal dependencies inherent in satellite image time series. The model is trained to forecast spectral index values for the target year, and discrepancies between the predicted and observed values are used to identify anomalous changes. Burned areas are detected using a root mean square error (RMSE)-based thresholding method, which provides a robust mathematical foundation for selecting the optimal threshold. Using MODIS products with a temporal coverage spanning up to 9 years, we constructed spectral index time series based on multiple spectral indices. Experimental results from two large-scale forest fires demonstrate that Burn Area Index (BAI) exhibits the most qualified and stable performance. Under various conditions, the Recall, Precision, and F1 score of the model using the BAI time series as input data all exceed 80%. The results further indicate that CL-UNet achieves outstanding performance in both study areas. When utilizing the BAI time series as input, the Recall, Precision, and F1 score of CL-UNet are 1% to 10% higher than those of other models. The proposed algorithm shows strong potential for general spatiotemporal disturbance detection tasks in remote sensing applications beyond wildfire monitoring.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2025.3629696