Satellite imagery, specifically Landsat, have been widely used for mapping and monitoring wildfire burned areas. The new Landsat-9 satellite – with higher radiometric resolution compared to its predecessors, and improved temporal resolution when combined with Landsat-8 (∼8 days) – enables a wide range of applications, particularly burned area mapping (BAM). We propose a novel deep learning BAM model that leverages the strengths of the convolutional layers for deep feature generation from Landsat-9 imagery and shift-transformer block for burned area classification. The performance of the model is evaluated in five large fire case studies across the globe. BAM results are also compared with two state-of-the-art models, namely residual convolutional neural network and vision transformer. The proposed convolutional shift-transformer (CST) outperforms other models with an F1-score of greater than 96% across the case studies. Furthermore, CST only requires a single post-fire image that reduces computational costs compared to traditional models that use bi-temporal images.
This is an author-produced, peer-reviewed version of this article. © 2023, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International license. The final, definitive version of this document can be found online at Measurement, https://doi.org/10.1016/j.measurement.2023.112961
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Seydi, Seyd Teymoor and Sadegh, Mojtaba. (2023). "Improved Burned Area Mapping Using Monotemporal Landsat-9 Imagery and Convolutional Shift-Transformer". Measurement, 216, 112961. https://doi.org/10.1016/j.measurement.2023.112961
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