WEKO3
アイテム
DECDNet: A dual encoder change detection network for monitoring mangrove gain and loss using Sentinel-2 data
https://jircas.repo.nii.ac.jp/records/2001127
https://jircas.repo.nii.ac.jp/records/20011274d2cd5e8-996a-4e73-a482-0265cb08cafc
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
1-s2.0-S2352938525004203-main (7.2 MB)
|
.png)
|
| アイテムタイプ | 国際農研デフォルトアイテムタイプ（フル）(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2026-03-13 | |||||||||||
| タイトル | ||||||||||||
| タイトル | DECDNet: A dual encoder change detection network for monitoring mangrove gain and loss using Sentinel-2 data | |||||||||||
| 言語 | en | |||||||||||
| 作成者 |
Maung, Win Sithu
× Maung, Win Sithu （Author）
ORCID
0000-0001-8148-5393
× Tsuyuki, Satoshi （Author）
ORCID
0009-0009-0197-3844
× Hiroshima, Takuya （Author）
ORCID
0000-0001-8391-1018
|
|||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 権利情報 | ||||||||||||
| 権利情報 | © 2026 The Authors. Published by Elsevier B.V. | |||||||||||
| 権利情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/deed/en | |||||||||||
| 権利情報 | This is an open access article under the CC BY [Creative Commons Attribution 4.0 International] license (https://creativecommons.org/licenses/by/4.0/). | |||||||||||
| 主題 | ||||||||||||
| 主題 | Change detection, Mangrove, Deep learning, Remote sensing | |||||||||||
| 内容記述 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Mangrove forests are increasingly threatened by human activities such as aquaculture, agriculture, urban development, and illegal logging. Monitoring these dynamic changes requires accurate and efficient methods. However, traditional change detection approaches typically involve multi-step processes which can be time-consuming and prone to errors. Most existing deep learning models combined with remote sensing have shown great potential for environmental monitoring but are limited to binary classification (change and no change), making it difficult to capture specific land cover transitions such as mangrove gain or loss. To address these limitations, this study introduces DECDNet (Dual Encoder Change Detection Network), a novel deep learning model specifically designed for detecting and mapping mangrove gain and loss using Sentinel-2 imagery. The model utilizes a dual encoder-decoder structure that extracts spatial features from two time points and compares them using a subtraction layer. DECDNet was trained on Sentinel-2 data from 2015 to 2020, incorporating spectral indices to enhance discrimination. As a result, DECDNet achieved superior performance, with an IoU of 0.87, F1 score of 0.93, precision of 0.94, and recall of 0.92. In comparison, the standard deep learning models U-Net and FCN produced IoU values of 0.84 and 0.84, F1 scores of 0.91 and 0.91, precision values of 0.92 and 0.93, and recall values of 0.90 and 0.89, respectively. The generalization capability of DECDNet was further confirmed on a separate 2020–2023 dataset. The model detected 204.22 ha of mangrove loss and 747.09 ha of gain (2015–2020), and 463.48 ha of loss with 48.36 ha of gain (2020–2023) in the Wunbaik Reserved Mangrove Forest. These findings highlight practical implementation of DECDNet as a robust and scalable tool for mangrove monitoring and management. | |||||||||||
| 言語 | en | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Elsevier B.V. | |||||||||||
| 日付 | ||||||||||||
| 日付 | 2025-12-31 | |||||||||||
| 日付タイプ | Accepted | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 関連情報 | ||||||||||||
| 関連タイプ | isIdenticalTo | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1016/j.rsase.2025.101867 | |||||||||||
| 収録物識別子 | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2352-9385 | |||||||||||
| 書誌情報 |
en : Remote Sensing Applications: Society and Environment 巻 41, p. 101867, 発行日 2026-01-02 |
|||||||||||
| 助成情報 | ||||||||||||
| 識別子タイプ | Crossref Funder | |||||||||||
| 助成機関識別子 | https://doi.org/10.13039/501100009472 | |||||||||||
| 助成機関名 | Japan International Research Center for Agricultural Sciences (JIRCAS)(en) | |||||||||||
| 研究課題番号 | 05a1A1 | |||||||||||
| 研究課題番号URI | https://www.jircas.go.jp/program/proa/a1 | |||||||||||
| 研究課題名 | Development of comprehensive agricultural technologies for climate change mitigation and adaptation in Monsoon Asia(en) | |||||||||||
