期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-09-01 |
| 作品名稱 | A 3D Spatial–Spectral–Temporal Deep Regression Model for Improving Mangrove Canopy Height Estimation Through Fusion of Optimized Red-Edge Sentinel-2 Bands and Sentinel-1 SAR Data |
| 作品名稱（其他語言） | |
| 著者 | Ilham Jamaluddin; Ying-Nong Chen; Amalia Gita Ayudyanti; Lin Hui; Kuo-Chin Fan |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , p.1-28 |
| 摘要 | Mangroves are vital blue carbon ecosystems with high carbon storage, where canopy height is a key parameter for estimating above-ground biomass. This study integrates Sentinel-1 SAR time-series and Sentinel-2 optical imagery and focused on the investigation of Red-Edge (RE) bands for mangrove canopy height estimation. A new RE-based spectral index named REMCH (RE Mangrove Canopy Height) index was developed for improving mangrove canopy height estimation. To improve the estimation results, this study proposed the 3DSST-RECLT model, a 3D spatial–spectral–temporal deep learning regression model that combining ConvLSTM, hybrid 3D–2D convolution, and Swin Transformer. Airborne LiDAR canopy height data served as target data. Results show fusing Sentinel-1 time-series and Sentinel-2 data using the proposed 3DSST-RECLT model achieved satisfactory performance, with the inclusion of RE bands and the REMCH index enhancing the model performance with an average mean absolute error of 1.648 m on the test dataset and outperforming the other models. This study produced mangrove canopy height maps of the coastal zone of South and Southwest Florida for 2017 and 2020 and found an increase in mangrove canopy height between 2017 and 2020. The produced mangrove canopy height map for 2020 was compared with three global canopy height maps, with the map generated in this study exhibiting higher accuracy. This finding indicates the advantage of integrating Sentinel-1 time-series and Sentinel-2 RE bands with a deep learning regression model to improve mangrove canopy height mapping and monitoring. |
| 關鍵字 | Feature extraction; Data models; Sentinel-1; Laser radar; Atmospheric modeling; Indexes; Estimation; Spatial resolution; Data mining; Carbon; Canopy height; deep learning regression; mangrove; red-edge (RE) bands; Sentinel-1; Sentinel-2 |
| 語言 | en_US |
| ISSN | 2151-1535; 1939-1404 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | Ying-Nong Chen |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127899 ) |