Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record
文献类型：期刊论文
| 作者 | Zhang, Yihang2; Ling, Feng2,3; Wang, Xia4; Foody, Giles M.5; Boyd, Doreen S.5; Li, Xiaodong2; Du, Yun2; Atkinson, Peter M.1,6,7 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2021-08-01 |
| 卷号 | 261页码:17 |
| ISSN号 | 0034-4257 |
| 关键词 | Forest disturbance Small-scale clearing Landsat and Sentinel-2 Deep learning Downscaling |
| DOI | 10.1016/j.rse.2021.112470 |
| 通讯作者 | Ling, Feng(lingf@whigg.ac.cn) |
| 英文摘要 | Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation of non-mechanized, small-scale (i.e., small area) clearings in tropical forests with Landsat series images. Because the appearance of small-scale openings in a tropical tree canopy are often ephemeral due to fastgrowing vegetation, and because clouds are frequent in tropical regions, it is challenging for Landsat images to capture the logging signal. Moreover, the spatial resolution of Landsat images is typically too coarse to represent spatial details about small-scale clearings. In this paper, by fusing all available Landsat and Sentinel-2 images, we proposed a method to improve the tracking of small-scale tropical forest disturbance history with both fine spatial and temporal resolutions. First, yearly composited Landsat and Sentinel-2 self-referenced normalized burn ratio (rNBR) vegetation index images were calculated from all available Landsat-7/8 and Sentinel-2 scenes during 2016-2019. Second, a deep-learning based downscaling method was used to predict fine resolution (10 m) rNBR images from the annual coarse resolution (30 m) Landsat rNBR images. Third, given the baseline Landsat forest map in 2015, the generated fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images were fused to produce the 10 m forest disturbance map for the period 2016-2019. From data comparison and evaluation, it was demonstrated that the deep-learning based downscaling method can produce fineresolution Landsat rNBR images and forest disturbance maps that contain substantial spatial detail. In addition, by fusing downscaled fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images, it was possible to produce state-of-the-art forest disturbance maps with OA values more than 87% and 96% for the small and large study areas, and detected 11% to 21% more disturbed areas than either the Sentinel-2 or Landsat-7/8 time-series alone. We found that 1.42% of the disturbed areas indentified during 2016-2019 experienced multiple forest disturbances. The method has great potential to enhance work undertaken in relation to major policies such as the reducing emissions from deforestation and forest degradation (REDD+) programmes. |
| WOS关键词 | COVER ; DEFORESTATION ; PERSISTENT ; RECOVERY ; IMAGERY ; CONGO ; MODIS ; MAPS |
| 资助项目 | Key Research Program of Frontier Sciences, Chinese Academy of Sciences[ZDBS-LY-DQC034] ; National Natural Science Foundation of China[41801292] ; Hubei Provincial Natural Science Foundation for Innovation Groups[2019CFA019] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA2003030201] ; Hubei Province Natural Science Fund for Distinguished Young Scholars[2018CFA062] ; Hubei Provincial Natural Science Foundation[2018CFB274] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000663446800001 |
| 资助机构 | Key Research Program of Frontier Sciences, Chinese Academy of Sciences ; National Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation for Innovation Groups ; Strategic Priority Research Program of Chinese Academy of Sciences ; Hubei Province Natural Science Fund for Distinguished Young Scholars ; Hubei Provincial Natural Science Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164102]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ling, Feng |
| 作者单位 | 1.Univ Southampton, Sch Geog & Environm Sci, Southampton SO17 1BJ, Hants, England 2.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China 3.Chinese Acad Sci, Sino Africa Joint Res Ctr, Wuhan 430074, Peoples R China 4.Wuhan Inst Technol, Sch Environm Ecol & Biol Engn, Res Ctr Environm Ecol & Engn, Wuhan 430205, Peoples R China 5.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England 6.Univ Lancaster, Fac Sci & Technol, Lancaster Environm Ctr, Lancaster LA1 4YQ, England 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yihang,Ling, Feng,Wang, Xia,et al. Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record[J]. REMOTE SENSING OF ENVIRONMENT,2021,261:17. |
| APA | Zhang, Yihang.,Ling, Feng.,Wang, Xia.,Foody, Giles M..,Boyd, Doreen S..,...&Atkinson, Peter M..(2021).Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record.REMOTE SENSING OF ENVIRONMENT,261,17. |
| MLA | Zhang, Yihang,et al."Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record".REMOTE SENSING OF ENVIRONMENT 261(2021):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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