气候变化领域集成服务门户(CCPortal): Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record

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DOI	10.1016/j.rse.2021.112470
	Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record
	Zhang Y.; Ling F.; Wang X.; Foody G.M.; Boyd D.S.; Li X.; Du Y.; Atkinson P.M.
发表日期	2021
ISSN	00344257
卷号	261
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 fast-growing 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 fine-resolution 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. © 2021 Elsevier Inc.
英文关键词	Deep learning; Downscaling; Forest disturbance; Landsat and Sentinel-2; Small-scale clearing
语种	英语
scopus关键词	Deep learning; Forestry; Image enhancement; Vegetation; Deep learning; Down-scaling; Fine resolution; Forest disturbances; LANDSAT; Landsat and sentinel-2; Ratio images; Small scale; Small-scale clearing; Tropical forest; Tropics
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178821
作者单位	Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China; Sino-Africa Joint Research Centre, Chinese Academy of Sciences, Wuhan, 430074, China; Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan, 430205, China; School of Geography, University of Nottingham, Nottingham, NG7 2RD, United Kingdom; Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster, LA1 4YQ, United Kingdom; School of Geography and Environmental Science, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing, 100101, China
推荐引用方式 GB/T 7714	Zhang Y.,Ling F.,Wang X.,et al. Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record[J],2021,261.
APA	Zhang Y..,Ling F..,Wang X..,Foody G.M..,Boyd D.S..,...&Atkinson P.M..(2021).Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record.Remote Sensing of Environment,261.
MLA	Zhang Y.,et al."Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record".Remote Sensing of Environment 261(2021).