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Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-05-03 , DOI: 10.1016/j.rse.2021.112470
Yihang Zhang , Feng Ling , Xia Wang , Giles M. Foody , Doreen S. Boyd , Xiaodong Li , Yun Du , Peter M. Atkinson

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.



中文翻译:

跟踪小型热带森林干扰:融合Landsat和Sentinel-2数据记录

有关森林干扰的信息对于热带森林管理和全球碳循环分析至关重要。Landsat任务的长期数据收集为理解全球热带森林扰动的过程提供了一些最有价值的信息。但是,利用Landsat系列图像估算热带森林中非机械化,小规模(即小面积)的空地时,存在很大的不确定性。由于植被快速生长,热带树木冠层中小规模开口的出现通常是短暂的,并且由于热带地区经常出现云,因此对Landsat图像捕捉测井信号提出了挑战。此外,Landsat影像的空间分辨率通常太粗糙,无法代表有关小规模空地的空间细节。在本文中,通过融合所有可用的Landsat和Sentinel-2图像,我们提出了一种以精细的时空分辨率改进对小规模热带森林扰动历史的追踪的方法。首先,每年合成的Landsat和Sentinel-2自参考归一化燃烧率(r NBR)植被指数图像是根据2016-2019年期间所有可用的Landsat-7 / 8和Sentinel-2场景计算得出的。第二,使用深学习基于缩减方法来预测精细分辨率(10米)- [R从年度粗糙分辨率(30米)陆地卫星NBR图像ř NBR图像。第三,给定2015年的基准Landsat森林图,将生成的高分辨率Landsat r NBR图像和原始Sentinel-2 r NBR图像融合,以产生2016-2019年的10 m森林扰动图。通过数据比较和评估,表明基于深度学习的降尺度方法可以产生高分辨率的Landsat r。包含大量空间细节的NBR图像和森林干扰图。此外,通过将缩小的高分辨率Landsat r NBR图像和原始的Sentinel-2 r NBR图像融合,可以生成最新的森林干扰图,其中小值的OA值超过87%,96%和较大的研究区域,并且比单独的Sentinel-2或Landsat-7 / 8时间序列检测到的受干扰区域多11%至21%。我们发现,在2016-2019年间确定的受干扰地区中,有1.42%经历了多次森林干扰。该方法具有巨大的潜力,可以加强与重大政策相关的工作,例如减少毁林和森林退化(REDD +)计划的排放。

更新日期:2021-05-04
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