Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring
Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring
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DOI：
10.1080/17538947.2016.1187673
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发表时间：
2016-01-01
影响因子：
5.1
通讯作者：
Campbell, Lorraine B.
中科院分区：
文献类型：
--
作者：
Hermosilla, Txomin;Wulder, Michael A.;Campbell, Lorraine B.
免费和开放地查阅陆地卫星档案使国家和全球地面监测项目得以实施。在此，我们总结了一个描述加拿大森林生态系统变化历史的项目，该项目提供了1984-2012年的时间序列数据。使用Composite2Change方法，我们对Landsat TM和ETM+图像生成的年度最佳可用像元(BAP)地表反射率图像进行了光谱趋势分析。总共使用了73,544幅图像来制作29个年度图像合成，产生了大约400 TB的临时数据产品，并产生了大约25 TB的年度无间隙反射合成和变化产品。平均而言，在年度BAP合成数据中，10%的像素缺少数据，其中86%的像素在连续两年或更短时间内存在数据缺口。变化检测的总体准确率为%。变化归因的总体准确率为92%，其中林分替代野火和收获的准确率较高。更改被分配到正确的年份，准确率为89%。该项目的成果为量化和描述森林生态系统的变化提供了基线信息和全国一致的数据来源。所采用的方法和吸取的经验教训建立了对所产生的产品的信心，并使其他国家能够制定或改进类似的卫星监测项目。
Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada's forested ecosystems with a time series of data representing 1984-2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating approximate to 400 TB of interim data products and resulting in approximate to 25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand-replacing wildfire and harvest. Changes were assigned to the correct year with an accuracy of 89%. Outcomes of this project provide baseline information and nationally consistent data source to quantify and characterize changes in forested ecosystems. The methods applied and lessons learned build confidence in the products generated and empower others to develop or refine similar satellite-based monitoring projects.