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Development of a Historical Multi-Year Land Cover Classification Incorporating Wildfire Effects

机译:结合野火影响的历史多年土地覆盖分类的发展

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摘要

Land cover change impacts ecosystem function across the globe. The use of land cover data is vital in the detection of these changes over time; however, most available land cover products, such as the National Land Cover Dataset (NLCD), are produced relatively infrequently. The most recent NLCD at the time of this research was produced in 2006 and does not adequately reflect the impact of land cover changes that have occurred since, including the occurrence of two large wildfires in 2008 in our study area. Therefore, there is a need for the classification of historical remotely sensed data, such as Landsat scenes, through replicable methods. While it is possible to collect field data coinciding with current or future Landsat acquisitions, it is impossible to retrospectively collect data for previous years; thus, fewer studies have focused on the classification of historical scenes. Using a single year of field reference and multi-year aerial photography data, we applied a simple decision tree classifier to accurately classify historic satellite data and produced maps of land cover to incorporate the effects of 2008 wildfires occurring between NLCD production dates. Overall accuracy ranged from 76 to 90 percent and was assessed using conventional error matrices.
机译:土地覆盖变化影响全球生态系统功能。使用土地覆盖数据对于及时发现这些变化至关重要。但是,大多数可用的土地覆盖产品(例如国家土地覆盖数据集(NLCD))生产的频率相对较低。进行此项研究时,最新的NLCD产生于2006年,但没有充分反映此后发生的土地覆被变化的影响,包括我们研究区域2008年发生的两次大森林大火。因此,需要通过可复制的方法对历史遥感数据(例如Landsat场景)进行分类。尽管可以收集与Landsat当前或将来的收购相吻合的现场数据,但不可能追溯地收集前几年的数据;因此,很少有研究集中在历史场景的分类上。使用一年的野外参考资料和多年的航拍数据,我们应用了一个简单的决策树分类器来准确地对历史卫星数据进行分类,并制作了土地覆盖图,以纳入NLCD生产日期之间发生的2008年野火的影响。总体准确度在76%到90%之间,并且使用常规误差矩阵进行了评估。

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