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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia
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Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

机译:使用Landsat数据合成的欧洲俄罗斯区域性北方森林覆盖和变化图

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摘要

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.
机译:北方森林是全球碳循环的重要组成部分,及时的监测可以评估森林覆盖率变化及其对碳动态的影响。地球观测数据集是重要的信息来源,可以对整个生物群落进行系统的监控。由USGS地球资源观测与科学中心(EROS)免费提供的Landsat影像可实现一致且及时的森林覆盖率更新。但是,在北部生物群系的某些部分内不规则的图像采集,加上缺少经过大气校正的数据,妨碍了使用Landsat图像进行区域规模的监测工作。在2000年至2005年之间,已在欧洲俄罗斯开发并测试了使用Landsat影像进行的北方森林覆盖和变化制图的方法。该方法采用了多年合成方法,适用于年度数据不完整,生长季节区域内变化的情况。长和频繁的云层覆盖。相对辐射归一化和云/阴影数据筛选算法用于创建无缝图像合成,而剩余的云/阴影污染小于总合成面积的0.5%。将监督分类树算法应用于时间序列图像合成,以表征研究期间的森林覆盖率和森林总损失。与独立获取的Landsat数据样本相比,森林覆盖率结果具有较高的一致性(总体准确性为89%,Kappa为0.78),并且符合俄罗斯政府的官方森林覆盖率统计数据。森林覆盖面积损失的总体规模制图结果与单个Landsat影像对变化检测结果相当(总体准确度为98%,Kappa为0.71)。据估计,欧洲2000-2005年俄罗斯的森林总覆盖损失为2210千公顷,比2000年减少了1.5%。在区域范围内,估计人口最多的地区(Leningradskaya和Moskovskaya Oblast)的森林覆盖比例损失最高。我们的结果强调了随着欧洲俄罗斯森林覆盖率变化的热点,大型工业城市周围的森林覆盖率枯竭。

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