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Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

机译:使用支持Web的Landsat数据(WELD)监测美国(CONUs)土地覆盖变化

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摘要

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests\u27 absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. Google Earth™ time series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user\u27s accuracy of 78% and a producer\u27s accuracy of 68%. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (Google Earth™) classification; however, user\u27s (42%) and producer\u27s (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user\u27s and producer\u27s accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user\u27s and producer\u27s accuracies) and urban gain (72% and 18% for respective user\u27s and producer\u27s accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs. gov/CONUS_5Y_LandCover/).
机译:使用美国地质调查局地球资源观测与科学中心(EROS)提供的基于Web的Landsat数据,以30 m的空间分辨率对2006年至2010年间美国本土(CONUS)的森林覆盖面积损失和裸地收益进行了量化(http: //landsat.usgs.gov/WELD.php)。该方法通过决策树分类算法,将多时相WELD度量标准和专家得出的森林覆盖率损失和裸地增益训练数据相关联。报告了州和生态区域范围内的森林覆盖率损失,并使用来自GLAS(地球科学激光测高系统)仪器的LiDAR数据对没有变化的核心森林进行了识别并进行了验证。在沙漠或半沙漠环境以外的大都市统计区域(MSA),裸露的地面增益与人口变化相关。 Google Earth™时间序列图像用于验证产品。映射的森林覆盖面积损失共计53,084 km2,被保守地描绘出来,用户的准确度为78%,生产者的准确度为68%。排除邻接误差,用户和生产者的准确性分别上升到93%和89%。映射的裸露地面增益为5974平方千米,几乎与参考(Google Earth™)分类的估计面积相匹配;但是,用户(42%)和生产者(49%)的准确度远低于森林覆盖损失产品的准确度。排除邻接误差,用户和生产者的准确性分别上升到62%和75%。与最近的2001-2006年USGS国家土地覆盖数据库验证数据相比,森林损失(用户和生产商的准确度分别为82%和30%)和城市收益(用户和生产商的分别为72%和18%) u27s精度),使用具有WELD数据的单个CONUS比例模型的结果是有希望的,并指出了在全国范围内对关键土地覆被过渡进行操作的潜力。但是,验证结果突出了局限性,其中一些局限性可以通过改善训练数据,创建更健壮的图像特征空间,向输入中添加同期Landsat 5数据以及修改定义集来解决时空和空间观测尺度的差异来解决。可通过WELD官方网站(ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs。gov / CONUS_5Y_LandCover /)获得所提供的土地覆盖范围和变化数据。

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