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Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

机译:使用Landsat数据和C-CAP产品计算补充十年代美国沿海土地使用和土地覆盖分类产品的地理空间方法

摘要

This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.
机译:本文讨论了地理空间数据处理方法和多年代Landsat时间序列的开发和实现,该时间序列用于计算美国沿海地区的土地利用和土地覆被(LULC)的总体分类和变化产品,包括七个类别(水,贫瘠,高地草本) ,非木质湿地,木质高地,木质湿地和城市)。假设每个目标年度的任何季节都有一个无云的Landsat场景,使用这种方法将NOAA生成的海岸变化与分析计划(C-CAP)产品的观测期延长了将近二十年。阿拉巴马州的莫比尔湾地区被用作研究区域,用于开发,演示和验证该方法,该方法用于在34年的时间段内以大约五年的间隔获取九个日期的LULC产品,使用单个日期的数据森林处于上叶,下叶或衰老混合状态的每种分类。使用决策规则结合Landsat数据和C-CAP增值产品的无监督分类来计算和优化分类。通过将分层的随机位置与可用的参考数据进行比较来评估每个分类的整体准确性,这些参考数据包括更高空间分辨率的卫星和航空影像,野外测量数据以及原始的Landsat RGB。总体分类准确度从83%到91%不等,总体Kappa统计数据从0.78到0.89不等。精度与从C-CAP数据得出的类似的通用LULC产品的精度相当。基于Landsat MSS的LULC产品准确性与来自Landsat TM或ETM +数据的准确性相似。对所有九个日期进行了准确的分类,无论季节如何,都能产生有效的结果。这种分类方法产生的产品用于通过附加GIS覆盖技术计算LULC变更产品。

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