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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification
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Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification

机译：使用500 M Modis Land Cover产品来推导一致的大陆尺度30米Landsat Land Cuct分类

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Classification is a fundamental process in remote sensing used to relate pixel values to land cover classes present on the surface. Over large areas land cover classification is challenging particularly due to the cost and difficulty of collecting representative training data that enable classifiers to be consistent and locally reliable. A novel methodology to classify large volume Landsat data using high quality training data derived from the 500 m MODIS land cover product is demonstrated and used to generate a 30 m land cover classification for all of North America between 20 degrees N and 50 degrees N. Publically available 30 m global monthly Web-enabled Landsat Data (GWELD) products generated from every available Landsat 7 ETM+ and Landsat 5 TM image for a three year period, that are defined aligned to the MODIS land products and are consistently pre-processed data (cloud-screened, saturation flagged, atmospherically corrected and normalized to nadir BRDF adjusted reflectance), were classified. The MODIS 500 m land cover product was filtered judiciously, using only good quality pixels that did not change land cover class in 2009, 2010 or 2011, followed by automated selection of spatially corresponding 30 m GWELD temporal metric values, to define a large training data set sampled across North America. The training data were sampled so that the class proportions were the same as the North America MODIS land cover product class proportions and corresponded to 1% of the 500 m and <0.005% of the 30 m pixels. Thirty nine GWELD temporal metrics for every 30 m North America pixel location were classified using (a) a single random forest, and (b) a locally adaptive method with a random forest classifier derived and applied locally and the classification results spatially mosaicked together. The land cover classification results appeared geographically plausible and at synoptic scale were similar to the MODIS land cover product. Detailed visual inspection revealed that the locally adaptive random forest classifications and associated classification confidences were generally more coherent than the single random forest classification results. The level of agreement between the 30 m classifications and the MODIS land cover product derived training data was assessed by bootstrapping the random forest implementation. The locally adaptive random forest classification had higher overall agreement (95.44%, 0.9443 kappa) than the single random forest classification (93.13%, 0.9195 kappa). The paper concludes with a discussion of future research including the potential for automated global land cover classification. (C) 2017 The Authors. Published by Elsevier Inc.
机译：分类是遥感中的基本进程，用于将像素值相关联到表面上存在的陆地覆盖类。在大面积的土地覆盖分类是挑战，特别是由于收集了代表性培训数据的成本和难度，使得分类器能够保持一致和局部可靠。使用高质量培训数据进行分类的新型方法，这些方法使用高质量的培训数据来自500米Modis Land封面产品，并用于在20度N和50度N之间为所有北美生成30米的土地覆盖分类可用30米全球每月支持的Web的LANDSAT数据（GWELD）产品从每个可用的LANDSAT 7 ETM +和Landsat 5 TM图像中产生的三年期间，定义与MODIS Land产品对齐，并且是一致的预处理数据（云-Screened，饱和度标记，大气纠正和标准化为Nadir BRDF调整的反射率）。 Modis 500米覆盖产品是明智地过滤的，只使用2009年或2011年没有改变土地覆盖类的良好质量像素，然后自动选择空间相应的30米GWELD时间公制值，以定义大型训练数据在北美举行抽样。培训数据被取样，以便班级比例与北美MODIS土地覆盖产品类比例相同，占500米和30米像素的<0.005％的1％。每30米北美的Gweld颞级度量为每30米北美的颞型度量，使用（a）单个随机森林分类，并且（b）一种具有随机森林分类器的局部自适应方法，其本地衍生和应用，并且在空间上镶嵌在一起的分类结果。土地覆盖分类结果出现了地理位置合理的，并且在天气尺度上类似于Modis Land覆盖产品。详细的视觉检查显示，当地自适应随机森林分类和相关的分类信心通常比单一随机森林分类结果更加连贯。通过自动启动随机森林实施，评估了30米分类和MODIS土地覆盖产品衍生培训数据的协议水平。局部自适应随机森林分类总体协议（95.44％，0.9443 kappa）比单一随机林分类（93.13％，0.9195 kappa）。本文讨论了未来研究的讨论，包括自动全球土地覆盖分类的潜力。 （c）2017作者。 elsevier公司发布

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