首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >DEVELOPMENT OF PHENOLOGY BASED ALGORITHM FOR CROPLAND AND CROP TYPE MAPPING WITH MULTITEMPORAL LANDSAT IMAGE DATA - CASE STUDY IN THE NORTHWEST OF VIETNAM
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DEVELOPMENT OF PHENOLOGY BASED ALGORITHM FOR CROPLAND AND CROP TYPE MAPPING WITH MULTITEMPORAL LANDSAT IMAGE DATA - CASE STUDY IN THE NORTHWEST OF VIETNAM

机译:基于物候学的作物和作物类型映射的多时相LandSAT图像数据开发-以越南西北为例。

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Cropland mapping is very important for food security, policy development, land use planning, and environmental protection. Scientists have developed methods and techniques for cropland mapping with remote sensing image data. Both single date and multitemporal image data are used for generation of cropland and crop type maps. Multitemporal image data has advantages over single date image data from reliability and accuracy point of view because multitemporal image data allows to eliminate seasonality of vegetation. In this paper, the authors present new algorithm for cropland and crop type mapping with multitemporal Landsat image data. The algorithm requires for analysis of all Landsat scenes observed during one year and if needed, scenes in some years back to compensate clouds and cloud shadows. Phenology of land cover is constructed based on six bimonthly cloud free land covers that were automatically classified using the selected scenes. By grouping land covers within two months to six land covers of periods January–February, March–April, May-June, July–August, September–October, and November–December we create six bimonthly cloud free land covers that formulate a database for mapping cropland and crop types. By analysis of 50 Landsat scenes of path/row number 128/45 (northwest of Vietnam) observed mainly from 2017, 2016 and 2015 we success to map upland cropland and 14 crop types with area ranging from 145,143?ha to 3,373?ha per crop type. The study pointed out that phenology characterized by six bimonthly land covers is acceptable to identify cropland distribution and some specific crop types. For better results, apparently we need higher temporal resolution of image data. Due to uncertainty of the atmosphere, it is almost impossible to rely only on optical remote sensing data to achieve high temporality of data so application of multitemporal SAR data could be a way to overcome this obstacle.
机译:耕地制图对于粮食安全,政策制定,土地利用规划和环境保护非常重要。科学家们开发了利用遥感影像数据进行农田制图的方法和技术。单日期和多时间图像数据都用于生成农田和作物类型图。从可靠性和准确性的角度来看,多时间图像数据具有优于单日期图像数据的优势,因为多时间图像数据可以消除植被的季节性。在本文中,作者提出了使用多时态Landsat图像数据进行耕地和作物类型映射的新算法。该算法需要分析一年中观察到的所有Landsat场景,如果需要,还可以分析几年前的场景以补偿云和云影。土地覆盖物的物候是基于六个双月无云的土地覆盖物构建而成的,这些土地覆盖物是使用所选场景自动分类的。通过将两个月内的土地覆盖物分为1月至2月,3月至4月,5月至6月,7月至8月,9月至10月和11月至12月的六个土地覆盖,我们创建了六个月度无云的土地覆盖,这些数据库为绘制农田和作物类型图。通过分析主要在2017年,2016年和2015年观察到的路径/行数128/45(越南西北部)的50个Landsat场景,我们成功绘制了高地农田和14种作物类型的图,每种作物的面积从145,143?ha到3,373?ha类型。该研究指出,以六个月一次的双月土地覆盖为特征的物候学可以用来识别农田分布和某些特定的作物类型。为了获得更好的结果,显然我们需要更高的图像数据时间分辨率。由于大气的不确定性,几乎不可能仅依靠光学遥感数据来实现数据的高实时性,因此多时SAR数据的应用可能是克服这一障碍的一种方法。

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