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A new Copernicus High Resolution Layer at Pan-Europeanscale: Small Woody Features

机译:Pan-Europmentscale的新哥白尼高分辨率层:小型木质特征

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Small Woody Features (SWF) represent some of the most stable vegetated linear and small landscape features providing numerous ecological and soeio-cuftural functions, which can be grouped in four main categories: soil and water conservation, climate protection and adaptation, support to biological diversity, and cultural identity. Copernicus Land monitoring service, through the High-Resolution Layers, aims to map those SWFs at Pan-European level (39 countries, 6 million square kilometers) with the use of more than 37,000 Very High Spatial Resolution (VHSR) Earth Observation (EO) scenes. This unprecedented mapping exercise is focused on the the extraction of SWF with a maximum width of 30m and a minimum length of 50m for linear features and a minimumand maximum area of 200 and 5,000m respectively. The main outputs are vector and raster products from the Pan-European coverage of the VHSR image data available from the European Space Agency (ESA) Copernicus Space Component Data Access (CSCDA) VHRJMAGE.2015 dataset. To fulfill this goal with a semi-automated approach, we developed a classification processing chain with the goal of very high computer efficiency and accuracy, using Object Based Image Analysis (OBIA) approach and Cloud-computing solutions. This highly efficient methodology based on differential attribute profiles (DAP) and classical classifier such as Random-Forest is particularly adapted to this exercise since it combines the use of spatial information and the spectral signature of each pixel. In this paper, we present the detailed methodology validated at Pan-European scale with various VHSR data source and landscape characteristics as well as full production results and internal validation.
机译:小伍迪功能(SWF)代表了最稳定的植被线性和小景观为特色,提供众多的生态和soeio-cuftural功能,它可以在四个主要类别进行分组:对生物多样性的保持水土,气候保护和适应,支持和文化认同。哥白尼土地监测服务,通过高分辨率层,目的是在泛欧层面的主权财富基金(39个国家,6000000平方公里)配合使用的地图37000多空间分辨率非常高(VHSR)地球观测(EO)场景。这种前所未有的绘图工作的重点是SWF与30米的最大宽度和50米的线性特征的最小长度为200和5000米分别minimumand最大面积的提取。主要成果是从泛欧洲范围可从欧洲航天局(ESA)哥白尼空间部分数据访问(CSCDA)VHRJMAGE.2015数据集中的VHSR图像数据的矢量和栅格产品。为了履行与半自动化的方法这个目标,我们制定了分类处理链的非常高的计算机的效率和准确性的目的,使用基于对象的图像分析(OBIA)方法和云计算解决方案。此基于差分属性简档(DAP)和经典分类器,诸如随机林高效方法是特别适合于本练习,因为它结合了使用空间信息和每个像素的光谱特征。在本文中,我们提出在泛欧洲范围内的各种VHSR数据源和景观特征以及完整的生产结果和内部验证验证的详细方法。

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