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Technical note: A simple approach for efficient collection of field reference data for calibrating remote sensing mapping of northern wetlands

机译:技术说明:用于校准北湿地遥感映射的现场参考数据的简单方法

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The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to take wide-angle images from 3.1 to 4.5m in altitude using an extendable monopod, as well as representative near-ground (1m) images to identify spectral and structural features that correspond to various land covers in present lighting conditions. A semi-automatic classification was made based on six surface types (graminoids, water, shrubs, dry moss, wet moss, and rock). The method enables collection of detailed field reference data, which is critical in many remote sensing applications, such as satellite-based wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel?1 and 2 (10m pixel size).
机译:遥感陆地覆盖产品的校准和验证高度依赖于准确的现场参考数据,这是收集的昂贵和实际上具有挑战性的。我们描述了用于集合现场参考数据的光学方法,这是一种快速,成本效益和强大的替代现场调查和UAV成像。使用可伸展的单盖,以及代表近地(1M)图像,使用轻量级,防水,远程控制的RGB相机(Gopro Hero4 Silver,Gopro Inc.)用于从海拔3.1到4.5米处取得广角图像,以及代表近地(1M)图像识别与当前照明条件中的各种陆地覆盖物对应的光谱和结构特征。基于六种表面类型(禾本科,水,灌木,干燥苔藓,湿苔和岩石)进行半自动分类。该方法使得能够收集详细的字段参考数据,这在许多遥感应用中至关重要,例如基于卫星的湿地映射。该方法使用普通的非昂贵设备,不需要特殊技能或培训,并通过补充中包含的逐步手册促进。随着时间的推移,可以建立一个全球地面封面数据库,其可以用作来自诸如哨兵?1和2(10M像素尺寸)的卫星的非森林湿地研究的参考数据。

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