Workshops-NEON: Training in Scientific Discoveries with NEON's AOP; Boise State University; Spring, 2016 - Early - 2017

将组织一系列三个实践讲习班，以促进和协调研究，将来自新的NSF国家生态观测网(NEON)的机载观测平台(AOP)的数据与来自当地实地站点的相关地面数据相结合。讲习班将侧重于遥感原理、遥感数据产品的开发以及实地数据的整合和验证，以利用霓虹灯数据解决生物物理科学中的具体研究问题。重点领域的专家和进行实践培训的专家将领导讲习班。霓虹灯数据将用于研讨会，并将向霓虹灯提供教程，以便在网上发布。讲习班的目的是通过在线传播培训材料，让大约75名面对面的参与者与数百名科学家一起参加。组织者将鼓励代表人数不足的少数族裔、妇女和残疾人以及职业生涯早期参与者出席。这一培训将有助于未来研究人员之间的合作和协调，并使科学家具备帮助描述美国生态系统在广泛的空间和时间范围内变化的技能。三个主题将指导三个讲习班：激光雷达处理和分析、高光谱处理和分析以及利用遥感数据产品进行生态系统建模。讲习班将汇集生物物理学家和遥感和生态系统建模方面的专家，鼓励开发一系列规模化数据产品，使基于霓虹灯的研究问题成为可能，并供更广泛的科学界使用。

A series of three hands-on workshops will be organized to foster and coordinate research that blends data from the Airborne Observing Platform (AOP) of the new NSF National Ecological Observatory Network (NEON) and associated ground data from local field sites. The workshops will focus on remote sensing principles, development of remote sensing data products, and field data integration and validation to address specific research questions in the biophysical sciences with NEON data. Experts in the focus areas and in conducting hands-on training will lead the workshops. NEON data will be used for the workshops and tutorials will be made available to NEON to post online. The workshops are designed to reach approximately 75 in-person participants each, along with hundreds of scientists via dissemination of the training materials online. Organizers will encourage attendance of underrepresented minorities, women, and persons with disabilities and early career participants. This training will assist with future collaborations and coordination among researchers and prepare scientists with the skills to help describe changes in U.S. ecosystems across a wide range of space and time. Three themes will guide the three workshops: LiDAR processing and analysis, hyper spectral processing and analysis, and the use of remotely sensed data products for ecosystem modeling. The workshops will bring together biophysical scientists and experts in remote sensing and ecosystem modeling that will encourage development of a range of scaled data products to enable NEON-based research questions and for use by the broader scientific community.