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

将组织一系列的三个实践研讨会，以促进和协调研究，将来自新的NSF国家生态观测站网络（NEON）的机载观测平台（AOP）的数据和来自当地实地站点的相关地面数据混合在一起。讲习班将集中讨论遥感原理、遥感数据产品的开发以及现场数据的整合和验证，以便利用NEON数据解决生物物理科学中的具体研究问题。重点领域和实践培训方面的专家将主持讲习班。NEON的数据将用于讲习班，教程将提供给NEON在线发布。这些讲习班的目的是通过在线传播培训材料，使每个讲习班约有75名面对面的参加者，以及数百名科学家。组织者将鼓励代表性不足的少数民族、妇女、残疾人和早期职业参与者参加。这项培训将有助于未来研究人员之间的合作和协调，并为科学家提供技能，帮助他们描述美国生态系统在大范围的空间和时间内的变化。三个主题将指导三个研讨会：激光雷达处理和分析，高光谱处理和分析，以及遥感数据产品在生态系统建模中的应用。这些讲习班将汇集遥感和生态系统建模方面的生物物理学家和专家，这将鼓励开发一系列规模数据产品，使基于neon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.