Join NEON for a virtual workshop June 14-17, 2021!
This workshop introduces participants to NEON, teaches them how to access and work with NEON data, and allows them to interact with NEON science staff to get assistance working on the specific data products they are interested in using. The workshop includes hands-on, interactive instruction on how to access and work with NEON data, both through the NEON data portal and programmatically.
NEON will be presenting at the Exploring a Dynamic Soil Information System workshop, hosted by the National Academies of Sciences, Engineering, and Medicine.
Paulinus Chigbu, PhD and Robert Shepard, PhD, will be hosting a three-day workshop with the National Ecological Observatory Network (NEON) with a specific focus on identifying opportunities for collaboration, and increasing awareness of and engagement with NEON, among faculty, researchers, and students of Historically Black Colleges and Universities (HBCUs).
The participants of the workshop will learn about the available datasets and tools to study ecological processes using NEON data. By the end of the workshop, participants will understand how they can use the suite of NEON data products to address their research questions.
This workshop will provide hands on experience with working lidar data in raster format in R. It will cover the basics of what lidar data are and commonly derived data products.
This NEON internal brownbag introduces the concept of Hierarchical Data Formats in the context of developing the NEON HDF5 operational file format. Look here to discover resources on HDF5, code snippets in R, Python and Matlab to use H5 files and some example H5 files for Remote Sensing Hyperspectral data and time series temperature data.
This workshop will providing hands on experience with working with hyperspectral imagery in hierarchical data formats (HDF5) in R. It will also cover basic raster data analysis in R.
This lunchtime brown-bag workshop will explore how different gridding methods and associated settings can impact rasters derived from sample points. We will use a LiDAR point cloud, which represents canopy height values, to create several raster grids using different point-to-pixel conversion methods. We will then quantify and assess differences in height values derived using these different methods.
