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YERC Data and Collaborations
YERC scientists use a wide range of analysis methods based on the data being used and the research objectives. Typical classification approaches include maximum likelihood, decision tree, spectral angle mapper and matched filters. Spectral analysis methods used by YERC staff include spectral feature analysis (e.g. band depth analysis), spectral mixture analysis (including Multiple Endmember Spectral Mixture Analysis), principal components analysis (and MNF), vegetation indices (e.g. PRI, EWT, NDVI, NDWI) and derivative spectra. While the remote sensing lab is largely an ENVI/IDL, ESRI product (ArcView, ArcGIS) and R facility, a large number software are used in the lab and many analysis approaches and are developed and coded in-house.
Remote Sensing Lab
YERC’s remote sensing lab is a state-of-the-art computing facility with all of the resourced needed to conduct remote sensing research. It is a PC-based computing environment in a fully networked configuration with access to all the necessary data input/output devices for handling large datasets including 8mm and DLT tape drives and DVD burners. The lab uses networked storage devices that serve and redundantly backup the large volume of remote sensing and field data.
Field Support
An important component in all of YERC’s research is high quality field data. Field crews based at YERC’s field station utilize the latest in field equipment and sampling methods to support all YERC remote sensing research. This allows YERC researchers access to detailed current information on a range of field parameters including: vegetation type, density, vegetation height, biomass, soil moisture, water depth and water temperature. The wealth of field data YERC has collected in recent years provides an excellent field data set for validation and modeling of ecological phenomena using remote sensing.
Data & Collaboration - Datasets available by request.
YERC has made extensive field measurements and has professional experience with processing LiDAR (Light Detection and Ranging), PolSAR (polarimetric Synthetic aperture radar), InSAR (Interferometric Synthetic aperture radar), and hypterspectral imageries. These field measurements and remote sensing imageries are good data sources for remote sensing study, (e.g validating canopy height, canopy percentage, tree density, vegetation biomass and simulating broadband remote sensing etc..). If you would like to use these datasets, please contact us.
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