Building a Spectral Library to aid Hyperspectral Data Simulation for Boreal Alaska

Boreal tree/shrub species mapping is essential for forest and wildfire management. Remote sensing tools and techniques are efficient to derive this information from a satellite or airborne images. For species-level detailed vegetation information, we require hyperspectral image data with high spatial and spectral resolutions but their sparse availability and high cost of acquisition limit their use. Multispectral data have global coverage and are widely available, but their spatial and spectral resolutions are not good enough for species-level vegetation mapping. The paucity of hyperspectral data can be overcome by simulating hyperspectral data from widely available multispectral data using the Universal Pattern Decomposition Method (UPDM). One of the major requirements for the simulation of hyperspectral data is to have a reliable spectral library of all tree/shrub species for use in endmember selection. In this study, we are building a spectral library for all major tree and shrub species of the boreal forest of Alaska to aid in the simulation of hyperspectral data. We collected 20-25 in situ spectra for most tree/shrub species using a PSR+ 3500 Field Spectroradiometer. We extracted the (canopy level) image spectra from a 5-m resolution Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) reflectance image and combined them into the spectra collected from the field. We identified the optimal endmember subset that provides the best class separability within a spectral library using the Iterative Endmember Selection (IES) algorithm in the VIPER Tools 2 (beta) software. The spectral library includes canopy and leaf-level endmember spectra for wavelengths 350 - 2500 nm. These endmembers served as input in the UPDM matrix along with the reflectance of Sentinel-2 data, and spectral response function of Sentinel-2 and AVIRIS-NG data for hyperspectral data simulation. The simulated hyperspectral data will be used for species-level vegetation and fuel mapping for the boreal region of Alaska and will be shared publicly to meet the needs of hyperspectral data users in Alaska.