Physical Sciences and Mathematics Commons^™

Deriving Landscape-Scale Vegetation Cover And Aboveground Biomass In A Semi-Arid Ecosystem Using Imaging Spectroscopy, Andrew Poley

Boise State University Theses and Dissertations

Environmental disturbances in semi-arid ecosystems have highlighted the need to monitor current and future vegetation conditions across the landscape. Imaging spectroscopy provide the necessary information to derive vegetation characteristics at high-spatial resolutions across large geographic areas. The work of this thesis is divided into two sections focused on using imaging spectroscopy to estimate and classify vegetation cover, and approximate aboveground biomass in a semi-arid ecosystem.

The first half of this thesis assesses the ability of imaging spectroscopy to derive vegetation classes and their respective cover across large environmental gradients and ecotones often associated with semi-arid ecosystems. Optimal endmember selection and …

A Machine Learning Approach To Estimation Of Downward Solar Radiation From Satellite-Derived Data Products: An Application Over A Semi-Arid Ecosystem In The U.S., Qingtao Zhou, Alejandro Flores, Nancy F. Glenn, Reggie Walters, Bangshuai Han

Geosciences Faculty Publications and Presentations

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations …