Year of Award

2017

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Forest and Conservation Science

Department or School/College

College of Forestry and Conservation

Committee Chair

Brady W. Allred

Commitee Members

Stephen F. Siebert, Steven W. Running, Mark Hebblewhite, Hugh S. Robinson, Anna E. Klene

Abstract

Human driven alteration of the earth’s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth’s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS).

In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain.

The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices.

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© Copyright 2017 Nathaniel Paul Robinson