Leaf Area Index in Riparian Forests: Estimation with Airborne Lidar vs. Airborne Structure-from-Motion and the Societal Value of Remotely Sensed Ecological Information

Thesis (Master's)--University of Washington, 2018Remote Sensing technology has expanded tremendously over the past few decades and has created value when integrated into environmental concepts and practices. But there is unmet potential for bolstering ecosystem services and creating additional value for society. Impediments such as the cost and complexity of the technology, and the difficulty of readily assimilating it into a decision-making process, must be overcome to facilitate broader use. This study demonstrates the capacity for an emerging and inexpensive remote sensing technology to estimate an important ecological indicator and then discusses the broader implications for societal value. First, we compare the estimation of effective leaf area index (LAIe) of heterogeneous riparian forests between two remote sensing methodologies: discrete-return Airborne Laser Scanning (ALS) and airborne structure-from-motion (SfM). LAIe is an indispensable component of process-based ecological research and can be associated with a variety of ecosystem services. SfM data acquisition is more frequent and inexpensive compared to ALS, but its capabilities less explored. Two point-cloud data files for each technology were evaluated using respective field-measured reference data. SfM shows promise: a combinational linear regression revealed that the distribution elevation values of upper-canopy point returns and the elevation values representing mid and max stand-level, when paired grey-level co-occurrence matrix (GLCM), can estimate LAIE (r2 = 0.62). Although it did not perform as well as ALS, which has more data representing light attenuation behavior (r2 = 0.66), SfM as an alternative methodology for remotely sensing ecological data has demonstrated potential and warrants further investigation. Next, we discuss how remotely sensed ecological information like LAIe can create value for society. We provide a primer on the ways in which society values the environment and how these values may be perceived and quantified, and the dynamic behavior that exists between them. We then introduce a major policy tool used in quantifying these values, benefit cost analysis, and why it is useful for framing environmental issues and how remote sensing can contribute to its outcomes. Finally, we review remote sensing applications used in increasing our understanding of society’s interaction with the environment and existing opportunities for value addition