Fine Scale Remote Sensing of Forest Structure and Condition

Abstract

Forest structure refers to the three-dimensional arrangement of biophysical elements (i.e., vegetation) within a forested landscape, whereas forest condition is the characteristics of biophysical elements that are not directly related to the physical arrangement. Condition can relate to measurable quantities such as moisture content. Disturbances in forest structure, such as fire consuming woody vegetation, can result in changes to the condition of the vegetation, such as charring and reduced photosynthesis ability due to scorched leaves. Quantifying both forest structure and condition is crucial for answering a variety of ecological questions. Utilizing remote sensing techniques such as drone imagery and terrestrial lidar allows for the quantification of fine scale ecological processes and changes at a scale where direct measurement is possible.This interdisciplinary dissertation examined three ecological questions related to forest structure and condition. The first question, “Can lidar detect changes in moisture content of potential fire fuels?’, used terrestrial lidar and field spectrometers to measure changes in surface reflectance of dead litter beds as they dried. Moisture content plays a crucial role in determining flammability and this first inquiry presents novel methods for quantifying the moisture content of leaf litter beds using laser pulses from terrestrial lidar scanners. The second question, “Can drone digital aerial photogrammetry be used to quantify fire effect in three-dimensions?”, explored the utility of multispectral drone imagery to generate three-dimensional photogrammetric point clouds to quantify fire effects at different canopy height strata, and to compare drone estimates of fire effects to satellite-derived estimates. The third question, “How can terrestrial lidar be used to quantify habitat, and then upscale those estimates to region wide models using machine learning?”, utilized terrestrial lidar scans to quantify forest structure and viewshed in the understory to first identify areas that met habitat requirements for Canada lynx (Lynx canadensis), and then to use XGboost machine learning algorithms to produce region-wide habitat models using aerial lidar. The unifying theme through these three questions, and the core of this dissertation, is the utilization of fine-scale remote sensing methods to quantify the structure and condition of forested systems to address ecological questions in an interdisciplinary manner.