ESTIMATES OF FOREST CHARACTERISTICS DERIVED FROM REMOTELY SENSED IMAGERY AND FIELD SAMPLES: APPLICABLE SCALES, APPROPRIATE STUDY DESIGN, AND RELEVANCE TO FOREST MANAGEMENT

Information and knowledge about a given forested landscape drives forest management decisions. Within forest management though, information that adequately describes various characteristics of the forested environment in the spatial detail desired to make fully informed management decisions is often limited. Key metrics such as species composition, tree basal area, and tree density are typically too expensive to collect using ground-based inventory methods alone across broad extents for forest level planning (thousands of ha) at fine spatial detail that permit use at tactical spatial scales (tens of ha). However, quantifying these metrics accurately, in spatial detail, across broad landscapes is important to inform the management process. While relating remotely sensed data to classical ground-based survey data through modeling has shown promise for describing landscapes at the spatial detail need to inform planning and tactical scale projects, questions remain related to integrating both sources of data, sample design, and linking plots to remotely sensed data. This dissertation addresses critical aspects of these questions by: quantifying and mitigating the impact of co-registration errors; comparing various sample designs and estimation techniques using simulated ground-based information, remotely sensed data, and a variety of modeling techniques; developing enhanced image normalization routines; and creating an ensemble approach to estimating various forest characteristics that describe species composition, basal area, and tree density. This dissertation address knowledge gaps in the fields of forestry, remote sensing, data science, and decision science that can be used to efficiently and effectively inform the natural resource management decision-making process at fine spatial resolutions across broad extents