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

Spatial and temporal quantification of forest residue volumes and delivered costs

Growing demand for bioenergy, biofuels and bioproducts has increased interests in the utilization of biomass residues from forest treatments as feedstock. In areas with limited history of industrial biomass utilization, uncertainties in the quantity, distribution, and cost of biomass production and logistics can hinder the development of new bio-based industries. This paper introduces a new methodology to quantify and spatially describe delivered feedstock volumes and costs across landscapes of arbitrary size in ways that characterize operational and annual management decision-making. Using National Agricultural Imagery Program (NAIP) imagery, the forest is segmented into operational-level treatment units. A remote sensing model based on NAIP imagery and Forest Inventory and Analysis plot data is used to attribute treatment units with stand-level estimates of basal area, tree density, above ground biomass, and quadratic mean diameter. These methods are applied to a study site in southwestern Colorado to assess the quantity and distribution of treatment residue for use in bioenergy production. Results from the case study demonstrate how this generalized approach can be used in the analysis and decision-making process when establishing new bioenergy industries that use forest residue as feedstock.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author