Thesis (M.S.) in Forestry--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 74-80).

Thesis (M.S.)--Oregon State University, 1997. / Typescript (photocopy). Includes bibliographical references (leaves 43-47). Also available on the World Wide Web.

Thesis (B.S.)--Rochester Institute of Technology, 1983. / Typescript. Includes bibliographical references (leaves 18-22).

Thesis (M.S.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains x, 128 p. : ill. (some col.), maps (some col.) Vita. Includes abstract. Includes bibliographical references (p. 116-121).

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2007. / Includes bibliographical references (p. 79-82).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1972. / Typescript. Vita. Description based on print version record. Includes bibliographical references (leaves 129-136).

Thesis (Ph. D.)--University of Helsinki. / Includes bibliographical references (p. 86-93).

Bayesian estimates of gross cubic- foot volume per acre were computed for four stand types (plantation pine, natural pine. hardwood. and mixed wood stands) using aerial photo volume tables as the prior information source. Aerial photographs provided a reliable source of information even though most photographs were nearly five years old. For a given level of precision within a particular stand, Bayesian methods reduced the required field sample size up to 50% using all or half of the prior information available. Those priors which utilized a regression or a regression/topographic correction in the estimation of photo heights required less field information for the given precision level than those priors which used uncorrected or topographic corrected photo heights. In order to obtain meaningful gains in sample size reduction corrections to the estimated photo heights should be made. Although the uncorrected prior produced generally less biased estimates. the reduction in sample size was not as large as that observed using other prior types. Greater gains were attributed to the better accuracy of the prior distribution. Although Bayesian methods are biased, it appeared that these methods tempered severely biased prior distributions. In the hardwood stand for example, the average bias present in the photo volume data amounted to -140%. After combining the prior with the field sample, the greatest average bias was -50%. Bayesian methods performed better than the traditional estimation methods in terms of precision. In a one to one comparison. the Bayes standard error was consistently less than its non-Bayes counterpart. The one exception to this trend was the regression prior from the hardwood stand. The poor performance of the prior was due to the weak height regression correction equation. Modal priors utilized were not subject to the extreme input values for prior distribution development as their conservative empirical prior counterparts were. Less overall variation was observed 1n the estimated values. Under the conditions for mode selection set forth in this project, modal priors provided another good source of prior information. / Master of Science

The potential for using large scale, small format aerial photography to obtain seedling density and woody competition information was investigated. Factors affecting photo interpretation of seedlings were examined and equations to predict seedling density and woody competition levels were developed and evaluated. Two scales of imagery, 1:4000 and 1:6000 were considered to compare their relative merits for these purposes. Greater age of the seedlings and their inherent development generally served to improve photo interpretation. The amount of woody competition present in the plots tended to hinder seedling identification at the 1:4000 scale, while enhancing it when 1:6000 scale data was used. Seedling density estimation and evaluation of competition through Free-To-Grow classification predictions yielded results comparable to ground surveys. Estimation of total groundline basal area in all woody competition, and classification of the plots by the amount of hard-to-control competition they contained, were less successful. These results may have been due in part to the partial leaf fall that occurred prior to obtaining the imagery. Many of the results found in this study favored the larger scale (1:4000) imagery, and its use for the procedures described is recommended. / M.S.

From the known image positional errors on aerial photographs, this thesis computes and evaluates acreage estimation errors. Four hypothetical tracts were used in simulating aerial photographs with 104 different camera orientation combinations. Flying heights of 4000 and 6000 feet, focal lengths of 24 and 50 millimeters with and without lens distortion, and tilts of 0, 3, 6, and 12 degrees were simulated. The 416 photographs were all simulated with the camera exposure station centered above the midpoint of the respective tract's bounding rectangle. The topographic relief of the tracts ranged from 19 feet in the Coastal Plain to 105 feet in the Piedmont. It was found that lens focal length did not have an independent effect on the acreage estimates. Relief error, the lowest, averaged -0.080%. In comparison, small errors in calculating scale were shown to be larger than relief errors. Tilt was recommended to be limited to six degrees, averaging +1.6% error at six degrees tilt. Because of its positive exponential nature when the tracts are centered, tilt can induce large biases. including tilts from zero to six degrees,the average was 0.634%. Lens distortion error averaged -0.686%. Overall, the average acreage error was 0.363% for simulations up to and including six degrees of tilt with and without lens distortion. This result is for centered tracts, and it was felt many of the errors were compensating given this situation. In conclusion, the photographic images can estimate areas to $1%, however, additional errors are imparted during actual measurement of the photographs. / Master of Science