Black/white/male/female struggles over knowledge correctness and who is brave are examined inductively in the field of bushfires. The paradoxes of a white male icon are linked to contradictions in gender theories in disaster. In mainstream literature, assumptions of innate white male superiority in bravery justify white women???s diminution and white male domination. In feminist theory, women???s diminution is the problem and their bravery for struggling against hegemony applauded. Philosophies of bravery are explored in 104 semistructured interviews and 12 months??? fieldwork as a volunteer bushfirefighter. There is great variety in the ways volunteers cope with bushfires. However, evidence of white male hegemony emerges when volunteers complain of state and territory indifference to preventing property and environmental damage and injury and death. Evidence is examined that Indigenous Australians once managed bushfires better than a sprawl of bureaucracy. Bushfire service claims that Aborigines knew nothing about hazard reductions are contradicted. This debate over bushfire management leads to the discovery of a third epistemology breaking with claims of white male iconic bravery and bureaucratic mastery. To generalise about the habitus of claims to knowledge and bravery, I analyse Newcastle Herald articles from 1881-1981. Three competing knowledge fields and their associated struggles are examined; Indigenous Australians and white womens??? emancipatory struggles confront data on bushfirefighting. Bushfires emerge as a serious problem, a bureaucratic power base and a white male icon from the 1920s.

There is growing interest in airborne lidar for forest carbon accounting and precision forestry purposes. Airborne lidar systems offer a cost-effective, versatile, operationally flexible and robust sampling tool for forest managers. The objective of this study was to develop and test lidar canopy surface enhancement and segmentation processes for estimating dominant above-ground biomass (DAB) in a harvested eucalypt forest on the Central Coast of New South Wales (Australia). The Crown Infill, Trim and Smooth (CITS) process, incorporating a series of filters, algorithms, and selective multi-stage smoothing, was used to enhance lidar canopy surfaces prior to segmentation. Canopy segmentation was achieved using a vertical crown template approach termed the Spatially and Morphologically Isolated Crest (SMIC) process. SMIC delineates dominant tree crowns by detecting elevated crown crests within a 3D lidar canopy surface. Consolidated crown units constitute the basic sampling, analysis and reporting units for wall-to-wall forest inventory. The performance, sensitivity and limitations of these procedures were evaluated using a combination of simulated forest models and actual lidar forest data. Automated crown polygons were used as a sampling template to extract dominant tree height values which were converted to DAB estimates via height-to-biomass relationships derived from field survey and on-site destructive sampling. Results were compared with field based tree height and biomass estimates. Compared against a manually derived crown map from a 2ha field plot, canopy segmentation results revealed a producer???s accuracy of 76% and overall accuracy of 67%. Results indicated a trend toward greater crown splitting (fragmentation) as trees increase in age, height, stem diameter and crown size. Extracted dominant tree height values were highly correlated with ground survey height estimates (r2 0.95 for precision survey and r2 0.69 for standard survey). There was also no significant difference between SMIC and manual crown height estimates. SMIC units overestimated ground-based DAB by 5%; this increased to 36% with the inclusion of segmentation errors. However, SMIC estimation of total plot above-ground biomass (AGB) was within 9% of the ground-based estimate. Results are encouraging considering the mixed-species, multi-aged composition of the forest, and the combined effects of SMIC segmentation and lidar height errors.

Thesis (Ph. D.)--Oregon State University, 1984. / Typescript (photocopy). Includes bibliographical references (leaves 75-77). Also available on the World Wide Web.

Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 227-250). Also available on the World Wide Web.

The United States Forest Service (USFS) Forest Inventory and Analysis (FIA) program provides a diverse selection of data used to assess the status of the nation’s forested areas using sample locations dispersed throughout the country. Airborne, and more recently, terrestrial lidar (light detection and ranging) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize three-dimensional vertical forest structure. This study investigates the potential of airborne and terrestrial scanning lidar systems for modeling forest volume and aboveground biomass on FIA subplots in the Malheur National Forest, eastern Oregon. A methodology for the creation of five airborne lidar metric sets (four point cloud-based and one individual tree based) and four terrestrial lidar metric sets (three height-based and one distance-based) is presented. Metrics were compared to estimates of subplot aboveground biomass and gross volume derived from FIA data using national and regional allometric equations respectively. Simple linear regression models from the airborne lidar data accounted for 15 percent of the variability in subplot biomass and 14 percent of the variability in subplot volume, while multiple linear regression models increased these amounts to 29 percent and 25 percent, respectively. When subplot estimates of biophysical parameters were scaled to the plot-level and compared with plot-level lidar metrics, simple linear regression models were able to account for 60 percent of the variability in biomass and 71 percent of the variation in volume. Terrestrial lidar metrics produced moderate results with simple linear regression models accounting for 41 percent of the variability in biomass and 46 percent of the variability in volume, with multiple linear regression models accounting for 71 percent and 84 percent, respectively. Results show that: (1) larger plot sizes help to mitigate errors and produce better models; and (2) a combination of height-based and distance-based terrestrial lidar metrics has the potential to estimate biomass and volume on FIA subplots.

Review of forestry policy and deforestation in the Democratic Republic of the Congo, with a look at REDD, national parks, forest certification systems, non-timber forest products (NTFPs) and a focus on community-based conservation.

Thesis (M.S.)--University of Toledo, 2009. / Typescript. "Submitted as partial fulfillment of the requirements for the Master of Science in Biology." "A thesis entitled"--at head of title. Bibliography: leaves 48-55.

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains viii, 138 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 105-108).

Thesis (M.S.)--Oregon State University, 2003. / Typescript (photocopy). Includes bibliographical references. Also available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 2003. / Includes bibliographical references (leaves 145-160).