http://hdl.handle.net/1957/10902 Graduate student research from the Forest Engineering, Resources and Management Department 2013-05-25T21:42:10Z http://hdl.handle.net/1957/37902 Keying forest stream protection to aquatic ecosystem values in multi-ownership watersheds Pickard, Brian R. Forested lands of western Oregon provide aquatic habitat for many fish and riparian dependent species, including a wide variety of salmon species. Current policies set riparian protections using fixed buffers on streams for federal and private lands based on stream type or size. These buffers can create a series of disjointed riparian protections, as federal lands require buffers that are much larger than private lands. In addition, the fixed buffer approach is neither flexible nor tailored to aquatic ecosystem values. This thesis presents a framework for comprehensively assessing stream networks using site specific watershed features and then suggests riparian conservation strategies that key stream and riparian protection to aquatic ecosystem values. Seven study watersheds were used in this analysis, totaling over 2.5 million acres of forested lands in western Oregon. Employing a set of geospatial tools, called NetMap, streams in each watershed were classified into higher and lower priorities using criteria of intrinsic potential, erosion/debris flow susceptibility, and thermal loading potential. Results demonstrated the inherent variability within and among watersheds based on the geomorphic and ecological processes determined important for selected salmon species. Within each watershed, both federal and non-federal lands had many miles of higher priority fish-bearing and non-fish bearing streams, suggesting the need for comprehensive, holistic watershed conservation strategies. Based on the partitioning of streams into higher and lower priorities, an alternative riparian conservation strategy was then modeled for federal lands that allocate protection on the basis of the ecological context of a stream segment’s potential and particular location while still meeting federal aquatic conservation goals and objectives. Possible increases to the land base for long-term timber production were then identified if this strategy were applied to federal Matrix lands. Results demonstrated that 8-30 percent of the current riparian buffers could be reallocated to the land base for long-term timber production. An additional 26-45 percent of current buffers could be managed simultaneously for both timber production and aquatic ecosystem goals. Results also provided a framework for targeting of conservation and restoration efforts towards higher priority streams within each watershed. As many of the most ecologically important streams were located on non-federal lands, riparian conservation policies focused on streams classified as higher priority on those lands may be needed to protect aquatic species and their environments. Graduation date: 2013 2013-03-15T00:00:00Z http://hdl.handle.net/1957/37209 Evaluation of six tools for estimating woody biomass moisture content Becerra Ochoa, Fernando Amador Woody biomass transportation costs and market values/costs are strongly correlated with the woody biomass moisture content. Properly managing moisture content can potentially lead to economic and environmental advantages in biomass energy markets. Good management requires accurate moisture content measurements. Therefore, availability of accurate, precise, reliable, and efficient tools to assess woody biomass moisture content is essential. In this study, six different tools (Fibre-Gen HM200, IML Hammer, Humimeter BLW, Timbermaster, Humimeter HM1 and Wile Bio Meter) were evaluated. The six tools employed three different measurement technologies; acoustic, conductance, and capacitance. Woody biomass samples were collected over one season (summer 2011) at three different locations in western Oregon (Corvallis, Dallas, and Clatskanie) for three softwood species and three hardwood species: Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), Ponderosa pine (Pinus ponderosa L.), western hemlock (Tsuga heterophylla (Raf.) Sarg.), hybrid poplar (Populus spp.), Madrone (Arbutus spp.), and Garryana Oak (Quercus garryana Dougl. ex Hook). Twenty 3-meter long log (20 to 400mm diameter) specimens were collected per species; 18 specimens were divided into two different treatments (open vs. covered), and the two remaining specimens were chipped. In addition, approximately 100 kilograms per species of hogfuel (limbs and tops) were collected and chipped. Moisture content measurements of logs, chips, and hogfuel were made regularly over a four month period. These data was used to develop multiple linear regression models for assessing the moisture content of the six species using the six tools. The major factors considered in the regression models were species (6), treatment (2), and tools (6). The data were also used to estimate the sample size needed for each tool. The best tool from each technology type was identified. The results generated from this study show that (1) none of the tools are accurate without calibration for different species, (2) the best model/tool combination could only explain about 80% of the variability in measurements, (3) further product development is required in some cases to ensure that the tools are robust for industrial application, and (4) there is a wide range in efficiency of the tools (i.e., 50 minute tool efficiency range). The Fibre-Gen HM200 and Wile Bio Meter were the most accurate, precise and efficient tools tested. The cost of transporting woody biomass from the forest to woody biomass plants is "optimized" when the moisture content drops to approximately 30% (wet basis). Validation of the models developed for three of the tools tested (Fibre-Gen HM200, Humimeter BLW and the Wile Bio Meter) indicates that the tools are accurate below 35% MC (wet basis). This suggests they could be used for making threshold transportation decisions, i.e., determining when to haul. Graduation date: 2013 2012-12-13T00:00:00Z http://hdl.handle.net/1957/35917 Initial attack fire suppression, spatial resource allocation, and fire prevention policy in California, the United States, and the Republic of Korea Lee, Yohan In this dissertation, I combined a scenario-based, standard-response optimization model with a stochastic simulation model to improve the efficiency of the deployment of initial attack firefighting resources on wildland fires in California and the Republic of Korea. The optimization model minimizes the expected number of fires that do not receive a standard response—defined as the number of resources by type that must arrive at the fire within a specified time limit—subject to budget and station capacity constraints and uncertainty about the daily number and location of fires. The simulation model produces a set of fire scenarios in which a combination of fire count, fire locations, fire ignition times, and fire behavior occur. Compared with the current deployment, the deployment obtained with optimization shifts resources from the planning unit with the highest fire load to the planning unit with the highest standard response requirements. Resource deployments that result from relaxing constraints on station capacity achieve greater containment success by encouraging consolidation of resources into stations with high dispatch frequency, thus increasing the probability of resource availability on high fire count days. I extended the standard response framework to examine how a policy priority influences the optimal spatial allocation and performance of initial attack resources. I found that the policy goal of a fire manager changes the optimal spatial allocation of initial attack firefighting resources on a heterogeneous landscape, especially, for the socio-economic value of a potential fire location. Furthermore, I investigated the tradeoff between the number of firefighting resources and the level of fire ignition prevention efforts mitigating the probability of human-made fires in the Republic of Korea where most fires are caused by human activities. I found that fire ignition prevention is as cost-effective as initial attack resources given the current budget in the Republic of Korea on reducing the expected number of fires not receiving the standard response. From the comparison of the California and Republic of Korea cases, I can identify "rules of thumb" to be followed when allocating IA resources in particular ecological and policy settings. Graduation date: 2013 2012-11-26T00:00:00Z http://hdl.handle.net/1957/35061 Evaluating the accuracy of imputed forest biomass estimates at the project level Gagliasso, Donald Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. Previous research has shown that nearest-neighbor imputation methods can accurately estimate forest volume across a landscape by relating variables of interest to ground data, satellite imagery, and light detection and ranging (LiDAR) data. Alternatively, parametric models, such as linear and non-linear regression and geographic weighted regression (GWR), have been used to estimate net primary production and tree diameter. The goal of this study was to compare various imputation methods to predict forest biomass, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. In this study I compared the predictive performance of, 1) linear regression, GWR, gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest and 2) MSN and k-nn when imputing forest biomass at spatial scales ranging from 5,000 to 50,000 acres. To test the imputation methods a combination of ground inventory plots, LiDAR data, satellite imagery, and climate data were analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k=5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k=3), followed by the GWR model, and the random forest imputation. The GNN method was the least accurate. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k=5), followed by k-nn (k=3), and the random forest method. The GNN method, again, was the least accurate. The accuracy of MSN, the current imputation method used by the Malheur Nation Forest, and k-nn (k=5), the most accurate imputation method from the second chapter, were then compared over 6 spatial scales: 5,000, 10,000, 20,000, 30,000, 40,000, and 50,000 acres. The root mean square difference (RMSD) and bias were calculated for each of the spatial scale samples to determine which was more accurate. MSN was found to be more accurate at the 5,000, 10,000, 20,000, 30,000, and 40,000 acre scales. K-nn (k=5) was determined to be more accurate at the 50,000 acre scale. Graduation date: 2013 2012-10-01T00:00:00Z