Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Assessing potential impacts of logging and road construction on the soil and water resources in a semi-primitive area(Montana State University - Bozeman, College of Agriculture, 1973) Aasheim, Ronald JayItem National forest timber harvest variability(Montana State University - Bozeman, College of Agriculture, 1984) Forrester, Albert AyersIt has long been the goal of the United States Forest Service to stabilize timber dependent communities via a sustained yield, even-flow of timber from the National Forests. This policy has been based upon the assumption that timber markets have a destabilizing impact on these communities since private timber operators harvest at varying rates. This paper examines the question of whether or not private harvests are more variable than Forest Service harvests. Statistically, it is shown that Forest Service harvests are not stable and that private harvests are much less variable than national forest harvests. The focus of the paper then turns to an explanation of the variability in Forest Service harvests. Timber sales policies and the Forest Service contract are given as two possible sources of this variability. Regression analysis shows that, for the most part, timber harvests are not significantly related to sales and that apparently there is enough slack in the timber contracts to allow operators time to alter harvest rates according to changes in the economy. Econometric analysis shows that harvests do respond to changes in the economy. Thus harvest variability is not solely due to variability in Forest Service timber sales. Because of the apparent lack of rigidity in timber contracts, evidenced by contract extension, termination, alteration, and slack in the contract period, it is proposed that firms harvesting national forest timber will behave differently than firms harvesting under private contracts. Specifically, it is proposed that firms reduce harvest rates dramatically when prices fall, perhaps ceasing operations altogether, and increasing harvest rates when prices rise. Econometric analysis shows that such behavior in national forest timber supply is present. The evidence provides a partial explanation of national forest harvest variability.Item Comparison of three remote sensing techniques to measure biomass on CRP pastureland(Montana State University - Bozeman, College of Agriculture, 2013) Porter, Tucker Fredrick; Chairperson, Graduate Committee: Bok SowellBiomass from land enrolled into CRP is being considered as a biofuel feedstock source. For sustainable production, harvesting, and soil protection, technology is needed that can quickly, accurately and non-destructively measure biomass. Remote sensing of vegetation spectral responses, which tend to be highly responsive to changes in biomass, may provide a means for inexpensive, frequent, and non-destructive measurements of biomass at management relevant scales. A valuable resource for land managers would be a biomass measurement model that could non-destructively measure biomass at different phenological growth stages across multiple growing seasons. The objective of this study was to compare remote sensing-based biomass measurement models using the normalized difference vegetation index (NDVI) and bandwise regression remote sensing techniques to determine which model best measures biomass at different phenological growth stages over multiple growing seasons on CRP pastureland in central Montana. Biomass and plant spectral response measurements were collected over the 2011 (n = 108) and 2012 (n = 108) growing seasons on an 8.1 ha CRP pasture. Measurements were stratified by phenological growth stage and growing season. Half of the data was used to build each measurement model and the other half was used to test the power of each model to measure biomass. Remote sensing-based biomass measurement models were constructed using NDVI measurements from an active ground-based sensor, NDVI measurements from Landsat images, and band combination measurements from Landsat images. All biomass measurement models showed no difference between actual and estimated biomass values (p-value > 0.05). The biomass measurement model using NDVI measurements from Landsat images had the smallest margin of difference between estimated biomass and actual biomass (22 kg/ha + or - 96 kg/ha), followed by the combination of individual spectral bands from Landsat images (128 kg/ha + or - 71 kg/ha), and NDVI measurements from a ground based sensor (182 kg/ha + or - 94 kg/ha). Results indicate remote sensing-based biomass measurement models are accurate at measuring biomass at different phenological growth stages across multiple growing seasons. Land managers can implement remote sensing-based biomass measurement models into their land management strategies to quickly, accurately, and non-destructively measure biomass across a landscape.