Three studies on applying Positive Mathematical Programming and Bayesian Analysis to model US crop supply

The purpose of this dissertation is to find a practical way of obtaining a reasonable crop supply model for the US using a limited dataset. This model can then be used for forecasting and impact modeling. The method that is central to this model is Positive Mathematical Programming (PMP) that allows for the calibration of a nonlinear programming model to mimic the observations. This method is improved by implementing Bayesian Analysis to allow for the model to consider a distribution for the supply elasticity. Using this method a national model was formed using only five years of data. While there were difficulties in forming a posterior density through manipulation of parameters, the Metropolis Hastings Algorithm ultimately allowed for the density to be simulated. Once the posterior data is simulated, a reasonable forecast could be made using this model. This model was then improved by disaggregating the national model into a regional model. This was done through an additional variable (which is the percentage of national price responsiveness for a crop in a region) to consider in the prior density. Ultimately, regional results and elasticities are formed and the overall forecasting was improved. Once the national and regional models have been formed, the models were tested under a variety of impact models. The response to the change in price for crops as well as yield changes in a region were done and reasonable results were found. Overall, a crop supply model was formed that produced reasonable elasticities and forecasted accurate results, thanks in part to a Bayesian approach which view parameters as distributions in the model

Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, carbon storage in moderate to high biomass forests is difficult to estimate with conventional optical or radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar measured canopy structure and coincident field measurements of forest stand structure at five locations in the Pacific Northwest of the U.S.A. with contrasting composition. Coefficient of determination values (r2) ranged between 41% and 96%. Correlations for two important variables, LAI (81%) and above ground biomass (92%), were noteworthy, as was the fact that neither variable showed an asymptotic response. Of the 17 stand structure variables considered in this study, we were able to develop eight equations that were valid for all sites, including equations for two variables generally considered to be highly important (aboveground biomass and leaf area index). The other six equations that were valid for all sites were either related to height (which is most directly measured by lidar) or diameter at breast height (which should be closely related to height). Four additional equations (a total of 12) were applicable to all sites where either Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla) or Sitka spruce (Picea sitchensi) were dominant. Stand structure variables in sites dominated by true firs (Abies sp.) or ponderosa pine (Pinus ponderosa) had biases when predicted by these four additional equations. Productivity-related variables describing the edaphic, climatic and topographic environment of the sites where available for every regression, but only two of the 17 equations (maximum diameter at breast height, stem density) incorporated them. Given the wide range of these environmental conditions sampled, we conclude that the prediction of stand structure is largely independent of environmental conditions in this study area. Most studies of lidar remote sensing for predicting stand structure have depended on intensive data collections within a relatively small study area. This study indicates that the relationships between many stand structure indices and lidar measured canopy structure have generality at the regional scale. This finding, if replicated in other regions, would suggest that mapping of stand structure using lidar may be accomplished by distributing field sites extensively over a region, thus reducing the overall inventory effort required

Hydrogen isotope behavior during rhyolite glass hydration under hydrothermal conditions

The diffusion of molecular water (H2Om) from the environment into volcanic glass can hydrate the glass up to several wt% at low temperature over long timescales. During this process, the water imprints its hydrogen isotope composition (δDH2O) to the glass (δDgl) offset by a glass-H2O fractionation factor (ΔDgl-H2O = δDgl – δDH2O) which is approximately -33‰ at Earth surface temperatures. Glasses hydrate much more rapidly at higher, sub-magmatic temperatures as they interact with H2O during eruption, transport, and emplacement. To aid in the interpretation of δDgl in natural samples, we present hydrogen isotope results from vapor hydration experiments conducted at 175–375 oC for durations of hours to months using natural volcanic glasses. The results can be divided into two thermal regimes: above 250 oC and below 250 oC. Lower temperature experiments yield raw ΔDgl-H2O values in the range of -33 ± 11‰. Experiments at 225 oC using both positive and negative initial ΔDgl-H2O values converge on this range of values, suggesting this range represents the approximate equilibrium fractionation for H isotopes between glass and H2O vapor (103lnαgl-H2O) below 250 oC. Variation in ΔDgl-H2O (-33 ± 11‰) between different experiments and glasses may arise from incomplete hydration, analytical uncertainty, differences in glass chemistry, and/or subordinate kinetic isotope effects. Experiments above 250 oC yield unexpectedly low δDgl values with ΔDgl-H2O values of ≤–85‰. While alteration alone is incapable of explaining the data, these run products have more extensive surface alteration and are not interpreted to reflect equilibrium fractionation between glass and H2O vapor. Fourier transform infrared spectroscopy (FTIR) shows that glass can hydrate with as much as 5.9 wt% H2Om and 1.0 wt% hydroxl (OH-) in the highest P-T experiment at 375 oC and 21.1 MPa. Therefore, we employ a 1D isotope diffusion– reaction model of glass hydration to evaluate the roles of equilibrium fractionation, isotope diffusion, water speciation reactions internal to the glass, and changing boundary conditions (e.g. alteration and dissolution). At lower temperatures, the best fitting model results to experimental data for low silica rhyolite (LSR) glasses require only an equilibrium fractionation factor and yield 103lnαgl-H2O values of -33‰± 5‰and -25‰± 5‰at 175 oC and 225 oC, respectively. At higher temperatures, ΔDgl-H2O is dominated by boundary layer effects during glass hydration and glass surface alteration. The modeled bulk δDgl value is highly responsive to changes in the δDgl boundary condition regardless of the magnitude of other kinetic effects. Observed glass dissolution and surficial secondary mineral formation are likely to impose a disequilibrium boundary layer that drives extreme δDgl fractionation with progressive glass hydration. These results indicate that the observed ΔDgl-H2O of ~-33 ± 11‰ can be cautiously applied as an equilibrium 103lnαgl-H2O value to natural silicic glasses hydrated below 250 oC to identify hydration sources. This approximate ΔDgl-H2O may be applicable to even higher temperature glasses hydrated on short timescales (of seconds to minutes) in phreatomagmatic or submarine eruptions before H2O in the glass is primarily affected by boundary layer effects associated with alteration on the glass surface

Evaluating Aster Satellite Imagery And Gradient Modeling For Mapping And Characterizing Wildland Fire Fuels

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite and gradient modeling for mapping fuel layers for fire behavior modeling within FARSITE. An empirical model, based upon field data and spectral information from an ASTER image, was employed to test the efficacy of ASTER for mapping and characterizing canopy closure and crown bulk density. Surface fuel models (NFFL 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories

Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR’s ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in Li- DAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance. Vegetation structure was found to have no influence, whereas increased variability in the vertical error was observed on slopes exceeding 30°, illustrating that these algorithms are not limited by high-biomass western coniferous forests, but that slope and sensor accuracy both play important roles. The observed vertical DEM error translated into ±1%–3% error range in derived timber volumes, highlighting the potential of LiDAR-derived inventories in forest management

Lake Breezes in Southern Ontario and Their Relation to Tornado Climatology

Geostationary Operational Environmental Satellite (GOES) imagery is used to demonstrate the development of lake-breeze boundaries in southern Ontario under different synoptic conditions. The orientation of the gradient wind with respect to the shorelines is important in determining the location of such lines. When moderate winds (5–10 m s21) are parallel to straight sections of coastlines, cloud lines can extend well inland. In the region between Lakes Huron and Erie lake-breeze lines merge frequently, sometimes resulting in long-lasting stationary storms and attendant heavy rain and flooding. The influence of the lakes is apparent in the tornado climatology for the region: tornadoes appear to be suppressed in regions visited by lake-modified air and enhanced in regions favored by lake-breeze convergence lines. The cloud patterns in the case of a cold front interacting with merging lake-breeze boundaries are shown to be similar to those on a major tornado outbreak day. Two of the cases discussed are used as conceptual models to explain many of the features in the patterns of tornado touchdown locations. In general, it appears that the lakes suppress tornadoes in southern Ontario, compared with neighboring states and in particular in areas where southwest winds are onshore, but enhance tornado likelihood locally in areas of frequent lake-breeze activity. 1