Quaking aspen (Populus tremuloides Michx.) is the most widespread deciduous tree species in North America and aspen ecosystems are highly valued for multiple use, being noted for forage production, understory diversity, wildlife habitat, timber, hydrological assets, and aesthetics. However, aspen communities in the Intermountain Region of the western United States are in evident decline, with certain areas experiencing rapid mortality over the past decade. One location of special interest is the quaking aspen on Cedar Mountain in Southern Utah, USA, an isolated population in the southwestern portion of aspen's geographic range. Lacking critical information on the location, extent, and magnitude of declining stands, land managers could utilize detailed spatial information to manage aspen on Cedar Mountain. To inform land managers of Cedar Mountain and develop methodologies applicable for aspen landscapes across the Intermountain West, a spatially explicit aspen stand type classification using multi-spectral imagery, digital elevation models, and ancillary data was produced for the 27,216-ha pilot study area. In addition, a statistical analysis was performed to assess the relationships between landscape parameters derived from the geospatial information (i.e. slope, aspect, elevation) and aspen on the Cedar Mountain landscape. A supervised classification composed of three aspen stand types (1-healthy, 2- damaged, 3-seral) was produced using Classification and Regression Tree (CART) analysis and validated using National Agriculture Imagery Program (NAIP) imagery. Within Cedar Mountain aspen cover, classification estimates were 49%, 35%, and 16% for healthy, damaged, and seral aspen stand types, respectively. Validation measures yielded an overall accuracy measure of 81.3%, (KHAT=.69, n = 446). Important landscape metrics for the three health classes were found to be significantly different. Damaged stands were found primarily at lower elevations on south-to-west (drier) aspects. Within the aspen elevation range, the mean elevation of damaged stands (2,708 m) was significantly lower than that of the mean elevation of healthy stands (2,754 m). Aspect (moisture index) was also significantly different, with damaged stands primarily on southerly (drier) aspects and healthy stands generally on northerly (wetter) aspects. Slope, however, was not found to be significantly different among aspen types.

It has become very crucial to manage water resources to meet the needs of the growing population. In irrigation command areas, and in order to build a better plan to manage service delivery from canals and reservoirs, it is important to build appropriate knowledge of water needs on a field basis. There is often a lag between the order and delivery of water to the field. Knowledge of the crop water requirement at the field level helps the decision maker to make the right choices leading to more efficient handling of the available water. The purpose of this study was to develop methodologies and tools that allow better management of irrigation water and water delivery systems, such as machine learning models that can be used as tools for decision support systems of water management. To achieve better modeling and prediction, wavelet decompositions were explored for their ability to give information about time and frequency changes in the data. Remote sensing approaches were also used for their ability to quantify water requirements at the spatial level. Therefore, this dissertation explored the use of the above-mentioned data tools and techniques to address water management problems. The framework of this dissertation consisted of three components that provide tools to support irrigation system operational decisions. In general, the results for each of the methods developed were satisfactory, relevant, and encouraging. They provided significant potential for improving decision making for real-time applications in irrigation command areas and better management of the water resources.

A longstanding source of uncertainty within the climate system is our understanding of clouds and their response to aerosols. The resulting cloud optical property changes constitute the largest uncertainty in our understanding of 20th century climate change. Central to being able to monitor and better understand the effects aerosols composition, size and concentration have on cloud reflectivity are accurate observations of the cloud droplet number concentration. Cloud droplet number concentrations couple aerosol properties to changes in cloud brightness. In the first portion of this dissertation, I present the development and evaluation of two techniques for observing cloud properties. The first is a new method of observing cloud droplet number concentration that uses polarimetric measurements and requires relatively few assumptions. The theoretical derivation is first presented followed by a method of implementation using NASA’s airborne Research Scanning Polarimeter (RSP). I use data obtained during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). Comparing cloud droplet number concentration retrievals with in situ measurements made by a cloud droplet probe during NAAMES shows strong agreement between measurements over a range of meteorological conditions and cloud types. Multilayered clouds are ubiquitous within Earth’s atmosphere, yet detecting their presence and height has been a longstanding challenge for passive remote sensing instruments. Retrieving the cloud top height is also an important part of the droplet concentration retrieval, and detecting the presence of multilayered clouds supports interpreting results. For this second technique, I present an assessment of RSP cloud top height retrievals, which are based on the concept of parallax. By comparing RSP cloud top height retrievals to the Cloud Physics Lidar (CPL), the technique is found to be capable of determining the presence and heights of up to three cloud layers, which is innovative for a passive remote sensing instrument. A second element essential to addressing the uncertainty in cloud’s response to aerosols is to better understand processes and drivers of cloud properties. Air-campaign studies offer opportunities to study high temporal and spatial resolution measurements that are needed to better understand the complex processes between aerosols, clouds and meteorological properties. My final investigation uses the two developed cloud property retrievals, in conjunction with other in situ and remotely sensed data, to undertake a broad investigation quantifying connections observed between aerosols, clouds and meteorology. I find a well- defined link between cloud microphysical property changes and marine biogenic aerosol concentrations. Changes in cloud properties are consistent with the Twomey effect, whereby an increase in cloud condensation nuclei is associated with increases in droplet concentrations and decreased droplet sizes. I also observe complex, non-linear secondary effects of aerosols on clouds such as cloud thinning and decreased droplet distribution width. I conclude this study by integrating my findings and discussing plausible linkages between aerosol, cloud and meteorological properties within the context of existing concepts.

This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.

Unprecedented rates of modern species extinction present a serious challenge in the field of conservation biology. While protected areas (PAs) are regarded as key tools to reduce rates of biodiversity loss, it is unclear to what degree PAs can maintain their ecological integrity while experiencing external pressures from outside of their boundaries. Satellite remote sensing essential biodiversity variables (SRS-EBVs) are indicators of biodiversity that can be produced with large spatial coverages and can be used to measure PAs’ capacity to preserve important ecological elements for biodiversity. In this study, I used SRS-EBVs representative of ecosystem structure and function, including productivity, disturbance regimes, ecosystem extent, and ecosystem composition. I tested if PAs preserved these determinants of species survival through time, whether any changes in these variables in PAs were independent of changes in their surrounding areas (buffer zones), and if the management type of PAs influenced either of these patterns. I found that PAs maintained elements of ecosystem structure, including habitat heterogeneity and extent, inside of their boundaries, regardless of changes that occurred in their surroundings. In contrast, PAs were less effective at sustaining elements of ecosystem function and mitigating other forms of human disturbance. Productivity within PAs was the same as that of their surroundings, underscoring the inability of PAs to track shifts in climate regimes that put some species at greater risk of extinction. Fire disturbance trends were maintained across PA boundaries; however, the causes of these fires are unknown, highlighting the importance of supplemental fire census data to tease apart the trends of natural fire regimes compared to harmful burns. Finally, other human pressures thought to be the indirect effects of linear transportation features (ex. edge effects from roads) were observed to have spilled over from buffer zones into PAs. Planning for future development of the global PA network can benefit greatly from the application of SRS-EBVs. Pairing these data products with foundational ecological conservation principles can build a stronger, more efficient PA network for the preservation of Earth’s species.

Indiana University-Purdue University Indianapolis (IUPUI) / Geospatial analysis is a generic term describing several technologies or methods of computational analysis using the Earth as a living laboratory. These methods can be implemented to assess risk and study preventative mitigation practices for Public Health. Through the incorporation Geographic Information Science and Remote Sensing tools, data collection can be conducted at a larger scale, more frequent, and less expensive that traditional in situ methods. These techniques can be extrapolated to be used to study a variety of topics. Application of these tools and techniques were demonstrated through Public Health research. Although it is understand resolution, or scale, of a research project can impact a study’s results; further research is needed to understand the extent of the result’s bias. Extreme heat vulnerability analysis was studied to validate previously identified socioeconomic and environmental variables influential for mitigation studies, and how the variability of resolution impacts the results of the methodology. Heat was also investigated for the implication of spatial and temporal resolution, or aggregation, influence on results. Methods studying the physical and socioeconomic environments of Dengue Fever outbreaks were also studied to identify patters of vector emergence.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20694号 / 工博第4391号 / 新制||工||1682(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 米田 稔, 教授 清水 芳久, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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