The twin city area of Nogales, Arizona and Nogales, Sonora, Mexico, known collectively as Ambos (both) Nogales, has experienced a common borderland history of urban growth presumably based on changes in policy and economic incentives. This research documents changes through time in an attempt to identify colonia (settlement) development and patterns along the U.S.-Mexico Border, combining a community participation approach with remote sensing analyses, to create an online mapping service. This study outlines a planning approach that is meant to promote sustainable development in the future, integrating both sides of the border.Urban area classifications for this watershed were created from images for early summer 1975, 1983, 1996 and 2002 as part of a research project to monitor colonias growth performed under a grant from the U.S. Department of Housing and Urban Development. This dataset was used as input to the Clarke urban growth model, called SLEUTH, to predict land use changes to the year 2030.Erosion-sedimentation models were applied to generate simulations of potential sources and sinks in the watershed. The Universal Soil Loss Equation (USLE), an empirical formula used to predict potential average annual soil loss in tons per acre per year (t/a/y), was applied to the study area. In order to calculate the location specific net sediment delivery in the watershed, the Spatially Explicit Delivery Model (SEDMOD) was employed, to quantify the amount of sediment that is deposited. In an effort to forecast the effects of urban development in 2030 on downstream water quality, changes predicted in urban growth by the SLEUTH model were retrofit to the erosion-sedimentation models.Using techniques designed to protect the previously identified erosion 'hot spots', alternate scenarios were generated, depicting better water quality possibilities if these guidelines could be adhered to. In this study, I provide (1) a new methodology for assessing future erosion impacts in urbanizing watersheds, (2) a quantification of urban sprawl and its implications for water quality, and (3) the generation of alternative future scenarios for management of downstream sedimentation.

Land surface albedo plays a key role in the surface-atmosphere internaction, because it greatly influences the shortwave radiation absorbed by the surface. Surface albedo depends on soil characteristics and vegetation types. Error in the specification of albedos of soil and vegetation may cause biases in the computation of ground temperature and surface fluxes, therefore accurate albedo estimates are essential for an accurate simulation of the Earth's climate. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterization in weather forecast and climate models as well as the remote sensing retrieval of surface solar fluxes through a series of three papers. First, the NCAR Community Climate System Model (CCSM2) albedo is evaluated using the MODIS BRDF and albedo data. The model and MODIS albedo differences are related to the deficiences in the model simulation of snow cover and soil moisture and in the model's specification of leaf and stem area indexes. They are also partially caused by the deficiency of the two-stream method. Second, motivated by these analyses, a new formulation for surface albedo is developed. Over desert, most land models assume that the bare soil albedo is a function of soil color and soil moisture but independent of solar zenith angle (SZA). However, analysis of MODIS BRDF/albedo data and in situ data indicates that bare soil albedo does vary with SZA. Furthermore this SZA dependence is found to affect the surface energy fluxes and temperature in the offline land surface model sensitivity tests. Finally, the MODIS BRDF algorithm is reformulated to derive a new two-parameter scheme for the computation of land surface albedo and its SZA dependence for use in weather and climate models as well as the remote sensing retrieval of surface solar fluxes. In this formulation, the season- and pixel-dependent black-sky albedo at 60 deg SZA can be directly prescribed using the MODIS BRDF data while the two parameters are taken as a function of vegetation type only. Comparison of this formulation with those used in weather, climate, and data assimilation models (at NCAR, NCEP, and NASA) as well as those used in remote sensing groups (University of Maryland, ISCCP-FD, and CERES/TRMM) reveals the deficiencies in the land surface albedo treatment in these models and remote sensing retrieval algorithm along with suggestions for improvement.

The Province of British Columbia is developing a suite of attributes to assess and monitor forest sustainability. Each attribute is in turn evaluated using a variety of indicators. Recently, digital remote sensing technologies have emerged as both alternative and supplement to traditional monitoring techniques, with light detection and ranging (lidar) in particular showing great promise for estimating a variety of indicators. The goal of this thesis was to review and assess the ability of lidar to estimate selected indicators of forest sustainability. Specifically, digital elevation model (DEM) interpolation (from which indicators are extracted both directly and indirectly) and wildlife tree class distributions were examined. Digital elevation models are a key derivative of lidar data, and their generation is a critical step in the data processing stream. A validation exercise was undertaken to determine which combination of interpolation routine and spatial resolution was the most accurate. Ground returns were randomly subsetted into prediction and validation datasets. Linear, quintic, natural neighbour, spline with tension, regularized spline, inverse distance weighting, and ANUDEM interpolation routines were used to generate surfaces at spatial resolutions of 0.5, 1.0, and 1.5 m. The 0.5 m natural neighbour surface was found to be the most accurate (RMSE=0.17 m). Classification and regression tree analysis indicated that slope and ground return density were the best predictors of interpolation error. The amount and variability of living and dead wood in a forest stand is an important indicator of forest biodiversity. In the second study, the capacity of lidar to estimate the distribution of living and dead trees within forests is investigated. Twenty-two field plots were established in which each stem (DBH>10cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal logistic regression was then employed to predict the cumulative proportions of stems within the WT classes. Results indicated that the coefficient of variation of the lidar height data was the best predictor variable (r = 0.85, p <0.000, RMSE = 4.9%). The derived relationships allowed for the prediction of the proportion of stems within WT classes across the landscape.

<p> Landslides pose a persistent threat to El Salvador's population, economy and environment. Government officials share responsibility in managing this hazard by alerting populations when and where landslides may occur as well as developing and enforcing proper land use and zoning practices. This thesis addresses gaps in current knowledge between identifying precisely when and where slope failures may initiate and outlining the extent of the potential debris inundation areas. Improvements on hazard maps are achieved by considering a series of environmental variables to determine causal factors through spatial and temporal analysis techniques in Geographic Information Systems and remote sensing. The output is a more dynamic tool that links high resolution geomorphic and hydrological factors to daily precipitation. Directly incorporable into existing decision support systems, this allows for better disaster management and is transferable to other developing countries.</p>

We analyze the solar wind interaction with Mars through examination of Mars Global Surveyor Magnetometer/Electron Reflectometer (MGS MAG/ER) data. We focus on data in the postshock, dayside plasma flow, especially the Magnetic Pileup Boundary (MPB). First, we present the data to characterize the features of the MPB. Next, we argue the evidence that the MPB is formed through a series of processes, beginning with electron impact ionization of planetary neutrals in the exosphere of Mars. These new ions form an unstable population of energetic ions in the post-shock flow. Ion cyclotron waves are established to scatter the ion distribution into one more stable. This removes thermal pressure from the ions. In order to maintain a constant total pressure in the flow, the magnetic field intensifies, transferring the lost ion thermal pressure into magnetic energy. Also, we develop a model of the electron impact ionization process in the martian exosphere. This model calculates the evolution of the electron distribution function as the flow encounters exospheric planetary neutrals. It reproduces the electron spectrum observed by the ER in the MPB very well. Therefore, we conclude that electron impact ionization is the process responsible for the onset of magnetic pileup in the postshock flow at Mars.

This research examines the potential to use passive microwave remote sensing for measuring soil moisture extremes that impact agricultural areas in Canada. A validation was made of three passive microwave remote sensing soil moisture data sets, with weekly averaged values from the Land Parameter Retrieval Model (LPRM) applied to AMSR-E C/X-Band data providing the most accurate results (root mean squared error of 5 to 10%). A further evaluation of this data set against a spatially distributed in situ soil moisture network in Alberta suggests that this data set may be less accurate in regions where dense vegetation or open water is present, particularly on the northern edges of the Canadian agricultural extent. A method to derive soil moisture anomalies was developed that uses homogenous regions to spatially aggregate soil moisture statistics to compensate for a short satellite data record. It was found that these anomalies can be estimated with errors of less than 5% when these regions are 15 pixels or more over a seven year time period. Surface soil moisture anomalies from LPRM showed weak but significant relationships to precipitation based drought indices, suggesting promise for using these anomalies for wider soil moisture extremes monitoring. Soil moisture anomalies from CLASS and in situ networks showed inconsistencies with LPRM anomalies in how they capture soil moisture conditions that are relevant to agricultural yield.. These data sets overall show that this approach to quantifying extremes has potential, but improvement to soil moisture retrieval from LPRM and CLASS, and an integration of the information they provide are needed to optimize these data sets for agricultural monitoring. / National Science and Engineering Research Council, Agriculture and Agri-Food Canada, Canadian Space Agency

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No description available.

Accurate information on the spatial-temporal distributions of air pollution at a regional scale is crucial for effective air quality control, as well as to impact studies on local climate and public health. The current practice of mapping air quality relies heavily on data from monitoring stations, which are often quite sparse and irregularly spaced. The research presented in this dissertation seeks to advance the methodologies involved in spatiotemporal analysis of air quality that integrates remotely-sensed data and in situ measurement. Aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is analyzed to estimate fine particulate matter (PM2.5) concentrations as the target air pollutant. The spatial-temporal distribution of columnar aerosol loading is investigated through mapping MODIS AOD in southern Ontario, Canada throughout 2004. Clear distribution patterns and strong seasonality are found for the study area. There is a detectable relationship between an AOD level and underlying land use structure and topography on the ground. MODIS AOD was correlated with the ground-level PM2.5 concentration (GL-[PM2.5]) at various wavelengths. The AOD-PM2.5 correlation is found to be sensitive to spatial-temporal scale changes. Further, a semi-empirical model has been developed for a more accurate prediction of GL-[PM2.5]. The model employs MODIS AOD data, assimilated meteorological fields, and ground-based meteorological measurements and is able to explain 65% of the variability in GL-[PM2.5]. To achieve a more accurate and informative spatiotemporal modelling of GL-[PM2.5], a method is proposed that integrates the model-predictions and in situ measurements in the framework of Bayesian Maximum Entropy (BME) analysis. A case study of southern Ontario demonstrates the procedures of the method and support for its advantages by comparison with conventional geostatistical approaches. The BME estimation, coupled with BME posterior variance, can be used to depict GL-[PM2.5] distribution in a stochastic context. The methodologies covered in this work are expected to be applicable to the modelling or analysis of other types of air pollutant concentrations. / Thesis (Ph.D, Geography) -- Queen's University, 2009-01-08 14:43:49.333

Natural hazards related to ground movement that directly affect the safety of motorists and highway infrastructure include, but are not limited to, rockfalls, rockslides, debris flows, and landslides. This thesis specifically deals with the evaluation of rockfall hazards through the evaluation of LiDAR data. Light Detection And Ranging (LiDAR) is an imaging technology that can be used to delineate and evaluate geomechanically-controlled hazards. LiDAR has been adopted to conduct hazard evaluations pertaining to rockfall, rock-avalanches, debris flows, and landslides. Characteristics of LiDAR surveying, such as rapid data acquisition rates, mobile data collection, and high data densities, pose problems to traditional CAD or GIS-based mapping methods. New analyses methods, including tools specifically oriented to geomechanical analyses, are needed. The research completed in this thesis supports development of new methods, including improved survey techniques, innovative software workflows, and processing algorithms to aid in the detection and evaluation of geomechanically controlled rockfall hazards. The scientific research conducted between the years of 2006-2010, as presented in this thesis, are divided into five chapters, each of which has been published by or is under review by an international journal. The five research foci are: i) geomechanical feature extraction and analysis using LiDAR data in active mining environments; ii) engineered monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR; iii) optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses; iv) location orientation bias when using static LiDAR data for geomechanical analysis; and v) evaluating roadside rockmasses for rockfall hazards from LiDAR data: optimizing data collection and processing protocols. ii The research conducted pertaining to this thesis has direct and significant implications with respect to numerous engineering projects that are affected by geomechanical stability issues. The ability to efficiently and accurately map discontinuities, detect changes, and standardize roadside geomechanical stability analyses from remote locations will fundamentally change the state-of-practice of geotechnical investigation workflows and repeatable monitoring. This, in turn, will lead to earlier detection and definition of potential zones of instability, will allow for progressive monitoring and risk analysis, and will indicate the need for pro-active slope improvement and stabilization. / Thesis (Ph.D, Geological Sciences & Geological Engineering) -- Queen's University, 2010-03-26 11:25:15.741