In order to ensure clean drinking water for all, it is crucial to understand potential upland stressors that compromise the quality of source waters treated by local community water systems (CWSs). Contamination associated with specific types of land cover can result in downstream water quality degradation, which may reduce the effectiveness of treatment by CWSs. Surface mining has been hypothesized as a source of drinking water degradation within the Central Appalachian region, which may result in adverse exposures and health disparities. The purpose of this study was to identify potential correlations between land cover and adverse birth outcomes (ABOs) through the application of watershed epidemiology, an emerging environmental health paradigm. Birth records for the Central Appalachian region were acquired from their respective state health departments from 2001 to 2015: each record contained the mother's street address, outcome variables, and covariates. Records were included in later analyses if they fell within an approximated CWS service area. Contributing land cover to each CWS was determined via previously delineated watersheds that relied on CWS intake points. A binomial generalized linear model was used to compare low birth weight (LBW), term low birth rate (tLBW), and preterm birth (PTB) incidence to CWS source watershed land cover, Safe Drinking Water Act (SDWA) violations, CWS size, and covariates related to the birth records. Source watershed mining and SDWA health based (HB) violations were significantly associated with greater risks for preterm birth (PTB) and low birth weight (LBW). Future work should be conducted to explore upstream flow impacts, address missing data in the birth records, and to more accurately represent CWS service areas to better characterize exposure. / Master of Science / Millions of individuals throughout the world are sickened by waterborne exposures every year. To ensure clean drinking water long-term, it is crucial to understand how human land cover might change the water quality of source watersheds, as this may impact the effectiveness of water treatment and increase adverse human health exposures. The goal of this effort is to understand whether land cover is linked to downstream adverse birth outcomes (ABOs) in Central Appalachia, a region of the United States previously associated with high disease incidence suspected to be partially linked to environmental exposure. Birth records were acquired for the years of 2001 to 2015 from four (VA, WV, TN, KY) respective state health departments. Each record contained the mother's address, outcome variables, and covariates (e.g., race, ethnicity). Births were located within approximate service areas for 140 surface water dependent community water systems (CWS) within the region. Data from each CWS, including weighted land cover proportions for their source watershed, were merged with the birth records according to approximate service areas. Statistical analysis suggested that higher source watershed levels of mining and urban development were associated with higher risks of preterm birth (PTB) and low birth weight (LBW). The number of health based (HB) violations associated with each CWS was also associated with both of these outcomes. Major limitations of this work include birth record data gaps and the lack of publicly available CWS service areas and/or water consumption rates, which does increase the risk of exposure misclassification.

I investigated the relationships between historical and contemporary land cover and macroinvertebrate communities, water quality, and nutrient levels in 10 streams in a historically agricultural region of the Virginia Piedmont. Historical (1963) and contemporary (2011) impervious surface, open area, and forested cover were evaluated using aerial photos and GIS data. Macroinvertebrates were collected in the fall of 2012 and spring of 2013. Water quality parameters (temperature, conductivity, alkalinity, hardness, and DO) and nutrient concentrations (NH3+NH4, PO4-P, NO3-N, Cl, and SO4) were measured at each site. Overall, forest cover decreased by 6.29%, open area decreased by 1.46%, and impervious surface increased by 4.83% from 1963 to 2011. Macroinvertebrate communities were explored using Principal Coordinates Analysis and were found to be significantly related to 2011 percent impervious surface. Water quality parameters were not significantly related to contemporary or historical land cover. Nitrate was negatively related with 2011 forest cover and positively related with 2011 open area; chloride was positively related with 2011 impervious surface and negatively related with 2011 open area. For the 10 watersheds included in this study, contemporary land cover is a better predictor of macroinvertebrate assemblages and nutrient concentrations than historical land cover. / Master of Science

Thousands of communities across the nation are exposed to health risks from contaminated drinking water. Upstream anthropogenic land covers have been linked with the degradation of source drinking water quality and likely pose a threat to a community water system's (CWS's) ability to provide safe drinking water. The goal of this study was to predict the differences in compliance with the Safe Drinking Water Act (SDWA) between CWSs based on their upstream land cover, economic situation, and system characteristics. In Central Appalachia, from 2001 to 2016, proportions of land cover in each target CWS's upstream source water watershed were weighted based on their distance to a CWS's source water intake. Violations to the SDWA at respective CWSs over the same period were modeled with their distance weighted land cover proportions, economic status of the county served, and system characteristics as covariates. The major findings were that increases in low intensity development increased the likelihood of a health-based violation, larger CWSs were less likely than smaller CWSs to obtain a monitoring and reporting violation, and CWSs that distributed purchased water were the least likely to incur either violation type. These results suggest that communities that have CWSs that are repeatedly failing to remain in compliance with the SDWA may be able to reduce public health risks associated with drinking water by purchasing from a larger CWS. Further to protect public health, community managers should consider source water protection and/or upgrading a CWS's treatment capacity prior to developing a previously undeveloped area. / Master of Science / Millions of people across the nation face health risks from contaminated drinking water. Understanding what factors influence a community water system's ability to supply safe drinking water is critical in the effort to protect public health. Land cover altered by humans has been found to pollute drinking water sources and may be linked to unsafe drinking water. This study aims to predict the differences in compliance with the Safe Drinking Water Act (SDWA) between community water systems (CWSs) based on their upstream land cover, economic situation, and system characteristics. In Central Appalachia, proportions of land cover between 2001 and 2016 were calculated for each target CWS's upstream source water watershed. Violations to the SDWA were used in a statistical analysis with land cover, economic status of the county served, and system characteristics of respective CWSs. The major findings were that increases in low intensity developed area increased the likelihood of health-based (HB) violations, larger CWSs were more likely than smaller CWSs to monitor and report their water quality, and CWSs that served purchased water were the least likely to have a HB or monitoring and reporting violation. These results suggest that purchasing drinking water from a larger CWS may allow water providers to reduce the risk to public health from unsafe drinking water. Additionally, protecting drinking source water and/or upgrading a CWS's treatment ability prior to developing a previously undeveloped area may reduce threats to drinking water safety.

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

La question du changement de la couverture terrestre est devenue importante dans le monde entier au cours des dernières années, non seulement pour les chercheurs, mais aussi pour les planificateurs urbains et les écologistes qui préconisent l'utilisation durable des terres dans l'avenir. En Europe méditerranéenne, les caractéristiques de couverture du sol ont considérablement changé depuis la Seconde Guerre mondiale en raison des activités humaines intensives, de la croissance de la population, et de l'étalement urbain et touristique. La plupart des études antérieures sur les changements de l’occupation du sol dans la région méditerranéenne se sont centrées sur un problème particulier et / ou ont décrit un type spécifique de changement de la couverture terrestre. Peu de recherches ont pris en compte les transformations de plusieurs catégories d’occupation du sol en même temps. De même, rares sont les travaux qui considèrent plusieurs variables dans le changement de l’occupation du sol au cours du temps, au-delà des traditionnels effets de l’altitude et de la pente. Nous souhaitons ici intégrer la variété des catégories et des composantes d’évolution. En outre, si certaines études à propos de la modélisation des mutations de la couverture terrestre se concentrent sur les variables d’influence, peu se penchent sur l’influence de la période historique et des échelles de temps différentes sur la prédiction. Ainsi, dans cette thèse, les changements de l’occupation du sol ont été prédits en utilisant différentes échelles de temps pour évaluer les impacts de la période historique dans la prédiction de la carte de la couverture terrestre d'ici 2025. Enfin, si l’étendue spatiale varie dans les différentes recherches, il semble utile de s’interroger sur les effets de la taille du terrain d’étude et de la résolution des cellules prises en compte, dans la prédiction. Les transformations de l’occupation du sol ont un impact significatif sur la dégradation des terres, y compris l'érosion des sols. / The European Mediterranean coastal area has experienced widespread land cover change since 1950 because of rapid urban growth and expansion of tourism. Urban sprawl and other land cover changes occurred due to post-war economic conditions, population migration, and increased tourism. Land cover change has occurred through the interaction of environmental and socio-economic factors, including population growth, urban sprawl, industrial development, and environmental policies. In addition, rapid expansion of tourism during the last six decades has caused significant socioeconomic changes driving land cover change in Euro-Mediterranean areas. Mediterranean countries from Spain to Greece experienced strong urban growth from the 1970’s onwards, and a moderate growth rate is projected to continue into the future. Land cover change can result in environmental changes such as water pollution and soil degradation. Several previous studies have shown that Mediterranean vineyards are particularly vulnerable to soil erosion because of high rainfall intensity and the fact that vineyards are commonly located on steeper slopes and the soil is kept bare during most of the cultivation period (November to April) when precipitation is at its highest. The main objective of this thesis is to predict long-term soil erosion evolution in a Mediterranean context of rapid urban growth and land use change at the catchment scale. In order to achieve this, the following specific aims have been formulated: (i) to analyze the spatial dynamics of land cover change from 1950 to 2008; (ii) to compare the impact of historical time periods on land cover prediction using different time scales; (iii) to test the impacts of spatial extent and cell size on LUCC modeling; and (iv) to predict the impact of land cover change on soil erosion for 2025.

No description available.

New satellite missions have provided High Resolution (HR) Satellite Image Time Series (SITS), offering detailed spatial, spectral, and temporal information for effective monitoring of diverse Earth features including weather, landforms, oceans, vegetation, and agricultural practices. SITS can be used for an accurate understanding of the Land Cover (LC) behavior and providing the possibility of precise mapping of LCs. Moreover, HR SITS presents an unprecedented possibility for the creation and modification of HR Land Cover Change (LCC) and Land Cover Transition (LCT) maps. For the long-term scale, spanning multiple years, it becomes feasible to analyze LCC and the LCTs occurring between consecutive years. Existing methods in literature often analyze bi-temporal images and miss the valuable multi-temporal/multi-annual information of SITS that is crucial for an accurate SITS analysis. As a result, HR SITS necessitates a paradigm shift in processing and methodology development, introducing new challenges in data handling. Yet, the creation of techniques that can effectively manage the high spatial correlation and complementary temporal resolutions of pixels remains paramount. Moreover, the temporal availability of HR data across historical and current archives varies significantly, creating the need for an effective preprocessing to account for factors like atmospheric and radiometric conditions that can affect image reflectance and their applicability in SITS analysis. Flexible and automatic SITS analysis methods can be developed by paying special attention to handling big amounts of data and modeling the correlation and characterization of SITS in space and time. Novel methods should deal with data preparation and pre-processing at large-scale from end-to-end by introducing a set of steps that guarantee reliable SITS analysis while upholding the computational efficiency for a feasible SITS analysis. In this context, the recent strides in deep learning-based frameworks have demonstrated their potential across various image processing tasks, and thus the high relevance for addressing SITS analysis. Deep learning-based methods can be supervised or unsupervised considering their learning process. Supervised deep learning methods rely on labeled training data, which can be impractical for large-scale multi-temporal datasets, due to the challenges of manual labeling. In contrast, unsupervised deep learning methods are favored as they can automatically discover temporal patterns and changes without the need for labeled samples, thereby reducing the computational load, making them more suitable for handling extensive SITS. In this scenario, the objectives of this thesis are mainly three. Firstly, it seeks to establish a robust and reliable framework for the precise mapping of LCs by designing novel techniques for time series analysis. Secondly, it aims to utilize the capacities of unsupervised deep learning methods, such as pretrained Convolutional Neural Networks (CNNs), to construct a comprehensive methodology for Change Detection (CD), thereby mitigating complexity and reducing computational requirements in comparison with supervised methods. This involves the efficient extraction of spatial, spectral, and temporal features from complex multi-temporal, multi-spectral SITS. Lastly, the thesis endeavors to develop novel methods for analyzing LCCs occurring over extended time periods, spanning multiple years. This multifaceted approach encompasses the detection of changes, timing identification, and classification of the specific types of LCTs. The efficacy of the innovative methodologies and associated techniques is showcased through a series of experiments conducted on HR SITS datasets, including those from Sentinel-2 and Landsat. These experiments reveal significant enhancements when compared to existing methods that represent the current state-of-the-art.

The research presented here informs the confidence that can be placed in the simulations of land-surface models (LSMs). After introducing a method for simplifying a complex, heterogeneous land-cover dataset for use in LSMs, I show that LSMs can realistically represent the spatial distribution of heterogeneous land-cover processes (e.g., biogenic emission of volatile organic compounds) in Texas. LSM-derived estimates of biogenic emissions are sensitive (varying up to a factor of 3) to land-cover data, which is not well constrained by observations. Simulated emissions are most sensitive to land-cover data in eastern and central Texas, where tropospheric ozone pollution is a concern. I further demonstrate that interannual variation in leaf mass is at least as important to variation in biogenic emissions as is interannual variation in shortwave radiation and temperature. Model estimates show that more-humid regions with less year-to-year variation in precipitation have lower year-to-year variation in biogenic emissions: as modeled mean emissions increase, their mean-normalized standard deviation decreases. I evaluate three parameterizations of subsurface hydrology in LSMs (with (1) a shallow, 10-layer soil; (2) a deeper, many-layered soil; and (3) a lumped aquifer model) under increasing parameter uncertainty. When given their optimal parameter sets, all three versions perform equivalently well when simulating monthly change in terrestrial water storage. The most conceptually realistic model is least sensitive to errant parameter values. However, even when using the most conceptually realistic model, parameter interaction ensures that knowing ranges for individual parameters is insufficient to guarantee realistic simulation. LSMs are often developed and evaluated at data-rich sites but are then applied in regions where data are sparse or unavailable. I present a framework for model evaluation that explicitly acknowledges perennial sources of uncertainty in LSM simulations (e.g., parameter uncertainty, meteorological forcing-data uncertainty, evaluation-data uncertainty) and that evaluates LSMs in a way that is consistent with models’ typical application. The model performance score quantifies the likelihood that a representative ensemble of model performance will bracket observations with high skill and low spread. The robustness score quantifies the sensitivity of model performance to parameter error or data error. The fitness score ranks models’ suitability for broad application. / text

No description available.

Urbanization has been associated with the degradation of streams, and a consequence of forest to urban land transition is a change in streamflow. Therefore, the purpose of this thesis is to examine the impacts of land-cover change in ten different watersheds in the rapidly urbanizing Atlanta, GA USA metropolitan area. Streamflow and precipitation data for a 30-year period (1986-2016) were analyzed in conjunction with land cover data from 1992, 2001, and 2011. Big Creek and Suwanee Creek experienced the most urbanization and increases (20%) in streamflow and runoff, and high flow (>95th percentile of flow) days doubled and increased 85%, respectively. Precipitation-adjusted streamflow for Peachtree Creek and Flint River decreased about 17%. Runoff ratios for South River were the highest among all watersheds, even the Etowah River, which remained moderately forested and had the most precipitation and slope.