Dynamic factor analysis of surface water management impacts on soil and bedrock water contents in Southern Florida Lowlands

As part of the C111 spreader canal project, structural and operational modifications involving incremental raises in canal stage are planned along one of the major canals (i.e., C111) separating Everglades National Park and agricultural production areas to the east of the park. This study used Dynamic Factor Analysis (DFA) as an alternative tool to physically based models to explore the relationship between different hydrologic variables and the effect of proposed changes in surface water management on soil and bedrock water contents in south Florida. To achieve the goal, objectives were to: (1) use DFA to identify the most important factors affecting temporal variation in soil and bedrock water contents, (2) develop a simplified DFA based regression model for predicting soil and bedrock water contents as a function of canal stage and (3) assess the effect of the proposed incremental raises in canal stage on soil and bedrock water contents. DFA revealed that 5 common trends were the minimum required to describe unexplained variation in the 11 time series studied. Introducing canal stage, water table evaporation and net recharge resulted in lower Akaike information criterion (AIC) and higher Nash-Sutcliffe (C[subscript eff]) values. Results indicated that canal stage significantly (t > 2) drives temporal variation in soil and bedrock water contents, which was represented as scaled frequency while net surface recharge was significant in 7 out of the 11 time series analyzed. The effect of water table evaporation was not significant at all sites. Results also indicated that the most important factor influencing temporal variation in soil and bedrock water contents in terms of regression coefficient magnitude was canal stage. Based on DFA results, a simple regression model was developed to predict soil and bedrock water contents at various elevations as a function of canal stage and net recharge. The performance of the simple model ranged from good (C[subscript eff] ranging from 0.56 to 0.74) to poor (C[subscript eff] ranging from 0.10 to 0.15), performance was better at sites with smaller depths to water table (< 1 m) highlighting the effect of micro-topography on soil and bedrock water content dynamics. Assessment of the effect of 6, 9 and 12 cm increases in canal stage using the simple regression model indicated that changes in temporal variation in soil and bedrock water contents were negligible (average<1.0% average change) at 500 to 2000 m from C111 (or low elevations) which may be attributed to the near saturation conditions already occurring at these sites. This study used DFA to explore the relationship between soil and bedrock water dynamics and surface water stage in shallow water table environments. This approach can be applied to any system in which detailed physical modeling would be limited by inadequate information on parameters or processes governing the physical system

Assessing the impact of spectral resolution on classification of lowland native grassland communities based on field spectroscopy in Tasmania, Australia

This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy for lowland native grasslands and other vegetation communities based on hyperspectral resolution datasets over multispectral equivalents. A spectral dataset was collected using an ASD Handheld-2 spectroradiometer at Tunbridge Township Lagoon. The study then employed a k-fold cross-validation approach for repeated classification of a full hyperspectral dataset, a reduced hyperspectral dataset, and two convoluted multispectral datasets. Classification was performed on each of the four datasets a total of 30 times, based on two different class configurations. The classes analysed were Themeda triandra grassland, Danthonia/Poa grassland, Wilsonia rotundifolia/Selliera radicans, saltpan, and a simplified C3 vegetation class. The results of the classifications were then tested for statistically significant differences using ANOVA and Tukey’s post-hoc comparisons. The results of the study indicated that hyperspectral resolution provides small but statistically significant increases in classification accuracy for Themeda and Danthonia grasslands. For other classes, differences in classification accuracy for all datasets were not statistically significant. The results obtained here indicate that there is some potential for enhanced detection of major lowland native grassland community types using hyperspectral resolution datasets, and that future analysis should prioritise good performance in these classes over others. This study presents a method for identification of optimal spectral resolution across multiple datasets, and constitutes an important case study for lowland native grassland mapping in Tasmania

Sustainability of irrigated agriculture under salinity pressure – A study in semiarid Tunisia

In semiarid and arid Tunisia, water quality and agricultural practices are the major contributing factors to the degradation of soil resources threatening the sustainability of irrigation systems and agricultural productivity. Nowadays, about 50% of the total irrigated areas in Tunisia are considered at high risk for salinization. The aim of this thesis was to study soil management and salinity relationships in order to assure sustainable irrigated agriculture in areas under salinity pressure. To prevent further soil degradation, farmers and rural development officers need guidance and better tools for the measurement, prediction, and monitoring of soil salinity at different observation scales, and associated agronomical strategy. Field experiments were performed in semi-arid Nabeul (sandy soil), semi-arid Kalâat Landalous (clay soil), and the desertic Fatnassa oasis (gypsiferous soil). The longest observation period represented 17 years. Besides field studies, laboratory experiments were used to develop accurate soil salinity measurements and prediction techniques. In saline gypsiferous soil, the WET sensor can give similar accuracy of soil salinity as the TDR if calibrated values of the soil parameters are used instead of standard values. At the Fatnassa oasis scale, the predicted values of ECe and depth of shallow groundwater Dgw using electromagnetic induction EM-38 were found to be in agreement with observed values with acceptable accuracy. At Kalâat Landalous (1400 ha), the applicability of artificial neural network (ANN) models for predicting the spatial soil salinity (ECe) was found to be better than multivariate linear regression (MLR) models. In semi-arid and desertic Tunisia, irrigation and drainage reduce soil salinity and dilute the shallow groundwater. However, the ECgw has a larger impact than soil salinity variation on salt balance. Based on the findings related to variation in the spatial and temporal soil and groundwater properties, soil salinization factors were identified and the level of soil “salinization risk unit” (SRU) was developed. The groundwater properties, especially the Dgw, could be considered as the main cause of soil salinization risk in arid Tunisia. However, under an efficient drainage network and water management, the soil salinization could be considered as a reversible process. The SRU mapping can be used by both land planners and farmers to make appropriate decisions related to crop production and soil and water management

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

14 Pags. The definitive version, with tabls. and figs., is available at: http://www.sciencedirect.com/science/journal/01681699The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor. Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.The Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) funded the PhD grant of the first author. This work is a result of the project RTA2005-00230, funded by INIA. The contribution of the project AGL2009-08931/AGR is also recognized.Peer reviewe

Mariner Mars 1971 optical navigation demonstration

The feasibility of using a combination of spacecraft-based optical data and earth-based Doppler data to perform near-real-time approach navigation was demonstrated by the Mariner Mars 71 Project. The important findings, conclusions, and recommendations are documented. A summary along with publications and papers giving additional details on the objectives of the demonstration are provided. Instrument calibration and performance as well as navigation and science results are reported

Advances in Clinical Molecular Imaging Instrumentation

In this article, we describe recent developments in the design of both single-photon emission computed tomography (SPECT) and positron emission tomography (PET) instrumentation that have led to the current range of superior performance instruments. The adoption of solid-state technology for either complete detectors [e.g., cadmium zinc telluride (CZT)] or read-out systems that replace photomultiplier tubes [avalanche photodiodes (APD) or silicon photomultipliers (SiPM)] provide the advantage of compact technology, enabling flexible system design. In SPECT, CZT is well suited to multi-radionuclide and kinetic studies. For PET, SiPM technology provides MR compatibility and superior time-of-flight resolution, resulting in improved signal-to-noise ratio. Similar SiPM technology has also been used in the construction of the first SPECT insert for clinical brain SPECT/MRI

The application of ocean front metrics for understanding habitat selection by marine predators

Marine predators such as seabirds, cetaceans, turtles, pinnipeds, sharks and large teleost fish are essential components of healthy, biologically diverse marine ecosystems. However, intense anthropogenic pressure on the global ocean is causing rapid and widespread change, and many predator populations are in decline. Conservation solutions are urgently required, yet only recently have we begun to comprehend how these animals interact with the vast and dynamic oceans that they inhabit. A better understanding of the mechanisms that underlie habitat selection at sea is critical to our knowledge of marine ecosystem functioning, and to ecologically-sensitive marine spatial planning. The collection of studies presented in this thesis aims to elucidate the influence of biophysical coupling at oceanographic fronts – physical interfaces at the transitions between water masses – on habitat selection by marine predators. High-resolution composite front mapping via Earth Observation remote sensing is used to provide oceanographic context to several biologging datasets describing the movements and behaviours of animals at sea. A series of species-habitat models reveal the influence of mesoscale (10s to 100s of kilometres) thermal and chlorophyll-a fronts on habitat selection by taxonomically diverse species inhabiting contrasting ocean regions; northern gannets (Morus bassanus; Celtic Sea), basking sharks (Cetorhinus maximus; north-east Atlantic), loggerhead turtles (Caretta caretta; Canary Current), and grey-headed albatrosses (Thalassarche chrysostoma; Southern Ocean). Original aspects of this work include an exploration of quantitative approaches to understanding habitat selection using remotely-sensed front metrics; and explicit investigation of how the biophysical properties of fronts and species-specific foraging ecology interact to influence associations. Main findings indicate that front metrics, particularly seasonal indices, are useful predictors of habitat preference across taxa. Moreover, frontal persistence and spatiotemporal predictability appear to mediate the use of front-associated foraging habitats, both in shelf seas and in the open oceans. These findings have implications for marine spatial planning and the design of protected area networks, and may prove useful in the development of tools supporting spatially dynamic ocean management

CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Simulated Crash Data Study

In this paper, a new non-parametric empirical Bayes approach called CGAN-EB is proposed for approximating empirical Bayes (EB) estimates in traffic locations (e.g., road segments) which benefits from the modeling advantages of deep neural networks, and its performance is compared in a simulation study with the traditional approach based on negative binomial model (NB-EB). The NB-EB uses negative binomial model in order to model the crash data and is the most common approach in practice. To model the crash data in the proposed CGAN-EB, conditional generative adversarial network is used, which is a powerful deep neural network based method that can model any types of distributions. A number of simulation experiments are designed and conducted to evaluate the CGAN-EB performance in different conditions and compare it with the NB-EB. The results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice, specifically low sample means, and when crash frequency does not follow a log-linear relationship with covariates.Comment: 17 pages, 8 figure