N.A.S. Hamm

Statistics-based outlier detection for wireless sensor networks

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.

Variance-based sensitivity analysis of the probability of hydrologically induced slope instability

Abstract Analysis of the sensitivity of predictions of slope instability to input data and model uncertainties provides a rationale for targeted site investigation and iterative refinement of geotechnical models. However, sensitivity methods based on local derivatives do not reflect model behaviour over the whole range of input variables, whereas methods based on standardised regression or correlation coefficients cannot detect non-linear and non-monotonic relationships between model input and output. Variance-based sensitivity analysis (VBSA) provides a global, model-independent sensitivity measure. The approach is demonstrated using the Combined Hydrology and Stability Model (CHASM) and is applicable to a wide variety of computer models. The method of Sobol’, assuming independence between input variables, was used to identify interactions between model input variables, whilst replicated Latin Hypercube Sampling (LHS) is used to investigate the effects of statistical dependence between the input variables. The SIMLAB software was used, both to generate the input sample and to calculate the sensitivity indices. The analysis provided quantified evidence of well-known sensitivities as well demonstrating how uncertainty in slope failure during rainfall is, for the examples tested here, more attributable to uncertainty in the soil strength than to uncertainty in the rainfall.

Handling uncertainties in image mining for remote sensing studies

This paper presents an overview of uncertainty handling in remote sensing studies. It takes an image-mining perspective and identifies different ways of handling uncertainties. It starts with the pixel, and through object identification and modelling, proceeds towards monitoring and decision making. Methods presented originate both from probability- and fuzzy-logic-based approaches. The paper is illustrated with three examples, one from a geographic information system stored object, one from an object identified from a remotely sensed image directly and a practical case study from the Tibet plateau. An important remaining topic is to combine and integrate errors and uncertainties collected during the whole image-mining process.

Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis

Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space-time maps that can identify red-flag regions exceeding statutory concentration limits. Continuous spatio-temporal Gaussian Process (GP) models can deliver maps depicting predicted PM levels and quantify predictive uncertainty. However, GP-based approaches are usually thwarted by computational challenges posed by large datasets. We construct a novel class of scalable Dynamic Nearest Neighbor Gaussian Process (DNNGP) models that can provide a sparse approximation to any spatio-temporal GP (e.g., with nonseparable covariance structures). The DNNGP we develop here can be used as a sparsity-inducing prior for spatio-temporal random effects in any Bayesian hierarchical model to deliver full posterior inference. Storage and memory requirements for a DNNGP model are linear in the size of the dataset, thereby delivering massive scalability without sacrificing inferential richness. Extensive numerical studies reveal that the DNNGP provides substantially superior approximations to the underlying process than low-rank approximations. Finally, we use the DNNGP to analyze a massive air quality dataset to substantially improve predictions of PM levels across Europe in conjunction with the LOTOS-EUROS chemistry transport models (CTMs).

Hydrological modelling of a drained grazing marsh under agricultural land use and the simulation of restoration management scenarios

Abstract The capability of the spatially-distributed, physically-based, rainfall-runoff modelling system, MIKE SHE, to simulate the hydrological behaviour of the natural and drained parts of the North Kent Grazing Marshes, UK, is investigated. The MIKE SHE code is applied to Bells Creek, a small, underdrained, agricultural catchment located within the marshes. The model is used to both provide insights into the essential parameters that control the hydrological processes in the catchment, and predict the influence of various, hypothetical, water management strategies (land use and drainage) on pumped discharge and soil moisture storage in the catchment. The water table model predictions arising from these hypothetical scenarios are also compared against field data obtained from on-going hydrological research on the neighbouring, natural, Elmley Marshes. The comparison is found to be favourable. The results of this study indicate the potential of the MIKE SHE system to simulate the hydrological regime of t...

The landscape epidemiology of echinococcoses

Echinococcoses are parasitic diseases of major public health importance globally. Human infection results in chronic disease with poor prognosis and serious medical, social and economic consequences for vulnerable populations. According to recent estimates, the geographical distribution of Echinococcus spp. infections is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and over time it may be affected by global environmental change. Therefore, landscape epidemiology offers a unique opportunity to quantify and predict the ecological risk of infection at multiple spatial and temporal scales. Here, we review the most relevant environmental sources of spatial variation in human echinococcosis risk, and describe the potential applications of landscape epidemiological studies to characterise the current patterns of parasite transmission across natural and human-altered landscapes. We advocate future work promoting the use of this approach as a support tool for decision-making that facilitates the design, implementation and monitoring of spatially targeted interventions to reduce the burden of human echinococcoses in disease-endemic areas.

Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases

Earth observation (EO) is the use of remote sensing and in situ observations to gather data on the environment. It finds increasing application in the study of environmentally modulated neglected tropical diseases (NTDs). Obtaining and assuring the quality of the relevant spatially and temporally indexed EO data remain challenges. Our objective was to review the Earth observation products currently used in studies of NTD epidemiology and to discuss fundamental issues relating to spatial data quality (SDQ), which limit the utilization of EO and pose challenges for its more effective use. We searched Web of Science and PubMed for studies related to EO and echinococossis, leptospirosis, schistosomiasis, and soil-transmitted helminth infections. Relevant literature was also identified from the bibliographies of those papers. We found that extensive use is made of EO products in the study of NTD epidemiology; however, the quality of these products is usually given little explicit attention. We review key issues in SDQ concerning spatial and temporal scale, uncertainty, and the documentation and use of quality information. We give examples of how these issues may interact with uncertainty in NTD data to affect the output of an epidemiological analysis. We conclude that researchers should give careful attention to SDQ when designing NTD spatial-epidemiological studies. This should be used to inform uncertainty analysis in the epidemiological study. SDQ should be documented and made available to other researchers.

Spatio-Temporal Assessment of Tuz Gölü, Turkey as a Potential Radiometric Vicarious Calibration Site

The paper provides an assessment of Tuz Golu, a site in Turkey proposed for the radiometric vicarious calibration of satellite sensors, in terms of its spatial homogeneity as expressed in visible and near-infrared (VNIR) wavelengths over a 25-year period (1984–2009). By combining the coefficient of variation (CV) and Getis statistic (Gi*), a spatially homogenous and temporally stable area at least 720 m × 330 m in size was identified. Analysis of mid-summer Landsat Thematic Mapper (TM) images acquired over the period 1984–2009 showed that the hemispherical-directional reflectance factor of this area had a spatial variability, as defined by the CV, in the range of 0.99% to 3.99% in Landsat TM bands 2–4. This is comparable with the reported variability of other test sites around the world, but this is the first time an area has been shown to have this degree of homogeneity over such a long period of time.

Analysing the effect of different aggregation approaches on remotely sensed data

The effect of spatial aggregation varies depending on the aggregation logic. This study examined and compared the effect of both categorical and numerical aggregation. Categorical aggregation focused on the majority rule-based (MRB), random rule-based (RRB), and point-centred distance-weighted moving window (PDW). Both RRB and PDW have a stochastic component. Numerical aggregation focused on mean aggregation and central pixel resampling (CPR). The change in class proportions and landscape metrics with respect to a fine-resolution base image were assessed. RRB, PDW, and CPR preserved class proportion with decreasing spatial resolution. MRB increased the proportion of the dominant class and decreased all other class proportions, whereas mean aggregation increased the proportion of non-dominant class. All approaches led to a less clumped pattern for each class, except MRB, which made the dominant class more clumped. For all classes, RRB, PDW, and CPR led to a lower distortion in shape complexity than MRB and mean aggregation. RRB responded similarly for all realizations, but variability in PDW could be minimized by choosing a specific parameter value. The study showed that RRB, PDW, and CPR can be used in, for example, studies on ecological resource management where consistency of the class proportions at coarser resolutions is required, and that PDW is the best option. MRB can be used in regional-level as well as national-level agriculture or forest planning, where the delineation of the dominant class is required.

Fuzzy Super Resolution Mapping Based on Markov Random Fields

Recent research has used Markov Random Fields (MRF) as a method for super-resolution mapping (SRM). This paper investigated the per-pixel uncertainty associated with MRF based SRM. This provided insight into the spatial distribution of uncertainty associated with SRM. Furthermore, the map of per-pixel uncertainty clearly shows the boundary between land-cover classes and this may provide an input for image segmentation. The insight provided by the per-pixel uncertainty together with the class boundaries will be valuable for development of the MRF approach to super-resolution mapping.