Nicholas A. S. Hamm

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Statistics-based outlier detection for wireless sensor networks Y. Zhang a , N.A.S. Hamm b , N. Meratnia a , A. Stein b , M. van de Voort a & P.J.M. Havinga a a Pervasive System Group, Department of Computer Science (EWI), University of Twente, Enschede, The Netherlands b Department of Earth Observation Science, Faculty of GeoInformation Science and Earth(More)
The paper provides an assessment of Tuz Gölü, 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(More)
Consistent satellite image time series are increasingly accessible to geoscientists, allowing an effective monitoring of environmental phenomena. Specifically, the use of vegetation index time series has pushed forward the monitoring of large-scale vegetation phenology. Most of these studies derive key phenological metrics from the Normalized Difference(More)
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(More)
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(More)
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(More)
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(More)
Coseismic displacements play a significant role in characterizing earthquake causative faults and understanding earthquake dynamics. They are typically measured from InSAR using preand post-earthquake images. The displacement map produced by InSAR may contain missing coseismic values due to the decorrelation of ASAR images. This study focused on(More)
This study shows two approaches to including uncertainty of the mapped feature in multi-temporal analysis. This is demonstrated on a series of Landsat ETM+ images of Lake Naivasha, Kenya, with fuzzy boundaries resulting from marshes and floating vegetation. The first approach creates image segments, merges these to image objects through object-based(More)
BACKGROUND Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their(More)