Alfred Stein

• University of Twente

Climate change affects plant dynamics and functioning of terrestrial ecosystems. This study aims to investigate temporal changes in global vegetation coverage and biomes during the past three decades. We compared historic annual NDVI time series (1982, 1983, 1984 and 1985) with recent ones (2015, 2016, 2017 and 2018), captured from NOAA-AVHRR satel...

Land Surface Temperature (LST) is significant for climatological and environmental studies. LST products from satellites, however, suffer from the tradeoff between spatial and temporal resolution. Spatial downscaling has emerged as a well explored field aiming to overcome limitations arising from this tradeoff. Previous research on regression based...

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese’s Advance Land Observation System (ALOS)...

Synthetic Aperture Radar (SAR) systems can be designed with different polarimetric modalities. Most space-borne SAR systems acquire dual polarimetric data to meet various operational requirements. They are designed to capture more information about the Earth’s surface than single-pol systems and to cover a wider area than full-pol modalities. Dual...

Increase in urban flood hazards has become a major threat to cities, causing considerable losses of life and in the economy. To improve pre-disaster strategies and to mitigate potential losses, it is important to make urban flood susceptibility assessments and to carry out spatiotemporal analyses. In this study, we used standard deviation ellipse (...

Himalayan glaciers have shown more sensitivity and visible changes to the climate change and global warming in the last 150 years. The highly rugged topography and inaccessible remote areas makes satellite images as the most appropriate source of information retrieval. We performed remote sensing based glacier change study for Samudra Tapu glacier,...

Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model...

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point cloud...

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.spasta.2021.100588. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal...

Spatial disease modeling remains an important public health tool. For cholera, the presence of zero counts is common. The Poisson model is inadequate to (1) capture over-dispersion, and (2) distinguish between excess zeros arising from non-susceptible and susceptible populations. In this study, we develop zero-inflated (ZI) mixture spatially varyin...

This paper presents an overview of the journal Spatial Statistics. It describes how it was initiated, how it developed and it highlights key moments from its young history. Starting in 2012, the journal has progressed in conjunction with the series of conferences in different countries over five continents. An important moment occurred when the jou...

In this paper, geospatial data are considered that are increasingly relevant in health insurance questions. An overview of the literature is presented, while the methodology is provided in the context of exceeding a threshold value. It is argued that the role of the threshold value might change to show spatial variability, while variability in time...

The second home is an important issue for a housing market and has effects on local land use. Conventional approaches do not consider second homes varying across space and time. Hence, spatial models, such as the Besag, York and Mollie (BYM) model and its variants, offer a feasible way to model both covariates and spatial, temporal dependence. This...

Deep learning methods based upon convolutional neural networks (CNNs) have demonstrated impressive performance in the task of building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. In this paper, we introduce an improved method that is able to predict regularized building outline in a vector format within an end-t...

Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use cl...

Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available. Despite its recognized advantages, DTW does not account for the duration and seasonality of crops and local differences...

Deep learning methods based on Fully convolution networks (FCNs) have shown an impressive progress in building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. Common issues still exist in extracting precise building shapes and outlines, often resulting in irregular edges and over smoothed corners. In this paper, we...

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remot...

Images are particular and well–known instances of spatial big data. Typically spatial data are scale specific and in this paper, we propose mechanisms to effectively address issues of scale in the analysis of images. We focus on spatial data extracted from images using the Discrete Pulse Transform (DPT). The DPT extracts discrete pulses from images...

The prevalence of obesity is still rising among Chinese adults and may be attributed to environmental factors, which, however, has only been examined in western countries before. This study aimed to estimate associations between obesogenic environments and adult obesity in China, on the basis of the official 2013-4 nationally representative survey....

Water is fundamental to human well-being, social development and the environment. Water development, particularly hydropower, provides an important source of renewable energy. Water development is strongly affected by poverty, but only few attempts have been made to understand the links between water development and poverty from a global water deve...

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of...

Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are avail...

In 2012, nearly 644,000 people died from diarrhea in sub-Saharan Africa. This is a significant obstacle towards the achievement of the Sustainable Development Goal 3 of ensuring a healthy life and promoting the wellbeing at all ages. To enhance evidence-based site-specific intervention and mitigation strategies, especially in resource-poor countrie...

Climate anomalies pose risks to agriculture and food security. To assess the impact, this paper models the complex dependences of climate extreme indices and the crop-related variables: yield, production, and price of a crop. Using a comprehensive copula-based analysis, the conditional distributions of the crop-related variables given extremes of a...

Detection of surface water from satellite images is important for water management purposes like for mapping flood extents, inundation dynamics, and water resources distributions. In this research, we introduce a supervised contextual classification model to detect surface water bodies from polarimetric Synthetic Aperture Radar (SAR) data. A comple...

In spatial epidemiology and public health studies, including covariates in small area estimation of spatial binary data remains a challenge. In this paper, Moran's spatial filtering is proposed to model two-scale spatial binary data. Two models are developed: the first uses deterministic estimation of the sample size at small areal level; the secon...

The introduction of apartheid in 1948 resulted in racial residential segregation that has influenced the spatial distribution of the population in South Africa. Apartheid laws, which were mainly based on race, brought about the exclusion of the non-white population from urban areas and the mainstream economy of South Africa, as well as the benefits...

Background: Various neglected tropical diseases show spatially changing seasonality at small areas. This phenomenon has received little scientific attention so far. Our study contributes to advancing the understanding of its drivers. This study focuses on the effects of the seasonality of increasing social contacts on the incidence proportions at...

The International Initiative on Spatial Lifecourse Epidemiology (ISLE) convened its first International Symposium on Lifecourse Epidemiology and Spatial Science at the Lorentz Center in Leiden, Netherlands, 16-20 July 2018. Its aim was to further an emerging transdisciplinary field: Spatial Lifecourse Epidemiology. This field draws from a broad per...

Glacier displacements play a vital role in the monitoring and understanding of glacier dynamics. Glacier displacement fields are typically retrieved from pre- and post-event SAR images using DInSAR. The glacier displacement map produced by DInSAR contains missing values due to decorrelation of the SAR images. This study demonstrates the utility of...

Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce env...

Properties of the conditional expectation. (DOCX)

Conditional copula density. (DOCX)

24 weather stations in the study area. The quality of measurements and number of missing values differ at each station. (DOCX)

The daily mean air temperatures from weather stations, reanalysis data and bias corrected values obtained by the bias correction methods for all locations on each day in June 2014. The number on each graph denotes the day in June 2014. (TIF)

Evaluating the stationarity assumption. (DOCX)

Variation of the mean air temperature on 1st day of June 2014 comparing with variation of the elevation in the study area. The mean air temperature in °C are derived from the synoptic and climatology type 1 weather stations. (TIF)

The values of co-correlogram and best fitting family at five spatial lags. Kendall’s τ correlations are obtained using the measured and reanalysis values on each day in June from 24 weather stations between 2004 to 2014. The copula families are: N = Gaussian, T = Student’s t, C = Clayton, G = Gumbel and F = Frank. (DOCX)

The vertical axis is daily mean air temperature in °C. The number on each graph denotes the weather station number. Time series of the measurements from weather stations, reanalysis data and bias corrected values obtained by the bias correction methods at each station in June 2014. (TIF)

The correlation coefficients r: a) in space on each day in June 2014, b) in time at each weather station. The numbers on the figures denote correlations. (TIF)

p values of the regression parameters in trend analysis obtained by F test. Based upon its results, spatial stationarity is assumed in estimating the marginal distribution. (TIF)

Influence of the choice of the increment value (IV) on a) the optimal conditional probability in CP-I and b) the mean absolute prediction errors. Three IVs 0.1, 0.01 and 0.001 are chosen. (TIF)

Comparing mean absolute prediction error with mean absolute bias at three types of the weather stations. The vertical axis is error/bias in °C. The synoptic stations are supposed to provide more precise measurements. (TIF)

The values of correlogram at five spatial lags. The vertical axis is Kendall’s τ correlations obtained using the measurements on each day in June between 2004 to 2014. The horizontal axis is spatial lags in meter. (TIF)

Reanalysis data retrieved from the European Centre for Medium-range Weather Forecasts (ECMWF) are commonly used for hydrological studies. Their use requires bias correction, defined as the difference between reanalysis values and measurements. We propose three multivariate copula quantile mappings (MCQMs) to predict bias-corrected values at unvisit...

The United Nations has called on all nations to take immediate actions to fight noncommunicable diseases (NCDs), which have become an increasingly significant burden to public health systems around the world. NCDs tend to be more common in developed countries but are also becoming of growing concern in low- and middle-income countries. Earth observ...

This study presents a method for estimating two area‐characteristic natural hazard recurrence parameters. The mean activity rate and the frequency–size power law exponent are estimated using Bayesian inference on combined empirical datasets that consist of prehistoric, historic, and instrumental information. The method provides for incompleteness,...

Spatial variability of soil health related variables in a hilly terrain may be high, and its characterization may require many samples. Our research compares deterministic and geostatistical interpolation methods in two hilly areas in India. The soil in the study area was acidic, without salts and with sufficient organic carbon content. Hence, thre...

Geographic Information Systems (GIS), Global Positioning Systems (GPS), and remote sensing (RS) are revolutionizing obesity-related research. The primary applications of GIS have included visualizing obesity outcomes and risk factors, constructing obesogenic environmental indicators, and detecting geographical patterns of obesity prevalence and obe...

Abstract Knowledge of the temporal trends and spatial patterns will have significant implications for effective preparedness in future epidemics. Our objective was to investigate the temporal trends and the nature of the spatial interaction of cholera incidences, dwelling on an outbreak in the Kumasi Metropolis, Ghana. We developed generalized nonp...

Images obtained from satellites are of an increasing resolution. [...]

Typhoid fever is estimated to cause between 9.9–24.2 million cases and 75,000–208,000 deaths per year globally. Low-income and middle-income countries report the majority of cases, especially those in sub-Saharan Africa. The epidemiology of typhoid fever is poorly understood, particularly in Ghana where there has been no study of the within-country...

Understanding the spatially varying effects of demographic factors on the spatio-temporal variation of intestinal parasites infections is important for public health intervention and monitoring. This paper presents a hierarchical Bayesian spatially varying coefficient model to evaluate the effects demographic factors on intestinal parasites morbidi...

In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Eu...

In this study, the fuzzy c- means classifier has been studied with nine other similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray-Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference and normalised squared Euclidean distance. Both single and composite...

This paper introduces two copula-based interpolation methods to produce air temperature maps in a data-scarce area: a spatial copula interpolator including covariates, and a mixed copula interpolator. The methods allow a construction of the conditional distribution of air temperature given the collocated covariates. Our study compared the new metho...

Background: Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH...

Kernel smoothing is commonly used in spatial point patterns to construct intensity plots. Kernels allow for visually and subjectively inferring on first-order stationarity. Formal objective tests exist for testing first-order stationarity that assume independence of spatial regions. We propose to extend inference for first-order stationary by using...

The Discrete Pulse Transform (DPT) is a nonlinear decomposition of a signal and extracts the structures in a signal at discrete scale levels. This study models the image structure extracted using the DPT as connected components. For this purpose, the Matérn cluster process is being used as it mimics the pulses of the DPT as clusters. In this paper...

This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzz...

1 Abstract This paper focuses on the presence of vegetation patches, called holes remaining after forest fires. Holes are of interest to explore because their vegetation is affected by severe temperature stress nearby, although they can serve as an agent to regenerate a forest after the burn. Further, it is interesting to know why holes emerge at a...

Classification of very high-resolution (VHR) satellite images has three major challenges: 1) inherent low intraclass and high interclass spectral similarities; 2) mismatching resolution of available bands; and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of ima...

Classification of very high resolution (VHR) satellite images has three major challenges: 1) inherent low intra-class and high inter-class spectral similarities, 2) mismatching resolution of available bands, and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of i...

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for...

PurposeSoil depth is a soil property that influences land use, land suitability, and earth surface processes. This article presents a simple method for predicting soil depth by constructing a membership function based on fuzzy C-means. Materials and methodsThis paper incorporates the soil type map, the land use map, and one type of DEM data to cons...

With their severe environmental and socioeconomic impact, drought events belong to the most far-reaching natural disasters. Effects are tremendous in rain-fed agricultural areas as in Africa. We analyzed and modeled the spatio-temporal statistical behavior of the Normalized Difference Vegetation Index as a risk indicator for drought, reflecting its...

Waddenzee vanaf de locaties Moddergat, Lauwersoog en Vierhuizen Advies Auditcommissie over de resultaten van het monitoringsjaar 2016 7 december 2017 / projectnummer: 3205

Model-based estimation of diarrhea risk and understanding the dependency on sociodemographic factors is important for prioritizing interventions. It is unsuitable to calibrate regression model with a single set of coefficients, especially for large spatial domains. For this purpose, we developed a Bayesian hierarchical varying coefficient model to...

This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for decision making and planning urban upgrading processes. D...

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another...

Information about the location and extent of informal settlements is necessary to guide decision making and resource allocation for their upgrading. Very high resolution (VHR) satellite images can provide this useful information, however, different urban settlement types are hard to be automatically discriminated and extracted from VHR imagery, bec...

Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify import...

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level...

This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Plei...

The paper presents a new method for empirical assessment of tsunami recurrence parameters, namely the mean tsunami activity rate kT, the Soloviev–Imamura frequency–magnitude power law bT-value, and the coastline-characteristic, maximum possible tsunami intensity imax. The three coastlinecharacteristic recurrence parameters are estimated locally by...