A5054607061- Soil Geostatistics and Mapping
- Spatial and Panel Data Analysis
- Geographic Information Systems Studies
- Data Management and Algorithms
- Data Analysis with R
- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Hydrology and Watershed Management Studies
- Geochemistry and Geologic Mapping
- Land Use and Ecosystem Services
- Scientific Computing and Data Management
- Atmospheric and Environmental Gas Dynamics
- Distributed and Parallel Computing Systems
- Air Quality Monitoring and Forecasting
- Data Mining Algorithms and Applications
- Geological Modeling and Analysis
- Climate variability and models
- 3D Modeling in Geospatial Applications
- Air Quality and Health Impacts
- Soil and Water Nutrient Dynamics
- Species Distribution and Climate Change
- Soil and Unsaturated Flow
- Remote-Sensing Image Classification
- demographic modeling and climate adaptation
- Cryospheric studies and observations
University of Münster
2016-2025
GeoInformation (United Kingdom)
2023
International Institute for Applied Systems Analysis
2019
52°North Spatial Information Research
2011-2013
Utrecht University
1999-2009
Imperial College London
2005
University of Bath
2005
University of Amsterdam
1998-2001
National Institute for Public Health and the Environment
1997
Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers.The sf package implements simple R, and has roughly the same capacity for as packages sp, rgeos, rgdal.We describe need this package, its place R ecosystem, potential to connect other computer systems.We illustrate with examples use.
We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat.Various covariance models have been implemented, such as separable, product-sum, metric sum-metric models.In a real-world application we compare spatiotemporal interpolations using these with purely spatial kriging approach.The target variable is daily mean PM 10 concentration measured at rural air quality monitoring stations across Germany in 2005.R code for variogram fitting...
Predictive modelling using machine learning has become very popular for spatial mapping of the environment. Models are often applied to make predictions far beyond sampling locations where new geographic might considerably differ from training data in their environmental properties. However, areas predictor space without support problematic. Since model no knowledge about these environments, have be considered uncertain. Estimating area which a prediction can reliably is required. Here, we...
Combined Global Surface Summary of Day and European Climate Assessment Dataset daily meteorological data sets (around 9000 stations) were used to build spatio‐temporal geostatistical models predict air temperature at ground resolution 1 km for the global land mass. Predictions in space time made mean, maximum, minimum temperatures using regression‐kriging with a series Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model wetness...
A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated measurements from the national network meteorological stations (159) in Croatia. The input data set contains 57,282 ground for year 2008. was modeled as a function latitude, longitude, distance sea, elevation, time, insolation, and LST images. original rasters were first converted...
This paper investigates the web-based remote sensing platform, Google Earth Engine (GEE) and evaluates platform's utility for performing raster vector manipulations on Landsat, Moderate Resolution Imaging Spectroradiometer GlobCover (2009) imagery. We assess its capacity to conduct space–time analysis over two subregions of Singapore, namely, Tuas Central Catchment Reserve (CCR), Urban Wetlands land classes. In current state, GEE has proven be a powerful tool by providing access wide variety...
The recent wave of published global maps ecological variables has caused as much excitement it received criticism. Here we look into the data and methods mostly used for creating these maps, discuss whether quality predicted values can be assessed, globally locally.
Abstract Aim Global‐scale maps of the environment are an important source information for researchers and decision makers. Often, these created by training machine learning algorithms on field‐sampled reference data using remote sensing as predictors. Since field samples often sparse clustered in geographic space, model prediction requires a transfer trained to regions where no available. However, recent studies question feasibility predictions far beyond location data. Innovation We propose...
This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented the package spacetime, provides examples for analyzing them. It builds upon spatial from sp, time series xts. The goal is cover a number useful representations sensor data, results predicting (spatial and/or temporal interpolation or smoothing), aggregating, subsetting them, represent trajectories. goals this paper explore how can be sensibly represented classes, find...
Abstract Climate change–driven shifts in streamflow timing have been documented for western North America and are expected to continue with increased warming. These changes will likely the greatest implications on already short overcommitted water supplies region. This study investigated American over 1948–2008 period, including very recent warm decade not previously considered, through (i) trends measures, (ii) two second-order linear models applied simultaneously region test acceleration...
In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more exposed and vulnerable to extreme weather events. period 1950–2015 occurrence of heat waves increased across European capitals. As an example, 2010 Moscow was hit by strongest wave present era, killing than ten thousand people. The cold extremes have decreasing tendency as global progresses, however due higher variability future climates, hazard may remain locally important...
At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, JavaScript) service providers regarding access processing, simplifying their direct comparability. Independent of the providers’ storage system, mimics functionalities a...
Abstract Several spatial and non‐spatial Cross‐Validation (CV) methods have been used to perform map validation when additional sampling for purposes is not possible, yet it unclear in which situations one CV method might be preferred over the other. Three factors identified as determinants of performance validation: prediction area (geographical interpolation vs. extrapolation), pattern landscape autocorrelation. In this study, we propose a new strategy that takes geographical space into...
Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required distributed hydrological modeling. To this end data from Netherlands national gauge network (330 nationwide) is combined an experimental (30 within 225 km2). Based on 74 selected events (March–October 2004) spatial variability investigated at three extents: small (225 km2), medium (10 000 and large (82 875 From analysis it shown that...