Stochastic Environmental Research and Risk Assessment (SERRA), 2002
The prediction error of a relatively simple soil acidification model (SMART2) was assessed before... more The prediction error of a relatively simple soil acidification model (SMART2) was assessed before and after calibration, focussing on the Al and NO 3 concentrations on a block scale. Although SMART2 is especially developed for application on a national to European scale, it still runs at a point support. A 5 Â 5 km 2 grid was used for application on the European scale. Block characteristic values were obtained simply by taking the median value of the point support values within the corresponding grid cell. In order to increase confidence in model predictions on large spatial scales, the model was calibrated and validated for the Netherlands, using a resolution that is feasible for Europe as a whole. Because observations are available only at the point support, it was necessary to transfer them to the block support of the model results. For this purpose, about 250 point observations of soil solution concentrations in forest soils were upscaled to a 5 Â 5 km 2 grid map, using multiple linear regression analysis combined with block kriging. The resulting map with upscaled observations was used for both validation and calibration. A comparison of the map with model predictions using nominal parameter values and the map with the upscaled observations showed that the model overestimated the predicted Al and NO 3 concentrations. The nominal model results were still in the 95% confidence interval of the upscaled observations, but calibration improved the model predictions and strongly reduced the model error. However, the model error after calibration remains rather large.
Lecture Notes in Geoinformation and Cartography, 2013
ABSTRACT Spatio-temporal aggregation of observed or predicted values for environmental phenomena ... more ABSTRACT Spatio-temporal aggregation of observed or predicted values for environmental phenomena is needed for fusing sensor data or coupling sensors and environmental models. However, estimates from sensors or environmental models can never represent our world precisely and are subject to errors. Hence, there is uncertainty in the estimates that needs to be considered in environmental model workflows. This chapter presents an approach for an error-aware spatio-temporal aggregation in the Web, where probabilistic uncertainties are used within a Monte Carlo simulation. The approach is applied in a Web-based model chain that provides uncertain crop yield predictions on field parcel level that are aggregated to larger regions.
Copulas are a flexible tool to model dependence of random variables. They cover the range from pe... more Copulas are a flexible tool to model dependence of random variables. They cover the range from perfect negative to positive dependence, include the independent case and incorporate asymmetric dependence as well as the widely used Gaussian dependence structure. The pair-copula construction for multivariate copulas exploits the ease of bivariate copulas and suggests a decomposition of a multivariate copula into a set of bivariate ones. We successfully adapted this approach for spatial data and developed a powerful spatial pair-copula based interpolation method.
This paper presents an analysis of disaggregation for PM 10 pollution from a grid to point suppor... more This paper presents an analysis of disaggregation for PM 10 pollution from a grid to point support for exposure modelling on a GPS track representing an individual space-time trajectory. Different sets of explanatory variables were tested to predict spatial variability of mobile PM 10 measurements at the point support. Disaggregation was performed using unconditional Gaussian simulation. The results show a considerable amount of uncertainty added due to disaggregation that depends in strength on the auxiliary data set used for prediction. Subsequent aggregation over the GPS track leads to a reduction in uncertainties.
Lecture Notes in Geoinformation and Cartography, 2011
Interoperable data exchange and reproducibility are increasingly important for modern scientific ... more Interoperable data exchange and reproducibility are increasingly important for modern scientific research. This paper shows how three open source projects work together to realize this: (i) the R project, providing the lingua franca for statistical analysis, (ii) the Open Geospatial Consortium's Sensor Observation Service (SOS), a standardized data warehouse service for storing and retrieving sensor measurements, and (iii) sos4R, a new project that connects the former two. We show how sos4R can bridge the gap between two communities in science: spatial statistical analysis and visualization on one side, and the Sensor Web community on the other. sos4R enables R users to integrate (near real-time) sensor observations directly into R. Finally, we evaluate the functionality of sos4R. The software encapsulates the service's complexity with typical R function calls in a common analysis workflow, but still gives users full flexibility to handle interoperability issues. We conclude that it is able to close the gap between R and the sensor web.
... Pebesma, EJ (1996), Gstat-a Program for Geostatistical Modelling, Prediction and Simulation. ... more ... Pebesma, EJ (1996), Gstat-a Program for Geostatistical Modelling, Prediction and Simulation. ... time knging of groundwater data, in M. Armstrong (ed.) Geostatistics, Kluwer Academic ... Rouhani, S. and Wackernagel, H.(1990) Multivariate geostatistical approach to space-time data ...
The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the ... more The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the atmosphere calls for policies that take account of changes in forest cover. Although much research has focused on the location and effects of deforestation, little is known about the distribution and reasons for the agricultural uses that replace forest cover. We used Landsat TM-based deforestation and agricultural census data to generate maps of the distribution and proportion of four major agricultural land uses throughout the Brazilian Amazon in 1997 and 2007. We built linear and spatial regression models to assess the determinant factors of deforestation and those major agricultural land uses e pasture, temporary agriculture and permanent agriculture e for the states of Pará, Rondônia, and Mato Grosso. The data include 30 determinant factors that were grouped into two years (1996 and 2006) and in four categories: accessibility to markets, public policies, agrarian structure, and environment. We found an overall expansion of the total agricultural area between 1997 and 2007, and notable differences between the states of Pará, Rondônia, and Mato Grosso in land use changes during this period. Regression models for deforestation and pasture indicated that determinant factors such as distance to roads were more influential in 1997 than in 2007. The number of settled families played an important role in the deforestation and pasture, the effect was stronger in 2007 than 1997. Indigenous lands were significant in preventing deforestation in high-pressure areas in 2007. For temporary and permanent agricultures, our results show that in 1997 the effect of small farms was stronger than in 2007. The mapped land use time series and the models explain empirically the effects of land use changes across the region over one decade.
The Geospatial Web provides data as well as processing functionality using web interfaces. Typica... more The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for automatic deployment in server runtime environments and demonstrate it with an implementation based on the open standards and open source components OGC Web Processing Service and R.
Stochastic Environmental Research and Risk Assessment (SERRA), 2002
The prediction error of a relatively simple soil acidification model (SMART2) was assessed before... more The prediction error of a relatively simple soil acidification model (SMART2) was assessed before and after calibration, focussing on the Al and NO 3 concentrations on a block scale. Although SMART2 is especially developed for application on a national to European scale, it still runs at a point support. A 5 Â 5 km 2 grid was used for application on the European scale. Block characteristic values were obtained simply by taking the median value of the point support values within the corresponding grid cell. In order to increase confidence in model predictions on large spatial scales, the model was calibrated and validated for the Netherlands, using a resolution that is feasible for Europe as a whole. Because observations are available only at the point support, it was necessary to transfer them to the block support of the model results. For this purpose, about 250 point observations of soil solution concentrations in forest soils were upscaled to a 5 Â 5 km 2 grid map, using multiple linear regression analysis combined with block kriging. The resulting map with upscaled observations was used for both validation and calibration. A comparison of the map with model predictions using nominal parameter values and the map with the upscaled observations showed that the model overestimated the predicted Al and NO 3 concentrations. The nominal model results were still in the 95% confidence interval of the upscaled observations, but calibration improved the model predictions and strongly reduced the model error. However, the model error after calibration remains rather large.
Lecture Notes in Geoinformation and Cartography, 2013
ABSTRACT Spatio-temporal aggregation of observed or predicted values for environmental phenomena ... more ABSTRACT Spatio-temporal aggregation of observed or predicted values for environmental phenomena is needed for fusing sensor data or coupling sensors and environmental models. However, estimates from sensors or environmental models can never represent our world precisely and are subject to errors. Hence, there is uncertainty in the estimates that needs to be considered in environmental model workflows. This chapter presents an approach for an error-aware spatio-temporal aggregation in the Web, where probabilistic uncertainties are used within a Monte Carlo simulation. The approach is applied in a Web-based model chain that provides uncertain crop yield predictions on field parcel level that are aggregated to larger regions.
Copulas are a flexible tool to model dependence of random variables. They cover the range from pe... more Copulas are a flexible tool to model dependence of random variables. They cover the range from perfect negative to positive dependence, include the independent case and incorporate asymmetric dependence as well as the widely used Gaussian dependence structure. The pair-copula construction for multivariate copulas exploits the ease of bivariate copulas and suggests a decomposition of a multivariate copula into a set of bivariate ones. We successfully adapted this approach for spatial data and developed a powerful spatial pair-copula based interpolation method.
This paper presents an analysis of disaggregation for PM 10 pollution from a grid to point suppor... more This paper presents an analysis of disaggregation for PM 10 pollution from a grid to point support for exposure modelling on a GPS track representing an individual space-time trajectory. Different sets of explanatory variables were tested to predict spatial variability of mobile PM 10 measurements at the point support. Disaggregation was performed using unconditional Gaussian simulation. The results show a considerable amount of uncertainty added due to disaggregation that depends in strength on the auxiliary data set used for prediction. Subsequent aggregation over the GPS track leads to a reduction in uncertainties.
Lecture Notes in Geoinformation and Cartography, 2011
Interoperable data exchange and reproducibility are increasingly important for modern scientific ... more Interoperable data exchange and reproducibility are increasingly important for modern scientific research. This paper shows how three open source projects work together to realize this: (i) the R project, providing the lingua franca for statistical analysis, (ii) the Open Geospatial Consortium's Sensor Observation Service (SOS), a standardized data warehouse service for storing and retrieving sensor measurements, and (iii) sos4R, a new project that connects the former two. We show how sos4R can bridge the gap between two communities in science: spatial statistical analysis and visualization on one side, and the Sensor Web community on the other. sos4R enables R users to integrate (near real-time) sensor observations directly into R. Finally, we evaluate the functionality of sos4R. The software encapsulates the service's complexity with typical R function calls in a common analysis workflow, but still gives users full flexibility to handle interoperability issues. We conclude that it is able to close the gap between R and the sensor web.
... Pebesma, EJ (1996), Gstat-a Program for Geostatistical Modelling, Prediction and Simulation. ... more ... Pebesma, EJ (1996), Gstat-a Program for Geostatistical Modelling, Prediction and Simulation. ... time knging of groundwater data, in M. Armstrong (ed.) Geostatistics, Kluwer Academic ... Rouhani, S. and Wackernagel, H.(1990) Multivariate geostatistical approach to space-time data ...
The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the ... more The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the atmosphere calls for policies that take account of changes in forest cover. Although much research has focused on the location and effects of deforestation, little is known about the distribution and reasons for the agricultural uses that replace forest cover. We used Landsat TM-based deforestation and agricultural census data to generate maps of the distribution and proportion of four major agricultural land uses throughout the Brazilian Amazon in 1997 and 2007. We built linear and spatial regression models to assess the determinant factors of deforestation and those major agricultural land uses e pasture, temporary agriculture and permanent agriculture e for the states of Pará, Rondônia, and Mato Grosso. The data include 30 determinant factors that were grouped into two years (1996 and 2006) and in four categories: accessibility to markets, public policies, agrarian structure, and environment. We found an overall expansion of the total agricultural area between 1997 and 2007, and notable differences between the states of Pará, Rondônia, and Mato Grosso in land use changes during this period. Regression models for deforestation and pasture indicated that determinant factors such as distance to roads were more influential in 1997 than in 2007. The number of settled families played an important role in the deforestation and pasture, the effect was stronger in 2007 than 1997. Indigenous lands were significant in preventing deforestation in high-pressure areas in 2007. For temporary and permanent agricultures, our results show that in 1997 the effect of small farms was stronger than in 2007. The mapped land use time series and the models explain empirically the effects of land use changes across the region over one decade.
The Geospatial Web provides data as well as processing functionality using web interfaces. Typica... more The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for automatic deployment in server runtime environments and demonstrate it with an implementation based on the open standards and open source components OGC Web Processing Service and R.
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Papers by Edzer Pebesma