Geostatistics

Brenning, A. & K. G. van den Boogaart (2001): Geostatistics without Stationarity Assumptions within GIS. Proceedings of the Annual Conference of the International Association for Mathematical Geology (IAMG), 6-12 Sep 2001, Cancún, Mexico, 14 pp.

by Alexander Brenning

Plattner, C., L. N. Braun & A. Brenning (2004): The spatial variability of snow accumulation on Vernagtferner, Austrian Alps, in Winter 2003/2004. Zeitschrift für Gletscherkunde und Glazialgeologie, 39: 43-57.

by Alexander Brenning

Comparación de métodos de interpolación para la realización de mapas de precipitación para el acuífero de Icod-Cañadas (Tenerife, Islas Canarias)

by Tatiana Izquierdo

… de diferentes métodos de interpolación para la realización de mapas de precipitación en la isla de La Gomera (Islas Canarias): Evaluación de la incorporación …

by Tatiana Izquierdo

Testing local versions of correlation coefficients

by Stamatis Kalogirou

The aim of this paper is to define and test local versions of standard correlation coefficients in statistical... more The aim of this paper is to define and test local versions of standard correlation coefficients in statistical analysis. This research is motivated by the increasing number of applications using local versions of explanatory spatial data analysis methods such as local regression. Local statistical methods should be applied together with local measures of statistical inference in order to check their performance and to provide an indication of the quality of their results. One example of local explanatory data analysis method is the Geographically Weighted Regression, the application of which allows the researcher to check for the existence of spatial nonstationarity in the relationships between a geographic phenomenon and its determinants. In this paper, a local version of Pearson correlation coefficient is defined and applied to internal migration data in Sweden. The results suggest that globally independent variables are not necessarily independent locally, thus the independence criterion may be violated when local regression analysis is performed. Thus, the results of local regression analysis should be presented in light of the local statistical inference and their interpretation should be made with care.

CFP - Geoinformatica - An International Journal (GIIJ)

by J. Stewart

Computer Science Journals (CSC Journals)

Computer Science Journals (CSC Journals) invites researchers, editors, scientists & scholars to publish their... more Computer Science Journals (CSC Journals) invites researchers, editors, scientists & scholars to publish their scientific research papers in a Geoinformatica - An International Journal (GIIJ) Volume 2, Issue 2.

Fast and mobile Internet access as well as a comprehensive standardization of the data exchange has opened the door to many new applications. Typical examples are spatial data infrastructures. They enable an overlay of data that reside on different computers but are geometrically referred to the same area. Spatial data infrastructures revolutionize the relations of citizens with administration and economy. Another booming field is location based services. This links mobile devices with spatial and temporal data. The most advanced concept is called "Ubiquitous GIS" which handles a great number of position-coded mobile objects such as containers within one system. The automatic administration of those objects has already gained importance within logistic enterprises.

According to the above mentioned thoughts, Geoinformatica – An International Journal (GIIJ) aims at publishing scientific and technical developments in the diverse field of Geoinformatics. GIIJ covers all aspects and information on scientific and technical advances in the geomatic sciences. The journal is providing a platform for exploring research, development and innovative applications in geographic information science and related areas. GIIJ provides a privileged view of what is currently happening in the field of geoinformatics as well as a preview of what could be the hottest developments and research topics in the near future. Additionally, it includes recent research results on spatial databases, spatial ontologies, computational geometry and visualization for geographic information systems, geostatistics and spatial statistics, spatial analysis, interoperability, and innovative applications of geotechnologies.

CSC Journals anticipate and invite papers on any of the following topics:


Applied Geography

Close Range and Videometric Photogrammetry

Computational Geometry and Visualization

Digital Mapping

Distributed GIS/GIS and the Internet

Geo Tags

Geodata: Capture, Sources and Standards

Geographic Data

Geographic Information

Geographic Information Science

Geoinformatics

Geospatial Applications

Geospatial Databases

Geospatial Processing

Geospatial Web

Geostatistics

Global Positioning System

Guidance Systems

Integrated Geodesy

land and Geographic Information Systems

Location-Based Services

Map Services

Mobile Maps

Remote Sensing

Sensor Networks

Sensor Web

Spatial Cognition

Spatial Data Analysis

Spatial Databases

Spatial Ontologies and Interoperability

Surface Modelling

Web Services

Important Dates - GIIJ CFP - Volume 2, Issue 2.
Paper Submission: March 31, 2012
Author Notification: May 15, 2012
Issue Publication: June 2012

For complete details about GIIJ archives publications, abstracting/indexing, editorial board and other important information, please refer to GIIJ homepage (http://www.cscjournals.org/csc/journals/GIIJ/description.php?JCode=GIIJ).

We look forward to receive your valuable papers. If you have further questions please do not hesitate to contact us at cscpress@cscjournals.org. Our team is committed to provide a quick and supportive service throughout the publication process.


A complete list of journals can be found at http://www.cscjournals.org/csc/bysubject.php

vesper

by Budiman Minasny

VESPER (Variogram Estimation and Spatial Prediction plus Error) is a user-friendly PC-windows software program that... more VESPER (Variogram Estimation and Spatial Prediction plus Error) is a user-friendly PC-windows software program that can calculate andmodel global local variograms and do global and local kriging in either punctual or block form

3D modelling of particle-size distributions in the shallow subsurface: Zeeland, The Netherlands

by Sam Roberson

Co-authored with Gert Jan Weltje published in Proceedings of IAMG Salzburg

A method for 3D interpolation of complete particle-size distributions is presented and applied to the Dutch province... more A method for 3D interpolation of complete particle-size distributions is presented and applied to the Dutch province of Zeeland. Particle-size data are integrated into a common format using a cubic-spline interpolation scheme to create distributions with twenty-three particle-size categories ranging from 64 mm to 0.2 µm. Interpolation uses linear regression of log-ratio transformed particle-size distributions to estimate percentage frequency values for each of the twenty-three particle-size categories for each grid cell. The voxel model, has a spatial resolution (x,y,z) of 100 by 100 by 0.5 m and consists of ~1.7 billion voxel cells. This contains percentage frequency estimates for twenty-three particle-size categories. A full range standard particle-size statistics, e.g. mean, sorting, skewness and cumulative percentiles, can be calculated from these data.

Areal distribution of rock glaciers in the Argentera Massif (Maritime Alps) as a tool for recent glacial evolution reconstruction

by Adriano Ribolini

Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation

by Budiman Minasny

This paper demonstrates the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based... more This paper demonstrates the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior distribution of variogram parameters and the coefficients of a linear spatial model, and derive estimates of predictive uncertainty. The DREAM method runs multiple different Markov chains in parallel and jumps in each chain are generated from a discrete proposal distribution containing a fixed multiple of the difference of the states of
randomly chosen pairs of other chains. This approach automatically scales the orientation and scale of the proposal distribution, and is especially designed to maintain detailed balance and ergodicity, thereby generating an exact approximation of the posterior probability density function (pdf) of the parameters of the linear model and variogram. This approach is tested using three different data sets from Australia involving
variogram estimation of soil thickness, kriging of soil pH, and spatial prediction of soil organic carbon content.
The results showed some advantages of MCMC over the conventional method of moments and residual maximum likelihood (REML) estimation. The posterior pdf derived with MCMC conveys important information about parameter uncertainty, multi-dimensional parameter correlation, and thus how many significant parameters are warranted by the calibration data. Parameter uncertainty constitutes only a small
part of total prediction uncertainty for the case studies considered here. The prediction accuracies using MCMC and REML are similar. The variogram estimated using conventional approaches (method of moments, and without simulation) lies within the 95% prediction uncertainty interval of the posterior distribution
derived with DREAM. Altogether our results show that conventional kriging and regression-kriging still
remain a viable option for production mapping.

Fusion of airborne LIDAR and satellite multispectral data for the estimation of timber volume in an Alpine region

by Markus Neteler

Tonolli, S., Dalponte, M., Gianelle, D., Neteler, M., Rodeghiero, M., and Vescovo, L. (2011). Fusion of airborne LIDAR and satellite multispectral data for the estimation of timber volume in an Alpine region. Remote Sensing of Environment, 115: 2486-2498.

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the... more Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km2 of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.
Research highlights

► Fusion of LiDAR and multispectral data improve stem volume estimation. ► LiDAR variables provide the majority of the explanative contribution. ► Multispectral variables alone provide limited contribution. ► The models presented are effective for stem volume estimation over large areas.

Keywords: LiDAR; Multispectral; Stem volume estimation; Forest; Mountain region

Climatic factors driving invasion of the tiger mosquito (Aedes albopictus) into new areas of Trentino, Northern Italy

by Markus Neteler

Roiz D., Neteler M., Castellani C., Arnoldi D., Rizzoli A., 2011: Climatic factors driving invasion of the tiger mosquito (Aedes albopictus) into new areas of Trentino, Northern Italy. PLoS ONE. 6(4): e14800

Background

The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into... more Background

The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into more northerly latitudes as well as into higher altitudes in northern Italy. Changes in the pattern of distribution of the tiger mosquito may affect the potential spread of infectious diseases transmitted by this species in Europe. Therefore, predicting suitable areas of future establishment and spread is essential for planning early prevention and control strategies.

Methodology/Principal Findings

To identify the areas currently most suitable for the occurrence of the tiger mosquito in the Province of Trento, we combined field entomological observations with analyses of satellite temperature data (MODIS Land Surface Temperature: LST) and human population data. We determine threshold conditions for the survival of overwintering eggs and for adult survival using both January mean temperatures and annual mean temperatures. We show that the 0°C LST threshold for January mean temperatures and the 11°C threshold for annual mean temperatures provide the best predictors for identifying the areas that could potentially support populations of this mosquito. In fact, human population density and distance to human settlements appear to be less important variables affecting mosquito distribution in this area. Finally, we evaluated the future establishment and spread of this species in relation to predicted climate warming by considering the A2 scenario for 2050 statistically downscaled at regional level in which winter and annual temperatures increase by 1.5 and 1°C, respectively.

Conclusions/Significance

MODIS satellite LST data are useful for accurately predicting potential areas of tiger mosquito distribution and for revealing the range limits of this species in mountainous areas, predictions which could be extended to an European scale. We show that the observed trend of increasing temperatures due to climate change could facilitate further invasion of Ae. albopictus into new areas.

Terra and Aqua satellites track tiger mosquito invasion: modeling the potential distribution of Aedes albopictus in north-eastern Italy

by Markus Neteler

Neteler, M., Roiz, D., Rocchini, D., Castellani, C. and Rizzoli, A. (2011). Terra and Aqua satellites track tiger mosquito invasion: modeling the potential distribution of Aedes albopictus in north-eastern Italy. International Journal of Health Geographics, 10:49

Background

The continuing spread of the Asian tiger mosquito Aedes albopictus in Europe is of increasing... more Background

The continuing spread of the Asian tiger mosquito Aedes albopictus in Europe is of increasing public health concern due to the potential risk of new outbreaks of exotic vector-borne diseases that this species can transmit as competent vector. We predicted the most favorable areas for a short term invasion of Ae. albopictus in north-eastern Italy using reconstructed daily satellite data time series (MODIS Land Surface Temperature maps, LST). We reconstructed more than 11,000 daily MODIS LST maps for the period 2001-09 (i.e. performed spatial and temporal gap-filling) in an Open Source GIS framework. We aggregated these LST maps over time and identified the potential distribution areas of Ae. albopictus by adapting published temperature threshold values using three variables as predictors (0 degree C for mean January temperatures, 11 degree C for annual mean temperatures and 1350 growing degree days filtered for areas with autumnal mean temperatures > 11 degree C ). The resulting maps were integrated into the final potential distribution map and this was compared with the known current distribution of Ae. albopictus in north-eastern Italy.
Results

LST maps show the microclimatic characteristics peculiar to complex terrains, which would not be visible in maps commonly derived from interpolated meteorological station data. The patterns of the three indicator variables partially differ from each other, while winter temperature is the determining limiting factor for the distribution of Ae. albopictus. All three variables show a similar spatial pattern with some local differences, in particular in the northern part of the study area (upper Adige valley).
Conclusions

Reconstructed daily land surface temperature data from satellites can be used to predict areas of short term invasion of the tiger mosquito with sufficient accuracy (200 m pixel resolution size). Furthermore, they may be applied to other species of arthropod of medical interest for which temperature is a relevant limiting factor. The results indicate that, during the next few years, the tiger mosquito will probably spread toward northern latitudes and higher altitudes in north-eastern Italy, which will considerably expand the range of the current distribution of this species.

Open Source geocomputation: using the R data

by Markus Neteler

GIS and the Random Forest Predictor: Integration in R for Tick-Borne Disease Risk Assessment

by Markus Neteler

C. Furlanello, M. Neteler, S. Merler, S. Menegon, S. Fontanari, A. Donini, A. Rizzoli, and C. Chemini. GIS and the randomForest Predictor: integration in R for tick-borne disease risk assessment. Distributed Statistical Computing 2003, Vienna, 20-22 March, 2003.

We discuss how sophisticated machine learning methods may be rapidly integrated within a GIS for the development of... more We discuss how sophisticated machine learning methods may be rapidly integrated within a GIS for the development of new approaches in landscape epidemiology. A multitemporal predictive map is obtained by modeling in R, analyzing geodata and digital maps in GRASS, and managing biodata samples and weather data in PostgreSQL. In particular, we present a risk mapping system for tick-borne diseases, applied to model the risk of exposure to Lyme borreliosis and tick-borne encephalitis (TBE) in Trentino, Italian Alps.

Open source geocomputation: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems

by Markus Neteler

Bivand, R., Neteler, M. (2000): Open Source geocomputation: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems. Proc. 5th conference on GeoComputation, 23-25 August 2000, University of Greenwich, U.K.

We report on work in progress on the integration of the GRASS GIS, the R data analysis programming language and... more We report on work in progress on the integration of the GRASS GIS, the R data analysis programming language and environment, and the PostgreSQL database system. All of these components are released under Open Source licenses. This means that application programming interfaces are documented both in source code and in other materials, simplifying insight into the workings of the respective systems. Current versions of this note and accompanying code are to be found at the Hannover GRASS site, together with earlier papers on related topics.

Integración de datos de calidad de aguas subterráneas mediante métodos geoestadísticos

by Juan Pedro Ruiz Castellano

En este trabajo se describe una metodología geoestadística para la integración de datos decalidad de aguas y su... more En este trabajo se describe una metodología geoestadística para la integración de datos decalidad de aguas y su aplicación a la creación de cubiertas temáticas en el contexto de la gestión de losrecursos hídricos subterráneos. El procedimiento de análisis numérico se fundamenta en las siguientesetapas: análisis estadístico multivariante de los datos experimentales (Análisis Factorial y Análisis deComponentes Principales), integración de datos mediante regresión logística y creación de la cubierta deaptitud del agua para consumo humano, mediante estimación espacial por “krigeaje”.El desarrollo de la metodología se explica a través de un estudio hidroquímico relacionado conla calidad de los recursos hídricos subterráneos del acuífero detrítico “Vega de Granada”, de gran interéssocioeconómico y para el que se está implementando un SIG para la gestión de sus recursos y de lacalidad de las aguas subterráneas.

Brenning A., Piotraschke H, Leithold P. 2008. Geostatistical analysis of on-farm trials in Precision Agriculture. In J. M. Ortiz & X. Emery (eds.), GEOSTATS 2008, Proceedings of the Eighth International Geostatistics Congress, 1-5 December 2008, Santiago, Chile, 2: 1131-1136.

by Alexander Brenning

Geostatistical methods are important tools for the assessment of site‐specific management (SSM) approaches in on‐farm... more Geostatistical methods are important tools for the assessment of site‐specific management (SSM) approaches in on‐farm research (OFR) on variable rate technology (VRT) based on high-resolution yield and environmental data. As a case study we analyze an on-farm trial assessing an SSM procedure for winter wheat. A simulation study is used to evaluate different spatial linear models (generalized-least-squares - GLS regression and spatial autoregressive - SAR error models) and ordinary-least-squares regression in terms of estimation bias,
efficiency and computational challenges. All spatial linear models produce comparable results in the estimation of linear model coefficients with small differences in efficiency, but in some cases considerable bias in the estimation of autocorrelation parameters; they are clearly superior to the non-spatial model. Regression by GLS with a variogram fitted to OLS residuals is computationally the least demanding approach and is comparable to the other spatial models.

Análisis geoestadístico de las áreas de entrenamiento en la clasificación digital de imágenes de satélite

by Juan Pedro Ruiz Castellano

co-autor: Mario Chica Olmo

Este trabajo presenta una metodología aplicada al estudio de separabilidad de clases temáticas en clasificación... more Este trabajo presenta una metodología aplicada al estudio de separabilidad de clases temáticas en clasificación digital de imágenes de satélite. La metodología se fundamenta en el análisis de la función variograma, que caracteriza desde el punto de vista geoestadístico la variabilidad espacial de los valores digitales. A través del cálculo y modelación del variograma se han obtenido un conjunto de parámetros que permiten caracterizar las áreas de entrenamiento de las clases temáticas y analizar la homogeneidad espacial de éstas. Con ello se pretende alcanzaruna mejora de los resultados de la clasificación digital supervisada.

Aplicación de la función variograma al análisis de cambios espacio-temporales en imágenes Landsat TM.

by Juan Pedro Ruiz Castellano

Coautores: Mario Chica Olmo y Juan Pedro Rigol Sánchez

En este trabajo se presenta una metodología geoestadística basada en el análisis variográfico aplicado al estudio de... more En este trabajo se presenta una metodología geoestadística basada en el análisis variográfico aplicado al estudio de cambios espacio-temporales en imágenes Landsat TM. El método utiliza la función variograma pseudo-cruzado para cuantificar las diferencias (cambios) espaciales de los valores digitales de las dos imágenes comparadas. La metodología consta de una etapa inicial de corrección geométrica y radiométrica de las imágenes, a efectos de facilitar el análisis comparativo posterior de las mismas. A partir de estas imágenes, se calcula la función variograma pseudocruzado en un contexto de vecindad local, aplicando según el caso de estudio distintos tamaños de ventana, 3x3, 5x5 píxeles, etc. De esta forma, para cada pareja de bandas espectrales comparadas o de sus transformadas (componentes principales, NDVI) se obtiene una imagen de co-textura que refleja los cambios ocurridos en el área de estudio. En el texto se recogen tanto los aspectos metodológicos como su aplicación a un sector piloto localizado al noroeste de la ciudad de Granada, para el que se disponen de un conjunto de imágenes Landsat TM. Esta región es particularmente interesante por presentar un gran dinamismo en los cambios de uso del suelo, particularmente ligado a la expansión del suelo urbano a costa del uso tradicional agrícola.