Geostatistical Mapping Research Articles

Distribution modeling of hazardous airborne emissions from industrial campuses in Iraq via GIS techniques

The presence of considerable amounts of hazardous elements in air may represent prolonged lethal effects for the residential and/or commercial campuses and activities, especially those around the emission activities, hence it is so important to monitor and anticipate these concentrations and design an effective spatial forecasting models for that sake. Geographic information systems GIS were utilized to monitor, analyze and model the presence and concentrations for airborne Pb, Cr, and Zn elements in the atmosphere around certain industrial campuses at the northern part of Iraq. Diffusion patterns were determined for these elements via the adaptation of GIS extension; the geostatistical and spatial analysis that implement Kriging and inverse distance weighted (IDW) methods to interpolate a raster surface. The main determination factors like wind speed, ambient temperature and topographic distributions were considered in order to design a prediction model that serves as early alert of future possible accidents. Results of eight months observation program have proved that the concentrations of the three elements had significantly exceeded the Iraqi and WHO limits at most of the observed locations especially for summer times. Also, the predicted models were validated with the field measures and have proved close match especially for the geostatistical analysis map that had around 4% percentage error for the three tested elements.

Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France)

This study considers approaches for calculating and mapping statistical predictions of topsoil organic carbon (SOC) content and attendant uncertainty from data across a small region of Northern France, the Versailles Plain (221km2). Cropland topsoil SOC content was modelled through a bootstrap resampling procedure: a set of 384 models were built from cokriging up to 3 soil properties (pH, iron content, plus five earth-size fractions, being clay, coarse sand, fine sand, coarse silt, and fine silt) with an external drift (DEM or a soilscapes map). The approach provided estimation for the uncertainty globally and for each sample location, using a criterion of pointwise RMSE. The model exhibiting the lowest RMSE median of 3.17g.Kg−1 was the one including clay content and elevation covariates and had the highest validation R2 (0.52). Highest uncertainties were observed at locations marked by organic matter inputs, past landuse, calcareous soils. This knowledge gives hints for further sampling and reachable bounds for further approaches that would incorporate spatial data on agricultural practices and past landuse.

Developing geostatistical mapping tools for precision farming of rice

Developing geostatistical mapping tools for precision farming of rice

Contouring the Cataclysm: A Geographical Analysis of the Effects of the Minoan Eruption of the Santorini Volcano

ABSTRACTThis study is a step forward in understanding the palaeoenvironmental effects of the Minoan eruption of Santorini (1627–1600 BCE). We employ geostatistics to produce a prediction map for the thickness of the tephra fallout over the Eastern Mediterranean, and we reconstruct the effects by comparisons with recent eruption analogues. Based on the geostatistical map, the amount of environmental disruption over so far undocumented areas is estimated by comparison with archaeological sites where emplaced Minoan tephra has been recorded before. Nevertheless, independent field evidence suggest that the environment responded differently in places, occasionally posing challenges to the presented interpolation. A second line of evidence coming from contemporaneous fluvial archives provides clues for a widespread ‘Minoan flood’ over a large part of the Eastern Mediterranean, associated with the eruption itself. This simultaneous hydrological event may have had a counterbalancing effect on the impacts of the Minoan tephra cover, and could explain the sporadic discrepancies between the predicted effects and the palaeoenvironmental evidence. Traces of the effects of this extraordinary volcanic event are also sought in the regional Late Bronze Age literature.

Spatial variation of atmospheric nitrogen deposition and critical loads for aquatic ecosystems in the Greater Yellowstone Area

Current and historic atmospheric nitrogen (N) deposition has impacted aquatic ecosystems in the Greater Yellowstone Area (GYA). Understanding the spatial variation in total atmospheric deposition (wet + dry) of N is needed to estimate air pollution deposition critical loads for sensitive aquatic ecosystems. This is particularly important for areas that have an increasing contribution of ammonia dry deposition to total N (TN), such as the GYA. High resolution geostatistical models and maps of TN deposition (wet + dry) were developed using a variety of techniques including ordinary kriging in a geographic information system, to evaluate spatial variability and identify areas of elevated loading of pollutants for the GYA. TN deposition estimates in the GYA range from <1.4 to 7.5 kg N ha−1 yr−1 and show greater variability than wet inorganic N deposition. Critical loads of TN deposition (CLTNdep) for nutrient enrichment in aquatic ecosystems range from less than 1.5 ± 1.0 kg N ha−1 yr−1 to over 4.0 ± 1.0 kg N ha−1 yr−1 and variability is controlled by differences in basin characteristics. The lowest CLTNdep estimates occurred in high elevation basins within GYA Wilderness boundaries. TN deposition maps were used to identify critical load exceedances for aquatic ecosystems. Estimated CLTNdep exceedances for the GYA range from 17% to 48% depending on the surface water nitrate (NO3−) threshold. Based on a NO3− threshold of 1.0 μmol L−1, TN deposition exceeds CLTNdep in approximately 30% of the GYA. These predictive models and maps can be used to help identify and protect sensitive ecosystems that may be impacted by excess atmospheric N deposition.

An application of GIS analysis on structural data from metamorphic rocks in Santorini Island.

The Santorini volcanic island is located in the northern Cretan Sea and is part of the ecent subduction-related volcanic arc. The opening of Cretan Sea is the result of extension associated with a series of ductile and brittle detachment faults developed since the Middle Miocene. A detachment between two exhumed metamorphic units is exposed at Athinios, on Santorini Island. Two exhumed metamorphic units are identified that show evidence of similar brittle deformation, but distinctive ductile and ductile-brittle structures. Different thermal histories indicate that a Miocene metamorphic unit is juxtaposed structurally below an Eocene metamorphic unit. In this paper, a prediction map of structural observations in Athinios is generated with statistical and GIS software, and shows a spatial distribution consistent with the exposure of two metamorphic units. K-Mean Cluster analysis using SPSS software on lineation azimuths of the metamorphic rock units shows two populations with center values of 347.2o ± 0.73o degrees (NNW to N) and 003.4o ± 0.83o degrees (N to NNE). NNW-lineation (arc-parallel extension) population belongs to the lower Miocene metamorphic unit and the NNE-lineation represents the Eocene metamorphic unit that was affected by arc-normal extension. A geostatistical map of ordinary Kriging type displays the possible exposed tectonic contacts. This methodology provides a structural prediction map that after field verification facilitates efficient structural and thermochronological sampling.

Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA) Sampling Approach and Geostatistical Interpolation Techniques

Soil moisture (SM) plays a key role in many environmental processes and has a high spatial and temporal variability. Collecting sample SM data through field surveys (e.g., for validation of remote sensing-derived products) can be very expensive and time consuming if a study area is large, and producing accurate SM maps from the sample point data is a difficult task as well. In this study, geospatial processing techniques are used to combine several geo-environmental layers relevant to SM (soil, geology, rainfall, land cover, etc.) into a land unit area (LUA) map, which delineates regions with relatively homogeneous geological/geomorphological, land use/land cover, and climate characteristics. This LUA map is used to guide the collection of sample SM data in the field, and the field data is finally spatially interpolated to create a wall-to-wall map of SM in the study area (Garmsar, Iran). The main goal of this research is to create a SM map in an arid area, using a land unit area (LUA) approach to obtain the most appropriate sample locations for collecting SM field data. Several environmental GIS layers, which have an impact on SM, were combined to generate a LUA map, and then field surveying was done in each class of the LUA map. A SM map was produced based on LUA, remote sensing data indexes, and spatial interpolation of the field survey sample data. The several interpolation methods (inverse distance weighting, kriging, and co-kriging) were evaluated for generating SM maps from the sample data. The produced maps were compared to each other and validated using ground truth data. The results show that the LUA approach is a reasonable method to create the homogenous field to introduce a representative sample for field soil surveying. The geostatistical SM map achieved adequate accuracy; however, trend analysis and distribution of the soil sample point locations within the LUA types should be further investigated to achieve even better results. Co-kriging produced the most accurate SM map of the study area.

Soil Contamination by Heavy Metals in Playgrounds of Kindergartens in Vilnius

The soil contamination by heavy metals in playgrounds of kindergartens in Vilnius city is analysed in this article. The aim of this research is to investigate and evaluate soil contamination by heavy metals in playgrounds of kindergartens in different territories of Vilnius city. Concentrations of heavy metals were measured using Thermo Fisher Scientific Niton<TEX>$^{(R)}$</TEX> XL2 X-ray fluorescence spectrometer. Maximum allowable and background concentrations that are given in Lithuanian hygiene standard and Lithuania geochemical atlas are used to compare and evaluate concentrations of heavy metals. Concentrations of heavy metals and their spatial distribution were analysed in order to exclude the most contaminated areas relating with different functional areas of the city. Geo-statistical analysis and maps of spatial distribution were developed using IDW interpolator in ArcMap software. Detail soil surveys helps to assess the extent of anthropogenic impact in different parts of the city which can be harmful to the soil ecosystem and human health. Such researches can help to change or select different function for city areas in territorial planning process.

Ecological Drivers of Mansonella perstans Infection in Uganda and Patterns of Co-endemicity with Lymphatic Filariasis and Malaria.

BackgroundMansonella perstans is a widespread, but relatively unknown human filarial parasite transmitted by Culicoides biting midges. Although it is found in many parts of sub-Saharan Africa, only few studies have been carried out to deepen the understanding of its ecology, epidemiology, and health consequences. Hence, knowledge about ecological drivers of the vector and parasite distribution, integral to develop spatially explicit models for disease prevention, control, and elimination strategies, is limited.MethodologyWe analyzed data from a comprehensive nationwide survey of M. perstans infection conducted in 76 schools across Uganda in 2000–2003, to identify environmental drivers. A suite of Bayesian geostatistical regression models was fitted, and the best fitting model based on the deviance information criterion was utilized to predict M. perstans infection risk for all of Uganda. Additionally, we investigated co-infection rates and co-distribution with Wuchereria bancrofti and Plasmodium spp. infections observed at the same survey by mapping geographically overlapping areas.Principal FindingsSeveral bioclimatic factors were significantly associated with M. perstans infection levels. A spatial Bayesian regression model showed the best fit, with diurnal temperature range, normalized difference vegetation index, and cattle densities identified as significant covariates. This model was employed to predict M. perstans infection risk at non-sampled locations. The level of co-infection with W. bancrofti was low (0.3%), due to limited geographic overlap. However, where the two infections did overlap geographically, a positive association was found.Conclusions/SignificanceThis study presents the first geostatistical risk map for M. perstans in Uganda. We confirmed a widespread distribution of M. perstans, and identified important potential drivers of risk. The results provide new insight about the ecologic preferences of this otherwise poorly known filarial parasite and its Culicoides vector species in Uganda, which might be relevant for other settings in sub-Saharan Africa.

Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa.

BackgroundThe initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions.Methods and FindingsPre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson’s correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2–90%) in 1975.Conclusions and SignificanceThis is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the feasibility of elimination with current and novel tools.

Mapping crop diseases using survey data: The case of bacterial wilt in bananas in the East African highlands

Globally, crop diseases result in significant losses in crop yields. To properly target interventions to control crop diseases, it is important to map diseases at a high resolution. However, many surveys of crop diseases pose challenges to mapping because available observations are only proxies of the actual disease, observations often are not normally distributed and because typically convenience sampling is applied, leading to spatially clustered observations and large areas without observations. This paper addresses these challenges by applying a geostatistical methodology for disease incidence mapping. The methodology is illustrated for the case of bacterial wilt of banana (BWB) caused by Xanthomonas campestris pv. musacearum in the East African highlands. In a survey using convenience sampling, 1350 banana producing farmers were asked to estimate the percentage yield loss due to bacterial wilt. To deal with the non-normal distribution of the data, the percentages were classified into two binary variables, indicating whether or not the disease occurred and whether or not the yield loss was severe. To improve the spatial prediction of disease incidence in areas with low sampling density, the target variables were correlated in a logistic regression to a range of environmental variables, for which maps were available. Subsequently, the residuals of the regression analysis were interpolated using simple kriging. Finally, the interpolated residuals were added to the regression predictions. This so-called indicator regression kriging approach resulted in continuous maps of disease incidence. Cross-validation showed that the method yields unbiased predictions and correctly assesses the prediction accuracy. The geostatistical mapping is also more accurate than conventional mapping, which uses the mean of observations within districts as the predicted value for all locations within the district, although the accuracy improvement is not very large. The maps were also spatially aggregated to district level to support regional decision-making. The analysis showed that the disease is widespread on banana farms throughout the study area and can locally reach severe levels.

Geostatistical techniques applied to mapping limnological variables and quantify the uncertainty associated with estimates

Abstract Aim: This study aimed to map the concentrations of limnological variables in a reservoir employing semivariogram geostatistical techniques and Kriging estimates for unsampled locations, as well as the uncertainty calculation associated with the estimates. Methods: We established twenty-seven points distributed in a regular mesh for sampling. Then it was determined the concentrations of chlorophyll-a, total nitrogen and total phosphorus. Subsequently, a spatial variability analysis was performed and the semivariogram function was modeled for all variables and the variographic mathematical models were established. The main geostatistical estimation technique was the ordinary Kriging. The work was developed with the estimate of a heavy grid points for each variables that formed the basis of the interpolated maps. Results: Through the semivariogram analysis was possible to identify the random component as not significant for the estimation process of chlorophyll-a, and as significant for total nitrogen and total phosphorus. Geostatistical maps were produced from the Kriging for each variable and the respective standard deviations of the estimates calculated. These measurements allowed us to map the concentrations of limnological variables throughout the reservoir. The calculation of standard deviations provided the quality of the estimates and, consequently, the reliability of the final product. Conclusions: The use of the Kriging statistical technique to estimate heavy mesh points associated with the error dispersion (standard deviation of the estimate), made it possible to make quality and reliable maps of the estimated variables. Concentrations of limnological variables in general were higher in the lacustrine zone and decreased towards the riverine zone. The chlorophyll-a and total nitrogen correlated comparing the grid generated by Kriging. Although the use of Kriging is more laborious compared to other interpolation methods, this technique is distinguished for its ability to minimize the variance of the estimate and provide the estimated value of the degree of uncertainty.

Mapping of satellite Earth observations using moving window block kriging

Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty. Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps, (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT (Greenhouse Gases Observing Satellite) satellite, and solar induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) instrument.

Estimating ozone risks using forest monitoring networks—results for science, policy, and society

Few integration steps (adding low-cost ozone measurements, link to existing conventional monitors, joint data processing) transformed the traditional forest monitoring network into a multifunctional infrastructure producing information relevant for estimating risk to vegetation and human health Traditionally, forest monitoring networks have been designed to assess status and trends of forest condition. We argue that they can help providing answers to a much broader range of questions for science, policy, and society. Here, we concentrate on the example of ground-level ozone pollution. The aim of this study is to demonstrate the value of present forest monitoring networks as infrastructures that—with few integration steps—can provide important data and information to estimate the risk posed by ground-level ozone to vegetation and human health. We measured ozone concentration by passive samplers at the local (Trentino, northern Italy) plots of the ICP Forests Level I network over the period 2007–2011. By integrating these data with those from conventional ozone monitors (mostly located in urban areas), we (i) obtained an even distribution of air quality measurements over the investigated area, (ii) estimated international exposure indicators for vegetation and human population, and (iii) obtained data allowing geostatistical modeling and mapping of ozone concentrations, exposure, and associated potential risk. Mean May–July ozone concentration ranged from 58 to 169 μg m−3, depending on forest site and year. Modeling and mapping (root-mean-square deviation (RMSD) = 12.31 μg m−3) provided evidence that the risk threshold for vegetation in terms of AOT40 was exceeded in large parts (90 %) of the study area, and frequently even by two times, depending on the year. With respect to population, up to 43 % of the dwellers were exposed to medium–high risk of exceedances of the information threshold. Ozone measurements carried out at the ICP Forests Level I forest monitoring network permitted mapping ozone levels and the estimation of possible risk for vegetation and human health. Forest monitoring networks can be seen as infrastructures that can be useful to address a wide range of environmental issues and with a much broader scope than their original one.

Mapping of maximum snow load values for the 50-year return period for Croatia

Snow load is an important climatic element that is, together with minimum and maximum temperatures and wind load, a part of the national annex of standards for the design of structures. In particular, the characteristic snow load, defined as the maximum snow load at the ground for the 50-year return period, has to be estimated and a map has to be supplied as part of the national annex. The method for the estimation of this parameter at the station locations is presented, followed by the geostatistical mapping procedure used to estimate this parameter for the whole territory of Croatia. Snow load is defined as a product of snow density, snow depth and gravitational acceleration. Although snow depths are measured at a large number of meteorological stations, snow loads are not easy to estimate because of very few snow density measurements. To overcome this, snow density was fitted to snow depth data using linear regression according to the daily pairs of values from 13 meteorological stations. Since snow density changes with elevations and seasons, the stations are divided into three subsets according to station elevation (lower than 600 m, 600–1000 m high and higher than 1000 m) and the regression equations are also built on a monthly basis. These modelling results allowed the estimation of the snow load at 96 low-elevation stations, eight higher elevation stations and one high-altitude station based on the monthly maximum snow depths and the model estimated snow densities. Based on the monthly maximum snow loads, the series of maximum annual snow loads can be calculated for 118 locations. These series of annual maximum snow loads are the basis for the estimation of the annual maximum snow load values for the 50-year return period, by means of an extreme value distribution.Finally, the estimated annual maximum snow load values for the 50-year return period at 118 stations constitute the input for the geostatistical procedure of mapping this parameter for the territory of Croatia.

Spatial soil analysis using geostatistical analysis and map Algebra

Evaluating soil spatial variability through sampling is an important step in precision farming processes that aids farmers to make informed decisions on the spread of agricul- tural inputs. Manual sampling is essential in ascertaining soil physical characteristics and could be used to monitor the chemical components like macronutrient nitrogen (N), phos- phorus(P),and potassium(K).Thistypeofsamplinghowever could be costly and time consuming in macronutrient sam- pling. In order to show the ability of manual sampling to capture the essence of variability in the agricultural fields with enough number of samples and therefore, helping the preci- sion farming process, we conducted an experiment on differ- ent designs of random, systematic, stratifiedrandom, stratified systematic, and different sizes of samples. The experiment was carried out on the geostatistical surfaces (base maps) cre- ated from a set of data which belonged to a rice plantation in Malaysia. A krigged map for each of these schemes was cre- atedandcomparedwiththeN,P,andKbasemaps.Theresults showed that the systematic and stratified systematic schemes were the most accurate sampling schemes in terms of estima- tion of mean. However, both stratified schemes were not suc- cessful to create the standard deviation of populations. Concerning the standard error of mean when the schemes were used in linear mixed effect modeling grouped by the sample size, stratified samples could produce lower standard error (except for stratified random sample of P). In terms of reproducing the original spatial variability, only systematic sampling scheme could create better accuracy in most cases. The result also revealed that the most important property of a sampling scheme in the study area is representativeness of samples, and the number of samples does not play an impor- tant role in accuracy and map making. Therefore, the data could be equally valid for the precision farming.

Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification

Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations.Using geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible.This work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated.The study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5millionm3 (cubic meter) of gas and 1.04millionm3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9millionm3 and 0.997millionm3 for gas and water, respectively.

Cost-effective sampling of 137Cs-derived net soil redistribution: part 1 – estimating the spatial mean across scales of variation

The 137Cs technique for estimating net time-integrated soil redistribution is valuable for understanding the factors controlling soil redistribution by all processes. The literature on this technique is dominated by studies of individual fields and describes its typically time-consuming nature. We contend that the community making these studies has inappropriately assumed that many 137Cs measurements are required and hence estimates of net soil redistribution can only be made at the field scale. Here, we support future studies of 137Cs-derived net soil redistribution to apply their often limited resources across scales of variation (field, catchment, region etc.) without compromising the quality of the estimates at any scale. We describe a hybrid, design-based and model-based, stratified random sampling design with composites to estimate the sampling variance and a cost model for fieldwork and laboratory measurements. Geostatistical mapping of net (1954–2012) soil redistribution as a case study on the Chinese Loess Plateau is compared with estimates for several other sampling designs popular in the literature. We demonstrate the cost-effectiveness of the hybrid design for spatial estimation of net soil redistribution. To demonstrate the limitations of current sampling approaches to cut across scales of variation, we extrapolate our estimate of net soil redistribution across the region, show that for the same resources, estimates from many fields could have been provided and would elucidate the cause of differences within and between regional estimates. We recommend that future studies evaluate carefully the sampling design to consider the opportunity to investigate 137Cs-derived net soil redistribution across scales of variation.

PlotKML: Scientific Visualization of Spatio-Temporal Data

plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (XML package), and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of arguments, and allows users to visually explore spatio-temporal data available in R: points, polygons, gridded maps, trajectory-type data, vertical profiles, ground photographs, time series vector objects or raster images, along with the results of spatial analysis such as geostatistical mapping, spatial simulations of vector and gridded objects, optimized sampling designs, species distribution models and similar. A generic plotKML() function automatically determines the parsing order and visualizes data directly from R; lower level functions can be combined to allow for new user-created visualization templates. In comparison to other packages writing KML, plotKML seems to be more object oriented, it links more closely to the existing R classes for spatio-temporal data (sp, spacetime and raster packages) than the alternatives, and provides users with the possibility to create their own templates.

Spatial Dependence of Physical Attributes and Mechanical Properties of Ultisol in a Sugarcane Field.

This study investigates the effect of conventional tillage and application of the monoculture of sugar cane on soil health. Variables like density, moisture, texture, consistency limits, and preconsolidation stress were taken as indicators of soil quality. The measurements were made at a 120 × 120 m field cropped with sugar cane under conventional tillage. The objective of this work was to characterize the soil and to study the spatial dependence of the physical and mechanical attributes. Then, undisturbed soil samples were collected to measure bulk density, moisture content and preconsolidation stress and disturbed soil samples for classification of soil texture, and consistency limits. The soil texture indicated that soil can be characterized as sandy clay soil and a sandy clay loam soil, and the consistency limits indicated that the soil presents an inorganic low plasticity clay. The preconsolidation tests tillage in soil moisture content around 19% should be avoided or should be chosen a management of soil with lighter vehicles in this moisture content, to avoid risk of compaction. Using geostatistical techniques mapping was possible to identify areas of greatest conservation soil and greater disturbance of the ground.