Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.     

基于GIS的宜宾城市土壤Pb含量空间分布特征及污

基于ArcGIS地统计模块,对长江上游城市宜宾市的63个土壤样品进行了分析,探讨了城市土壤中重金属Pb的空间分布特征和污染状况.结果表明,城市土壤样品中Pb含量为20.3-223.9 mg·kg-1,平均含量为61.2 mg·kg-1,显著高于四川省土壤背景值,具有明显的累积效应(p=0);与四川省土壤基线值相比,宜宾...     

生态修复后某矿区下游农田土壤金属污染特征与风险评价

为研究广东省某矿区开展生态修复多年后下游农田土壤的金属污染状况,选取该矿区下游某村周边农田土壤及灌溉水渠作为研究对象,对该区域采集了40个土壤表层样本和8个水体样本,利用Arcgis软件对农田土壤样品中As、Cu、Cd、Pb、Zn、Mn和Fe₂O₃的质量分数进行克里金空间插值,解析该区域农田土壤金属的空间分布特征;采用综合污染指数法和潜在生态风险指数法对该区域耕作层土壤中As、Cu、Cd、Pb、Zn和Mn进行风险评价。结果表明,40个土壤样品中As、Cd、Cu、Zn和Pb的超标率分别为77.5%、70%、87.5%、27.5%和67.5%,说明调查区域农田土壤污染属于多金属复合污染,且对农作物的生产和安全产生巨大的威胁。部分土壤样品中As、Pb和Cd含量超过了中国农用地土壤污染风险管制值,需采取严格管控措施。通过分析土壤金属的空间分布,发现土壤金属含量超标点位主要位于灌溉口与受污染河流周边,且含量与离灌溉口距离成反比。结合目前灌溉水样中的金属均未超标的情况,得出该区域农田土壤污染是由该矿区生态环境修复前所产生的含金属灌溉水导致土壤中金属的积累...     

Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level

Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for sustainable use of groundwater. Regions in which were detected as critical areas need to be much more managed for using the current water resources efficiently. Conducting water harvesting systems especially in critical and hot areas in order to recharge the groundwater, and altering the current cropping pattern to another one that need less water requirement and applying modern irrigation techniques are highly recommended; otherwise, it is most likely that in a few years no more crop would be cultivated.     

Mapping Soil Gas Radon Concentration: A Comparative Study of Geostatistical Methods

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.     

Space-time statistics for environmental and agricultural related phenomena

This paper presents an overview of space-time statistical procedures to analyse agricultural and environmental related phenomena. It starts with an application on root-rot development in cotton. Dependence modelling in space and time is done with the space-time variogram. Various kriging interpolators are presented for making predictions in space and time. Simulated annealing is used to design an optimal monitoring network for estimation of space-time variograms. In the application no clear indication was found for anisotropy, although strong evidence exists that the disease not only proceeds within rows but also jumps between rows. The optimal sampling scheme showed a spatial clustering of observations at the first and the last monitoring day and less observations at intermediate times.     

Use of best linear unbiased prediction for hot spot identification in two-way compositing

Compositing of individual samples is a cost-effective method for estimating a population mean, but at the expense of losing information about the individual sample values. The largest of these sample values (hotspot) is sometimes of particular interest. Sweep-out methods attempt to identify the hotspot and its value by quantifying a (hopefully, small) subset of individual values as well as the usual quantification of the composites. Sweep-out design is concerned with the sequential selection of individual samples for quantification on the basis of all earlier quantifications (both composite and individual). The design-goal is for the number of individual quantifications to be small (ideally, minimal). Previous sweep-out designs have applied to traditional (i.e., disjoint) compositing. This paper describes a sweep-out design suitable for two-way compositing. That is, the individual samples are arranged in a rectangular array and a composite is formed from each row and also from each column. At each step, the design employs all available measurements (composite and individual) to form the best linear unbiased predictions for the currently unquantified cells. The cell corresponding to the largest predicted value is chosen next for individual measurement. The procedure terminates when the hotspot has been identified with certainty.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

Geostatistical spatiotemporal analysis of air temperature as an aid to delineating thermal stability zones in a potential show cave: implications for environmental management

Air temperature in several galleries of the Covadura System (Sorbas Gypsum Karst, Almería) was measured at monthly intervals over a period of 1 year. The spatial temperature distribution for each month was modeled in a geostatistical framework. The mean trend of the air temperature and the difference between each experimental temperature measurement and this trend were calculated over space and time. Both the trend and residual component were characterized using a geostatistical space-time model. A large spatial trend of the air temperature was found due to the orientation of galleries within the cave system and as a function of the distance from the main cave entrance. Kriging was used for the spatial estimation of the time covariance of the residuals. This enabled the delimitation of the cave into three zones of varying environmental risk in the event of being opened to visits by the public, according to the degree of stability of air temperature over space and time. The influence of human presence on the spatial temperature distribution was assessed using data collected during a year (2000/2001) in pilot galleries opened to the public. An average visit corresponding to August was selected comprising 16 people over a period of 53 min. This average visit influenced the spatial temperature pattern at distances of more than 90 m from the cave entrance, according to the geostatistical model adopted. Within this zone the mean thermal increment generated by human presence was estimated to be 0.26 degrees C. The spatiotemporal mathematical model of the cave air temperature has been revealed as a useful tool for the environmental management of show caves.