A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Prediction the Spatial Air Temperature Distribution of an Experimental Greenhouse Using Geostatistical Methods

Concerning the greenhouse environment, the ultimate goal of an investigation would be to determine the climatic parameters for all locations in the study area. Objective of the present study is to analyse the distribution of air temperature and air velocity of an experimental greenhouse with tomato crop, equipped with fan and pad evaporative cooling system, using geostatistical methods. The main aspects of geostatistics in terms of theoretical background for understanding spatial correlation models and kriging applications are presented. The most common variogram models were fitted to the experimental data sets obtained during summer period from an experimental greenhouse equipped with fan and pad evaporative cooling system. The Kriging approach was applied using the semivariograms corresponded to these data sets. Finally, the prediction maps of air temperature and air velocity were produced in different height levels inside the tomato crop canopy showing a great variability. Geostatistic analysis may be applied to determine not just optimal spatial predictions but also probabilities associated with risk-based analysis in order to improve the suitability and efficiency of climatic controls systems in greenhouses

Integrating basic remote sensing, terrain analysis and geostatistical methods to generate spatially explicate continuous soil attribute maps for Fraser Experimental Forest

2010 Fall.Includes bibliographical references.Hans Jenny's Factors of Soil Formation, a system of quantitative pedology (1941), concisely summarized and illustrated many of the basic principles of pedology utilized to date (Jenny, 1941). This state factor model became the backbone for soil survey research and production because it proposed that a limited number of environmental factors could largely explain the distribution of soils within and among ecosystems. Advances in soil chemistry, soil physics, soil mineralogy, and soil biology, as well as in the basic sciences have helped increase our fundamental understanding of the spatial distribution of soil. In addition, new tools and new dimensions to the study of soil formation have evolved with the increasing power and utility of Geographical Information Systems (GIS) and geostatistical analysis to further quantify the complex spatial relationships of soils and landscapes. These advances have resulted in a new field of study termed pedometrics, which focuses on the application of mathematical and statistical methods for the study of the distribution and evolution of soils. This study implements pedometric principles and methods to develop high resolution and spatially explicate soil attribute maps for Fraser Experimental Forest (FEF) based on simple terrain, remote sensing and geostatistical analyses. The soil attribute models developed for this study provided a continuous representation of soil properties (Total soil depth, A-horizon and O-horizon thickness) at a fine scale (0.001 ha). These spatial models can be used as inputs to hydrological and ecological models to further evaluate the soil's influence on water chemistry and vegetation distributions, and to provide an initial platform for future soil survey activities in FEF. In addition to developing soil attribute surfaces for FEF, I tested the statistical, spatial and cost efficiencies of the Spatially Balances Survey (SBS) design developed to sample soils and inform the geostatistical models for FEF

A COMPARISON OF GEOSTATISTICAL AND SPATIAL AUTOREGRESSIVE APPROACHES FOR DEALING WITH SPATIALLY CORRELATED RESIDUALS IN REGRESSION ANALYSIS FOR PRECISION AGRICULTURE APPLICATIONS

Regressions such as Grain yield=f(soil,landscape) are frequently reported in precision agriculture research, and are typically computed using conventional OLS methods, implicitly ignoring spatial correlation of the residuals. This oversight can have a marked effect on the final conclusions derived from these regressions. A further issue is, which approach should be used to account for this problem? We investigated this question using a 2 year data set that includes sitespecific soil and topographic information and soybean yields and compare regression results from direct covariance representation and spatial autoregressive approaches. Our results show that the coefficients from both spatial approaches are in many cases significantly different to those from OLS, but the estimates from both spatial approaches appear to show little differences. To provide further insight into the comparison among these approaches we use a simulation of spatial random fields, with a model containing 2 independent explanatory variables and a spatially structured residual term. We then estimated the coefficients for 1000 simulations of this field and assessed their distributional properties. All methods yielded overall unbiased estimates and OLS showed the largest standard errors, while the ‘spatial’ approaches proved to be relatively consistent, although a certain neighborhood specification within the spatial autoregressive model had an evidently lower performance than the rest

Dense nonaqueous phase liquid (DNAPL) source zone characterization: Influence of hydraulic property correlation on predictions of DNAPL infiltration and entrapment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95335/1/wrcr9623.pd

Using Spatial Analysis to Study the Values of Variable Rate Technology and Information

We present a review of the last few years' literature on the economic feasibility of variable rate technology in agriculture. Much of the research on this topic has involved the estimation of site-specific yield response functions. Data used for such estimations most often inherently lend themselves to spatial analysis. We discuss the different types of spatial analyses that may be appropriate in estimating various types yield response functions. Then, we present a taxonomy for the discussion of the economics of precision agriculture technology and information. We argue that precision agriculture technology and information must be studied together since they are by nature economic complements. We contend that longer-term, multi-location agronomic experiments are needed for the estimation of ex ante optimal variable input rates and the expected profitability of variable rate technology and information gathering. We use our taxonomy to review the literature and its results with consistency and rigor.precision agriculture, spatial econometrics, variable rate technology, Research and Development/Tech Change/Emerging Technologies, C31, O33, Q16,

Mapping of penetrometer resistance in relation to tractor traffic using multivariate geostatistics

The traffic of agricultural machines can cause soil compaction and high variability of soil structure, both along normal lines and along those parallel to the field plane. The aim of this research is to investigate the potential of geostatistical techniques for understanding and evaluating the within-field spatial variability of soil compaction, caused by the traffic of agricultural machines and/or the action of tillage implements. In July 2003 soil cone penetrometer resistance was measured in a sandy-silt Cambisol of inland Sicily, where a three-year rotation wheat (Triticum durum Desf.) - wheat - tomato (Solanum lycopersicum L.) was adopted, along three parallel 3-m long transects, from the soil surface to a depth of 0.70 m. A multivariate geostatistical approach, including exploratory analysis, variography, stochastic simulation and post-processing of simulations was applied to produce thematic maps of penetrometer resistance and probability maps exceeding a critical value, corresponding to different examples of tractor movement. Penetrometer resistance variation was erratic at the surface but showed high spatial correlation between data measured at different depths. The maps of probabilistic compaction risk showed that the soil volume, exceeding the penetrometer resistance of 2.5 MPa, critical for root growth, increased from 20% to 40% after the tractor had passed through five times

On the spatial modelling of mixed and constrained geospatial data

Spatial uncertainty modelling and prediction of a set of regionalized dependent variables from various sample spaces (e.g. continuous and categorical) is a common challenge for geoscience modellers and many geoscience applications such as evaluation of mineral resources, characterization of oil reservoirs or hydrology of groundwater. To consider the complex statistical and spatial relationships, categorical data such as rock types, soil types, alteration units, and continental crustal blocks should be modelled jointly with other continuous attributes (e.g. porosity, permeability, seismic velocity, mineral and geochemical compositions or pollutant concentration). These multivariate geospatial data normally have complex statistical and spatial relationships which should be honoured in the predicted models. Continuous variables in the form of percentages, proportions, frequencies, and concentrations are compositional which means they are non-negative values representing some parts of a whole. Such data carry just relative information and the constant sum constraint forces at least one covariance to be negative and induces spurious statistical and spatial correlations. As a result, classical (geo)statistical techniques should not be implemented on the original compositional data. Several geostatistical techniques have been developed recently for the spatial modelling of compositional data. However, few of these consider the joint statistical and/or spatial relationships of regionalized compositional data with the other dependent categorical information. This PhD thesis explores and introduces approaches to spatial modelling of regionalized compositional and categorical data. The first proposed approach is in the multiple-point geostatistics framework, where the direct sampling algorithm is developed for joint simulation of compositional and categorical data. The second proposed method is based on two-point geostatistics and is useful for the situation where a large and representative training image is not available or difficult to build. Approaches to geostatistical simulation of regionalized compositions consisting of several populations are explored and investigated. The multi-population characteristic is usually related to a dependent categorical variable (e.g. rock type, soil type, and land use). Finally, a hybrid predictive model based on the advanced geostatistical simulation techniques for compositional data and machine learning is introduced. Such a hybrid model has the ability to rank and select features internally, which is useful for geoscience process discovery analysis. The proposed techniques were evaluated via several case studies and results supported their usefulness and applicability