This page has only limited features, please log in for full access.
Sustainability of mining projects is linked to informed investment decisions based on public reporting of exploration and mineral resource estimation results. In Australia, guidelines for public reporting are established by the Joint Ore Reserves Committee reporting code through the JORC Code (2012). Although assessment of uncertainty in the results reported is a requirement, this is often communicated in a qualitative manner and subjectively evaluated. This can become a liability if not communicated effectively, particularly in early stages of mining projects when spatial domains of geological interpretation and mineralization envelopes influence reliability of resource estimates. This review describes methodologies for quantitative uncertainty assessment and communication, and explores how they could be applied in public reporting practice. The complexity, cost and additional work of doing a quantitative assessment could hinder a straightforward implementation. This could be overcome if mining companies budget for quantitative uncertainty assessment and associated professional development. A compulsory requirement for inclusion of uncertainty assessments in public reporting or use of standardized subjective language would improve industry practice.
Scott McManus; Azizur Rahman; Jacqueline Coombes; Ana Horta. Uncertainty assessment of spatial domain models in early stage mining projects – A review. Ore Geology Reviews 2021, 133, 104098 .
AMA StyleScott McManus, Azizur Rahman, Jacqueline Coombes, Ana Horta. Uncertainty assessment of spatial domain models in early stage mining projects – A review. Ore Geology Reviews. 2021; 133 ():104098.
Chicago/Turabian StyleScott McManus; Azizur Rahman; Jacqueline Coombes; Ana Horta. 2021. "Uncertainty assessment of spatial domain models in early stage mining projects – A review." Ore Geology Reviews 133, no. : 104098.
The use of virtual species to test species distribution models is important for understanding how aspects of the model development process influence model performance. Typically, virtual species are simulated by defining their niche as a function of environmental variables and simulating occurrence probabilistically via Bernoulli trials. This approach ignores endogenous processes known to drive species distribution such as dispersal and population dynamics. To understand whether these processes are important for simulating virtual species we compared the probabilistic simulation approach to those incorporating endogenous processes. This comparison was done by evaluating changes in the relationship between species occurrence and habitat suitability over a number of landscapes with varying spatial structure. We found that the combined effects of population dynamics and dispersal meant the probability of occurrence of a single cell was not only dependent on habitat suitability, but also the number of occupied cells nearby. This resulted in a dependence on the size of clusters of high suitability cells (analogous to patch size) to maintain populations, increased residual spatial autocorrelation and non‐stationarity of the species response between landscapes. These data characteristics are attributes of real species distribution data and are not present in probabilistic simulations. Researchers using virtual species should consider the importance of these characteristics to their study objectives to decide whether the inclusion of endogenous processes is necessary.
Liam Grimmett; Rachel Whitsed; Ana Horta. Creating virtual species to test species distribution models: the importance of landscape structure, dispersal and population processes. Ecography 2021, 44, 753 -765.
AMA StyleLiam Grimmett, Rachel Whitsed, Ana Horta. Creating virtual species to test species distribution models: the importance of landscape structure, dispersal and population processes. Ecography. 2021; 44 (5):753-765.
Chicago/Turabian StyleLiam Grimmett; Rachel Whitsed; Ana Horta. 2021. "Creating virtual species to test species distribution models: the importance of landscape structure, dispersal and population processes." Ecography 44, no. 5: 753-765.
The financial capacity of the Australian agriculture sector to capture the benefits of the growing food and fibre demands of the burgeoning global population has been questioned, particularly in the face of a projected climate change impacts. This paper reports on the first phase of a multi-stage project that seeks to understand the causes of rural business failure, illustrated through the metaphorical voice of the farmer. It has been constructed in three parts comprising an overview of the rationale for the consideration of the rural business failure as it is understood by the operators of stressed rural businesses; description of the method and results; and thirdly, the implications of the results and direction for future research. This paper reports on the analysis of responses of approximately 33,000 clients collected as part of the Rural Financial Counselling Services (RFCS) during the period 2012–2016. A key finding of the paper is the perception that climate variation is the primary cause for the hardship experienced; that is, in the absence of the variable climate operators would not have found themselves in need of the RFCS. However, this result necessarily requires a more objective review before consideration as the basis of new policy.
Timothy Clune; Ana Horta. Climate Variation—A Perceived Drag on Rural Business Performance. Sustainability 2020, 12, 10285 .
AMA StyleTimothy Clune, Ana Horta. Climate Variation—A Perceived Drag on Rural Business Performance. Sustainability. 2020; 12 (24):10285.
Chicago/Turabian StyleTimothy Clune; Ana Horta. 2020. "Climate Variation—A Perceived Drag on Rural Business Performance." Sustainability 12, no. 24: 10285.
A significant reduction in the costs associated with contamination assessments can be achieved if traditional soil sampling for contaminated-site characterization is complemented by real-time sampling using proximal soil sensors. Real-time sampling using a portable X-ray fluorescence (pXRF) device is a cheap and fast sampling method to provide more data and reduce the time needed to map soil contamination. The main disadvantage of using pXRF is the degree of uncertainty of these in situ measurements due to the technology’s indirect nature, and its sensitivity to soil heterogeneity and soil moisture content. This study evaluates the potential of using both pXRF and traditional soil sampling measurements to accurately map soil contamination due to the presence of heavy metals. The approach proposed uses geostatistical sequential simulation with local probability distributions to characterize and integrate pXRF uncertainty at each sampling location. The resulting maps agree with the contamination map obtained using traditional laboratory data only, in terms of mapping accuracy and extent of contaminated areas. This study shows that with few collocated pXRF and laboratory analytical data it is possible to identify contaminated areas accurately, thus providing a cost-effective solution to work with pXRF data directly.
Ana Horta; Leonardo Azevedo; João Neves; Amilcar Soares; Liana Pozza. Integrating portable X-ray fluorescence (pXRF) measurement uncertainty for accurate soil contamination mapping. Geoderma 2020, 382, 114712 .
AMA StyleAna Horta, Leonardo Azevedo, João Neves, Amilcar Soares, Liana Pozza. Integrating portable X-ray fluorescence (pXRF) measurement uncertainty for accurate soil contamination mapping. Geoderma. 2020; 382 ():114712.
Chicago/Turabian StyleAna Horta; Leonardo Azevedo; João Neves; Amilcar Soares; Liana Pozza. 2020. "Integrating portable X-ray fluorescence (pXRF) measurement uncertainty for accurate soil contamination mapping." Geoderma 382, no. : 114712.
The size and intensity of bushfires during the 2019‐2020 Australian season has been unprecedented (Nolan et al., 2020). The fires in south‐eastern Australia were extraordinary in terms of the land area burnt (7.2 million hectares; Figure 1); four times that of the 2019 Brazilian Amazon and 1.8 times the 2017 United States’ fires (Bladon, 2018). The frequency and intensity of fires are predicted to increase over coming years as the Australian climate becomes warmer and drier (Leigh et al., 2015).
Luiz G. M. Silva; Katherine E. Doyle; Deanna Duffy; Paul Humphries; Ana Horta; Lee J. Baumgartner. Mortality events resulting from Australia's catastrophic fires threaten aquatic biota. Global Change Biology 2020, 26, 1 .
AMA StyleLuiz G. M. Silva, Katherine E. Doyle, Deanna Duffy, Paul Humphries, Ana Horta, Lee J. Baumgartner. Mortality events resulting from Australia's catastrophic fires threaten aquatic biota. Global Change Biology. 2020; 26 (10):1.
Chicago/Turabian StyleLuiz G. M. Silva; Katherine E. Doyle; Deanna Duffy; Paul Humphries; Ana Horta; Lee J. Baumgartner. 2020. "Mortality events resulting from Australia's catastrophic fires threaten aquatic biota." Global Change Biology 26, no. 10: 1.
Species distribution modelling (SDM) is an important tool for ecologists, but different algorithms and different sampling strategies produce different results. Using virtual species with differing characteristics, this study investigated the effect of sampling strategy choices on SDM predictions across multiple algorithms and species, including the impacts of different sample size and prevalence choices, and the effects of validating models using presence and background data as opposed to true absences. We also assessed the consistency of predictions between algorithms, and investigated the effectiveness of using stability assessment of spatial predictions in geographic space to evaluate SDM predictions. Maxent performed most consistently under all scenarios both in regards to performance metrics and spatial prediction stability, and should be considered for most scenarios either on its own or as part of a model ensemble, in particular when true absences are not available. A key recommendation of this study is the use of metrics to assess agreement between replicate predictions as a measure of spatial stability, rather than relying solely on performance metrics such as area under the curve (AUC).
Liam Grimmett; Rachel Whitsed; Ana Horta. Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics. Ecological Modelling 2020, 431, 109194 .
AMA StyleLiam Grimmett, Rachel Whitsed, Ana Horta. Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics. Ecological Modelling. 2020; 431 ():109194.
Chicago/Turabian StyleLiam Grimmett; Rachel Whitsed; Ana Horta. 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics." Ecological Modelling 431, no. : 109194.
In the mining industry, code compliant reporting standards for public announcements have been developed setting minimum standards for public reporting of exploration results and mineral resources. These include an assessment of the quality and confidence in the data and work carried out since public reporting aims to provide information that is material, transparent and competent to investors. There are four phases required to estimate an mineral resource (preparation, investigation, model creation and validation), and estimation is highly dependent on the accuracy of the preparation stage which is a result of the quality of the geological interpretation given for the mineralization process and current spatial location. Performance of feasibility studies in mining projects has been poor, with a 50% failure rate, 17% of failures are attributable to issues in geological interpretation. This interpretation seeks to spatially define geologically homogenous areas in the resource (spatial domains), corresponding to a single statistical population with a single orientation, where possible. In the estimation workflow, the creation of the spatial domain presents a challenge in terms of assessing the uncertainty in the geological interpretation often due to the manual and subjective interpretation used to guide its creation as well as in spatial domains with several mineralization overprint events. The proposed work investigates a Bayesian method using multivariate quantitative data combined with qualitative data to assess the interpretation uncertainty of classification of borehole intervals to a spatial domain defined by a 3D ‘wireframe’ or ‘rock type’ model interpretation using either implicit or explicit modeling techniques.
Scott Mcmanus; Azizur Rahman; Ana Horta; Jacqueline Coombes. Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains. Statistics for Data Science and Policy Analysis 2020, 3 -13.
AMA StyleScott Mcmanus, Azizur Rahman, Ana Horta, Jacqueline Coombes. Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains. Statistics for Data Science and Policy Analysis. 2020; ():3-13.
Chicago/Turabian StyleScott Mcmanus; Azizur Rahman; Ana Horta; Jacqueline Coombes. 2020. "Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains." Statistics for Data Science and Policy Analysis , no. : 3-13.
Small hydropower plants (SHPs) may be considered the most cost-effective and environmentally benign technology for energy production. However, it has been suggested that the social–environmental impacts of SHPs could be reduced through improvements to the planning stage. This work aims to develop a geographic information system (GIS)-based framework to improve SHP planning in Minas Gerais State, Brazil, by providing site selection for development based on social–environmental restrictions. The framework used a GIS-based multicriteria decision analysis based on the analytical hierarchy process (AHP) to classify and weigh each attribute used. The results indicated that the number of planned SHPs is relatively high for Minas Gerais State (203), with the majority either in the Inventory or Project Assigned stages. The results also highlighted priority and nonpriority areas for SHPs by considering the likelihood of the SHPs causing the least and greatest socioenvironmental disturbances, respectively. The GIS-based framework has proved to be effective for improving SHP development in Minas Gerais State while suggesting site selection of priority areas that are likely to cause the least negative social–environmental impacts.
João Paulo Romanelli; Luiz G. M. Silva; Ana Horta; Rogério A. Picoli. Site Selection for Hydropower Development: A GIS-Based Framework to Improve Planning in Brazil. Journal of Environmental Engineering 2018, 144, 04018051 .
AMA StyleJoão Paulo Romanelli, Luiz G. M. Silva, Ana Horta, Rogério A. Picoli. Site Selection for Hydropower Development: A GIS-Based Framework to Improve Planning in Brazil. Journal of Environmental Engineering. 2018; 144 (7):04018051.
Chicago/Turabian StyleJoão Paulo Romanelli; Luiz G. M. Silva; Ana Horta; Rogério A. Picoli. 2018. "Site Selection for Hydropower Development: A GIS-Based Framework to Improve Planning in Brazil." Journal of Environmental Engineering 144, no. 7: 04018051.
Tiago Ramos; Ana Horta; Maria Gonçalves; Fernando P. Pires; Deanna Duffy; José C. Martins. The INFOSOLO database as a first step towards the development of a soil information system in Portugal. CATENA 2017, 158, 390 -412.
AMA StyleTiago Ramos, Ana Horta, Maria Gonçalves, Fernando P. Pires, Deanna Duffy, José C. Martins. The INFOSOLO database as a first step towards the development of a soil information system in Portugal. CATENA. 2017; 158 ():390-412.
Chicago/Turabian StyleTiago Ramos; Ana Horta; Maria Gonçalves; Fernando P. Pires; Deanna Duffy; José C. Martins. 2017. "The INFOSOLO database as a first step towards the development of a soil information system in Portugal." CATENA 158, no. : 390-412.
Rachel Whitsed; Ana Horta; Faezeh Marzbanrad; Herbert F Jelinek. Spatial Characterization of Hypertension Clusters using a Rural Australian Clinical Database. 2017 Computing in Cardiology Conference (CinC) 2017, 1 .
AMA StyleRachel Whitsed, Ana Horta, Faezeh Marzbanrad, Herbert F Jelinek. Spatial Characterization of Hypertension Clusters using a Rural Australian Clinical Database. 2017 Computing in Cardiology Conference (CinC). 2017; ():1.
Chicago/Turabian StyleRachel Whitsed; Ana Horta; Faezeh Marzbanrad; Herbert F Jelinek. 2017. "Spatial Characterization of Hypertension Clusters using a Rural Australian Clinical Database." 2017 Computing in Cardiology Conference (CinC) , no. : 1.
It is generally recognized that people in rural Australia and world-wide do worse in terms of health outcomes compared to the urban population. Epidemiological studies rely on large datasets obtained through national surveys but efforts to survey rural populations usually result in small datasets. Hence small datasets are often disregarded even if they are the only source of health data available to study health outcomes at the local level. The main criticism is usually lack of representativeness of the general population. In this study, a spatial modelling approach was developed to assess the representativeness of a rural Australian clinical database. We compared two methods commonly used in health geography, namely Generalized Additive Models and the spatial scan statistic. Both methods were shown to have strengths that can be exploited to detect underrepresentation of a small health dataset. We concluded that our participant data are largely representative of the underlying population and highlight focus areas for further participant recruitment, allowing disease cluster mapping to with confidence, even on the small dataset.
Rachel Whitsed; Ana Horta; Herbert F Jelinek. Assessing representativeness of a rural Australian clinical database using a spatial modelling approach. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 2017, 65, 932 -935.
AMA StyleRachel Whitsed, Ana Horta, Herbert F Jelinek. Assessing representativeness of a rural Australian clinical database using a spatial modelling approach. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2017; 65 ():932-935.
Chicago/Turabian StyleRachel Whitsed; Ana Horta; Herbert F Jelinek. 2017. "Assessing representativeness of a rural Australian clinical database using a spatial modelling approach." VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 65, no. : 932-935.
A. Horta; Brendan Malone; U. Stockmann; Budiman Minasny; Thomas Bishop; Alex McBratney; R. Pallasser; Liana Pozza. Reply to “Comment on “Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review” by Horta et al”. Geoderma 2016, 271, 256 -257.
AMA StyleA. Horta, Brendan Malone, U. Stockmann, Budiman Minasny, Thomas Bishop, Alex McBratney, R. Pallasser, Liana Pozza. Reply to “Comment on “Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review” by Horta et al”. Geoderma. 2016; 271 ():256-257.
Chicago/Turabian StyleA. Horta; Brendan Malone; U. Stockmann; Budiman Minasny; Thomas Bishop; Alex McBratney; R. Pallasser; Liana Pozza. 2016. "Reply to “Comment on “Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review” by Horta et al”." Geoderma 271, no. : 256-257.
There are tens of millions of contaminated soil sites in the world, and with an increasing population and associated risk there is a growing pressure to remediate them. A barrier to remediation is the lack of cost-effective approaches to assessment. Soil contaminants include a wide range of natural and synthetic metallic and organic compounds and minerals thus making analytical costs potentially very large. Further, soil contaminants show a large degree of spatial variation which increases the burden on sampling costs. This paper reviews potentially cost-effective methods for measurement, sampling design, and assessment. Current tiered investigation approaches and sampling strategies can be improved by using new technologies such as proximal sensing. Design of sampling can be aided by on-the-go proximal soil sensing; and expedited by subsequent adaptive spatially optimal sampling and prediction procedures enabled by field spectroscopic methods and advanced geostatistics. Field deployment of portable Visible & Near Infrared [wavelength 400–2500nm] (Vis-NIR) and X-ray fluorescence (PXRF) spectroscopies will require special calibration approaches but show huge potential for synergistic use. The use of mid-infrared spectroscopy [wavelength 2500–25,000nm, wavenumber 4000–400cm−1] (MIR) for field implementation requires further adaptive research. We propose an integrated field-deployable methodology as a basis for further developments
A. Horta; Brendan Malone; U. Stockmann; Budiman Minasny; T.F.A. Bishop; Alex McBratney; R. Pallasser; Liana Pozza. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma 2015, 241-242, 180 -209.
AMA StyleA. Horta, Brendan Malone, U. Stockmann, Budiman Minasny, T.F.A. Bishop, Alex McBratney, R. Pallasser, Liana Pozza. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma. 2015; 241-242 ():180-209.
Chicago/Turabian StyleA. Horta; Brendan Malone; U. Stockmann; Budiman Minasny; T.F.A. Bishop; Alex McBratney; R. Pallasser; Liana Pozza. 2015. "Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review." Geoderma 241-242, no. : 180-209.
T.F.A. Bishop; Ana Horta; S.B. Karunaratne. Validation of digital soil maps at different spatial supports. Geoderma 2015, 241-242, 238 -249.
AMA StyleT.F.A. Bishop, Ana Horta, S.B. Karunaratne. Validation of digital soil maps at different spatial supports. Geoderma. 2015; 241-242 ():238-249.
Chicago/Turabian StyleT.F.A. Bishop; Ana Horta; S.B. Karunaratne. 2015. "Validation of digital soil maps at different spatial supports." Geoderma 241-242, no. : 238-249.
Most pedotransfer functions (PTFs) have adopted soil texture information as the main predictor to estimate soil hydraulic properties, whether inputs are defined in terms of the relative proportion of different grain size particles or texture-based classifications. The objective of this study was to develop ternary diagrams for estimating soil water retention (θ) at − 33 and − 1500 kPa matric potentials, corresponding to the field capacity and wilting point, respectively, from particle size distribution using two geostatistical approaches. The texture triangle was divided into a 1% grid of soil texture composition resulting in 4332 different soil textures. Measured soil water retention values determined in 742 soil horizons/layers located in Portugal were then used to develop and validate the hydraulic ternary diagrams. The development subset included two-thirds of the data, and the validation subset the remaining samples. The measured soil water content values were displayed in the ternary diagram according to the coordinates given by the particles size distribution determined in the same soil samples. The volumetric water content values were then predicted for the entire ternary diagram using two different geostatistical interpolation algorithms (ordinary kriging and the empirical best linear unbiased predictor). Uncertainty analysis resulted in a root mean square error below 0.040 and 0.034 cm3 cm− 3 when comparing the interpolated water contents at − 33 and − 1500 kPa matric potential values, respectively, with the measured ones included in the validation dataset. The estimation variance calculated with both methods was also considered to access the uncertainty of the predictions. The available water content of Portuguese soils was then derived from θ− 33 kPa and θ− 1500 kPa ternary diagrams developed with both approaches. The hydraulic ternary diagrams may thus serve as simplified tools for estimating water retention properties from particle size distribution and eventually serve as an alternative to the traditional statistical regression and data mining techniques used to derive PTFs.
T.B. Ramos; A. Horta; M.C. Gonçalves; J.C. Martins; Luis Santos Pereira. Development of ternary diagrams for estimating water retention properties using geostatistical approaches. Geoderma 2014, 230-231, 229 -242.
AMA StyleT.B. Ramos, A. Horta, M.C. Gonçalves, J.C. Martins, Luis Santos Pereira. Development of ternary diagrams for estimating water retention properties using geostatistical approaches. Geoderma. 2014; 230-231 ():229-242.
Chicago/Turabian StyleT.B. Ramos; A. Horta; M.C. Gonçalves; J.C. Martins; Luis Santos Pereira. 2014. "Development of ternary diagrams for estimating water retention properties using geostatistical approaches." Geoderma 230-231, no. : 229-242.
Ana Horta; Maria João Pereira; Maria Gonçalves; Tiago Ramos; Amílcar Soares. Spatial modelling of soil hydraulic properties integrating different supports. Journal of Hydrology 2014, 511, 1 -9.
AMA StyleAna Horta, Maria João Pereira, Maria Gonçalves, Tiago Ramos, Amílcar Soares. Spatial modelling of soil hydraulic properties integrating different supports. Journal of Hydrology. 2014; 511 ():1-9.
Chicago/Turabian StyleAna Horta; Maria João Pereira; Maria Gonçalves; Tiago Ramos; Amílcar Soares. 2014. "Spatial modelling of soil hydraulic properties integrating different supports." Journal of Hydrology 511, no. : 1-9.
J. Jaime Gómez-Hernández; Ana Horta; Nicolas Jeanée. Geostatistics for environmental applications. Spatial Statistics 2013, 5, 1 -2.
AMA StyleJ. Jaime Gómez-Hernández, Ana Horta, Nicolas Jeanée. Geostatistics for environmental applications. Spatial Statistics. 2013; 5 ():1-2.
Chicago/Turabian StyleJ. Jaime Gómez-Hernández; Ana Horta; Nicolas Jeanée. 2013. "Geostatistics for environmental applications." Spatial Statistics 5, no. : 1-2.
Soil contamination assessments can be improved with new methods aimed at the accurate estimation of the volume and extension of contaminated soil to be remediated. Geostatistical models that use secondary information to characterize soil contamination are incorporated into a new integration model to provide accurate three-dimensional maps. The proposed integration model is based on a stochastic inversion approach and uses sequential indicator simulation. A two-dimensional reference image representing the areal extension of the contamination is combined with local measurements of contamination in the vertical direction, to render a three-dimensional contamination map. To demonstrate how well the integration model performs, the case study presented focuses on geophysical data and how it can be integrated with soil contamination measurements to improve the characterization of a contaminated site. The results show that the model reproduces successfully the reference image thus providing an accurate three-dimensional contamination map.
Ana Horta; Pedro Correia; Luis Pinheiro; Amilcar Soares. Geostatistical Data Integration Model for Contamination Assessment. Mathematical Geosciences 2013, 45, 575 -590.
AMA StyleAna Horta, Pedro Correia, Luis Pinheiro, Amilcar Soares. Geostatistical Data Integration Model for Contamination Assessment. Mathematical Geosciences. 2013; 45 (5):575-590.
Chicago/Turabian StyleAna Horta; Pedro Correia; Luis Pinheiro; Amilcar Soares. 2013. "Geostatistical Data Integration Model for Contamination Assessment." Mathematical Geosciences 45, no. 5: 575-590.
Soil data acquisition and assessment are crucial phases in the evaluation of soil degradation scenarios. To overcome the lack of field data, flexible sampling approaches can be used to complement conventional soil sampling. For the assessment of soil quality, it is necessary to integrate different soil support data and to provide a coherent spatial characterization of soil properties. This study proposes a new model to combine soil data from two different supports: “point” data, which refers to the concentration measured in the topsoil layer, and “bulk” data, which refers to the concentration measured for the whole soil depth sampled. The method developed uses a geostatistical co-simulation algorithm based on the experimental bi-distribution between both types of soil supports to compute co-simulated values. This new approach was applied to assess Soil Organic Carbon (SOC) availability in the topsoil. The results were used to identify critical areas in the Left Margin of the Guadiana River; an area in the South of Portugal with a high susceptibility to desertification.
Ana Horta; Amílcar Soares. Data integration model to assess soil organic carbon availability. Geoderma 2010, 160, 225 -235.
AMA StyleAna Horta, Amílcar Soares. Data integration model to assess soil organic carbon availability. Geoderma. 2010; 160 (2):225-235.
Chicago/Turabian StyleAna Horta; Amílcar Soares. 2010. "Data integration model to assess soil organic carbon availability." Geoderma 160, no. 2: 225-235.
The practice of stochastic simulation for different environmental and earth sciences applications creates new theoretical problems that motivate the improvement of existing algorithms. In this context, we present the implementation of a new version of the direct sequential co-simulation (Co-DSS) algorithm. This new approach, titled Co-DSS with joint probability distributions, intends to solve the problem of mismatch between co-simulation results and experimental data, i.e. when the final biplot of simulated values does not respect the experimental relation known for the original data values. This situation occurs mostly in the beginning of the simulation process. To solve this issue, the new co-simulation algorithm, applied to a pair of covariates Z 1(x) and Z 2(x), proposes to resample Z 2(x) from the joint distribution F(z 1,z 2) or, more precisely, from the conditional distribution of Z 2(x 0), at a location x 0, given the previously simulated value \(z_{1}^{(l)}(x_{0})\) (\(F(Z_{2}|Z_{1}=z_{1}^{(l)}(x_{0})\) ). The work developed demonstrates that Co-DSS with joint probability distributions reproduces the experimental bivariate cdf and, consequently, the conditional distributions, even when the correlation coefficient between the covariates is low.
Ana Horta; Amilcar Soares. Direct Sequential Co-simulation with Joint Probability Distributions. Mathematical Geosciences 2010, 42, 269 -292.
AMA StyleAna Horta, Amilcar Soares. Direct Sequential Co-simulation with Joint Probability Distributions. Mathematical Geosciences. 2010; 42 (3):269-292.
Chicago/Turabian StyleAna Horta; Amilcar Soares. 2010. "Direct Sequential Co-simulation with Joint Probability Distributions." Mathematical Geosciences 42, no. 3: 269-292.