Article
Mathematics
Ramon Giraldo, Victor Leiva, Cecilia Castro
Summary: This article provides an overview of spatial prediction methodologies for functional data, covering both stationary and non-stationary conditions. The evaluation of stationarity is an important aspect in functional random fields analysis to assess the stability of statistical properties across spatial areas. The article examines existing methodologies from the literature and offers insights into the challenges and progress in functional geostatistics. This work is significant from both theoretical and practical perspectives, offering an integrated approach tailored to the specific stationarity conditions of the functional processes under investigation.
Article
Geosciences, Multidisciplinary
Gerard B. M. Heuvelink, Richard Webster
Summary: Pedologists traditionally mapped soil by drawing boundaries, but the introduction of geostatistics and ordinary kriging in the 1980s revolutionized soil mapping. Machine learning techniques have also been adopted, but they lack transparency and spatial correlation considerations. Spatial statisticians and pedometricians have important roles in incorporating uncertainty and communicating it to end users.
SPATIAL STATISTICS
(2022)
Article
Geosciences, Multidisciplinary
Sushil Lamichhane, Lalit Kumar, Kabindra Adhikari
Summary: This study predicted and mapped the soil organic carbon content in the topsoil of the Sarlahi district in Nepal, comparing the performance of different techniques and identifying silt deposition from river systems as a key predictor for SOC content. Random Forest technique outperformed Stepwise-Multiple-Linear-Regression-Kriging in predicting SOC, indicating the importance of silt deposition in low-relief alluvial regions.
Article
Environmental Studies
Samuel Ferreira Pontes, Yuri Jacques Agra Bezerra da Silva, Vanessa Martins, Cacio Luiz Boechat, Ademir Sergio Ferreira Araujo, Jussara Silva Dantas, Ozeas S. Costa Jr, Ronny Sobreira Barbosa
Summary: The study investigated the variability of soil-erodibility factors under different soil-texture classes and found that diffuse reflectance spectroscopy (DRS) can be used to predict the USLE and RUSLE K-factors effectively.
Article
Soil Science
Xiao-Lin Sun, Budiman Minasny, Hui-Li Wang, Yu-Guo Zhao, Gan-Lin Zhang, Yun-Jin Wu
Summary: The study highlights the importance of understanding spatiotemporal changes in soil conditions for food production, environmental sustainability, and climate change adaptation. The INLA-SPDE model shows promise in accurately predicting soil properties and accounting for uncertainties in spatiotemporal soil modeling. The study recommends the use of INLA-SPDE within a hierarchical model as an effective method in studying spatiotemporal soil change.
Article
Environmental Sciences
Yibo Yan, Yong Yang
Summary: Revealing the spatiotemporal distribution and changes in regional soil heavy metals is crucial for soil pollution control and management. However, existing studies often overlook the uncertainty of such changes, leading to unreliable results. This study proposes an ST sequential Gaussian simulation (STSGS) method, using soil Pb data collected from 2016 to 2019 in a mining city in China, to reveal the spatiotemporal distribution and variation of heavy metals in regional soils, while considering their uncertainties.
ENVIRONMENTAL POLLUTION
(2023)
Article
Agronomy
R. P. Sharma, S. Chattaraj, D. Vasu, K. Karthikeyan, P. Tiwary, R. K. Naitam, B. Dash, G. Tiwari, A. Jangir, A. Daripa, S. K. Singh, S. G. Anantwar, A. M. Nimkar
Summary: A study conducted in the basaltic region of central India investigated the spatial distribution of soil fertility parameters, revealing specific variability in soil attributes crucial for precision agriculture. The study identified deficiencies in certain nutrients and strong spatial dependence of others, supporting site-specific plant nutrient management for precision farming.
ARCHIVES OF AGRONOMY AND SOIL SCIENCE
(2021)
Article
Engineering, Geological
Opeyemi E. Oluwatuyi, Rebecca Holt, Rasika Rajapakshage, Shaun S. Wulff, Kam Ng
Summary: This study assesses the inherent variability in the geomaterial parameter and develops a site investigation plan with low uncertainty by quantifying the parameter uncertainty. Using sparse borehole data to predict a site geomaterial configuration is crucial for determining the design of the investigation plan.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Environmental Sciences
M. C. Madrigal, E. Botero, C. Diaz-Avalos
Summary: In this study, geostatistical tools were used to analyze and assess the evolution of subsidence in Mexico City caused by overexploitation of groundwater. The separable variogram model was found to best represent the spatial and temporal correlation of the phenomenon, and predictions were made for future ground elevations and subsidence rates. The highest subsidence rate was found near the Mexico City International Airport.
ENVIRONMENTAL EARTH SCIENCES
(2022)
Article
Engineering, Chemical
Erdem Kucuktopcu, Bilal Cemek, Halis Simsek
Summary: The effect of insulation thickness on energy efficiency and cost savings of exterior walls for cold storage facilities in different climatic zones in Turkiye was investigated. Rock wool was found to provide the highest energy savings and shortest payback periods among the insulation materials studied.
Article
Green & Sustainable Science & Technology
Konstantinos X. Soulis, Dimitris Manolakos, Erika Ntavou, George Kosmadakis
Summary: This study developed a simulation model of a two-stage ORC engine combined with evacuated tube solar collectors and applied it throughout Greece. The results show profound spatial variability in system performance.
Article
Chemistry, Multidisciplinary
Junliang Zou, Bruce Osborne
Summary: The study in a Sitka spruce forest in central Ireland found moderate spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN), with concentrations of both decreasing from the southeast in the study area. Variability of both DOC and DTN increased as sampling area expanded, and cokriging technique outperformed ordinary kriging for predictions due to their high correlation.
APPLIED SCIENCES-BASEL
(2021)
Article
Environmental Studies
Jonas Kloeckner, Joao Lucas O. Alves, Flavio H. T. Silva, Octavio R. A. Guimaraes, Marcel A. A. Bassani, Joao Felipe C. L. Costa
Summary: Managing uncertainty and risk is crucial for sustainable mineral extraction, as high-risk operations may fail to meet quality standards and reduce resource value. However, assessing risk in multivariate deposits like bauxite is challenging due to the complex relationships between variables. This study proposes a workflow using multivariate geostatistical simulations to measure risk and uncertainty in such deposits, aiming for sustainable gains in mining operations.
Article
Soil Science
Alexandre M. J. -C. Wadoux, Dick J. Brus
Summary: The study suggests that comparison of sampling designs for mapping should be based on the sampling distribution of map quality indices. In two different case studies, the performance of simple random sampling and conditioned Latin hypercube sampling showed large variations and significant overlaps at different sample sizes.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2021)
Article
Engineering, Civil
Robin Keegan-Treloar, Adrian D. Werner, Dylan J. Irvine, Eddie W. Banks
Summary: Hydraulic head distributions were investigated using Ordinary Indicator Co-Kriging in the Doongmabulla Springs Complex in Queensland, Australia, providing insights into the likelihood of different source aquifers supporting spring flow. Significant uncertainty in conceptual model assessment was identified due to data scarcity and variability.
JOURNAL OF HYDROLOGY
(2021)
Article
Soil Science
Gerard B. M. Heuvelink, Marcos E. Angelini, Laura Poggio, Zhanguo Bai, Niels H. Batjes, Rik van den Bosch, Deborah Bossio, Sergio Estella, Johannes Lehmann, Guillermo F. Olmedo, Jonathan Sanderman
Summary: This study utilized machine learning methods to predict the spatial and temporal variation of SOC stocks in Argentina, showing that machine learning methods can provide valuable information to land managers and policymakers when provided with a sufficient density of SOC observations.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2021)
Article
Geosciences, Multidisciplinary
Mengxiao Liu, Shan Hu, Yong Ge, Gerard B. M. Heuvelink, Zhoupeng Ren, Xiaoran Huang
Summary: By studying poverty determinants, it was found that the most important factor influencing poverty in Yunyang County is the accessibility index, which has a linear relationship with poverty. Random forest (RF) and multiple linear regression (MLR) methods showed consistency in identifying the three main poverty determinants.
SPATIAL STATISTICS
(2021)
Article
Soil Science
Cynthia C. E. van Leeuwen, Vera L. Mulder, Niels H. Batjes, Gerard B. M. Heuvelink
Summary: There is a growing demand for high-quality soil data, with this study quantifying uncertainties in wet chemistry soil data using a linear mixed-effects model. Experimental measurement design and replicate measurements were found to be crucial for accurate uncertainty quantification in soil data.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2022)
Article
Soil Science
Luc Steinbuch, Dick J. Brus, Gerard B. M. Heuvelink
Summary: This study aimed to evaluate if extending a Bayesian Generalized Linear Model (BGLM) to a Bayesian Generalized Linear Geostatistical Model (BGLGM) is worth it for mapping binary soil properties. The results showed that BGLGM performs considerably better than BGLM in terms of statistical validation metrics when a large observation sample and few relevant covariates are available, although it is more demanding in terms of calibration and application.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2022)
Article
Soil Science
Alexandre M. J-C Wadoux, Dennis J. J. Walvoort, Dick J. Brus
Summary: In this study, a more appropriate and effective method of map evaluation using Taylor and solar diagrams is recommended. These summary diagrams can visualize different aspects of map quality from the relationship between statistical indices, providing better insights compared to single or extensive list of indices.
Article
Geosciences, Multidisciplinary
Gerard B. M. Heuvelink, Richard Webster
Summary: Pedologists traditionally mapped soil by drawing boundaries, but the introduction of geostatistics and ordinary kriging in the 1980s revolutionized soil mapping. Machine learning techniques have also been adopted, but they lack transparency and spatial correlation considerations. Spatial statisticians and pedometricians have important roles in incorporating uncertainty and communicating it to end users.
SPATIAL STATISTICS
(2022)
Article
Soil Science
Bertin Takoutsing, Gerard B. M. Heuvelink, Jetse J. Stoorvogel, Keith D. Shepherd, Ermias Aynekulu
Summary: Digital soil mapping (DSM) approaches provide soil information by utilizing the relationship between soil properties and environmental variables. This study incorporates measurement error variances in the geostatistical models of soil properties, weights measurements according to their accuracy, and assesses the effects of measurement errors on DSM outputs. The results show that considering measurement errors in the models allows for more accurate quantification of prediction uncertainty. This approach is important for improving soil property estimation and uncertainty quantification in DSM.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2022)
Article
Soil Science
Andree M. Nenkam, Alexandre M. J. -C. Wadoux, Budiman Minasny, Alex B. McBratney, Pierre C. S. Traore, Gatien N. Falconier, Anthony M. Whitbread
Summary: Digital soil mapping has been successfully used for various applications since the early 2000s. However, the availability of soil data globally is uneven, posing challenges for fitting digital soil mapping (DSM) models. This study explores the possibility of transferring soil information through geographic model extrapolation within homosoils. Soil data from Mali, West Africa and its homosoils were collected, and a regression tree model was fitted to the data. The study concludes that extrapolating models within homosoils is possible and leads to more accurate maps compared to existing global and continental maps.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2022)
Article
Agronomy
Tijn L. Van Orsouw, Vera L. Mulder, Jeroen M. Schoorl, Gera J. Van Os, Everhard A. Van Essen, Karin H. J. Pepers, Gerard B. M. Heuvelink
Summary: Soil compaction poses a severe threat to agricultural productivity, leading to significant yield losses. Quantifying the level of compaction is crucial for optimal management. The commonly used indicator, bulk density, is expensive and time-consuming, making it less practical for farmers. Alternatively, measurements of penetration resistance offer a cheaper and quicker option, but uncertainty exists due to varying thresholds. The research findings highlight the substantial impact of measurement choice on soil compaction studies.
Article
Environmental Sciences
Maria Eliza Turek, Laura Poggio, Niels H. Batjes, Robson Andre Armindo, Quirijn de Jong van Lier, Luis de Sousa, Gerard B. M. Heuvelink
Summary: The development of point-based global maps of soil water retention improves the availability and quality of soil data, compared to traditional map-based approaches. By combining measured and predicted data with environmental variables, this study demonstrates the superior performance of the point-based mapping approach.
INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH
(2023)
Article
Soil Science
M. E. Angelini, G. B. M. Heuvelink, P. Lagacherie
Summary: This study aimed to help urban planners preserve soils of the highest quality by mapping a soil potential multifunctionality index. However, the prediction accuracy was poor and further improvements are needed.
EUROPEAN JOURNAL OF SOIL SCIENCE
(2023)
Article
Soil Science
Stephan van der Westhuizen, Gerard B. M. Heuvelink, David P. Hofmeyr
Summary: In digital soil mapping, traditional univariate methods neglect the dependence structure between soil properties, while multivariate machine learning models can capture complex non-linear relationships and maintain the dependence structure. This study compares the performance of a multivariate random forest model with two separate univariate random forest models, and finds that the multivariate model outperforms in maintaining the dependence structure and producing more realistic results.
Article
Ecology
Alexandre M. J. -C. Wadoux, Gerard B. M. Heuvelink
Summary: Global, continental and regional maps of natural resources are important for assessing ecosystem response to human disturbance and global warming. However, these maps suffer from multiple error sources, and it is important to report the associated uncertainties for users to evaluate their reliability.
METHODS IN ECOLOGY AND EVOLUTION
(2023)
Article
Multidisciplinary Sciences
Alexandre M. J-C. Wadoux, Mercedes Roman Dobarco, Brendan Malone, Budiman Minasny, Alex B. McBratney, Ross Searle
Summary: This article introduces a new dataset of high-resolution gridded total soil organic carbon content data across Australia. The dataset includes six maps of soil organic carbon content at two resolutions and provides uncertainty estimates. The maps were obtained through interpolation of organic carbon measurements and validation showed small errors and adequate prediction uncertainty. These soil carbon maps are important for monitoring carbon stock changes and assessing the influence of climate change, land management, and greenhouse gas offset.