The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Budiman Minasny - One of the best experts on this subject based on the ideXlab platform.
-
description and spatial inference of Soil Drainage using matrix Soil colours in the lower hunter valley new south wales australia
PeerJ, 2018Co-Authors: Brendan P Malone, Alex B Mcbratney, Budiman MinasnyAbstract:: Soil colour is often used as a general purpose indicator of internal Soil Drainage. In this study we developed a necessarily simple model of Soil Drainage which combines the tacit knowledge of the Soil surveyor with observed matrix Soil colour descriptions. From built up knowledge of the Soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining Soils generally follows the colour sequence of red → brown → yellow → grey → black Soil matrix colours. For each Soil profile, Soil Drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference Soil colours of the Soil Drainage colour sequence. The estimation of Drainage index at each profile incorporates the whole-profile descriptions of Soil colour where necessary, and is weighted such that observation of Soil colour at depth and/or dominantly observed horizons are given more preference than observations near the Soil surface. The Soil Drainage index, by definition disregards surficial Soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of Soil Drainage we digitally mapped the index across our study area. Spatial inference of the Drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson's correlation coefficients indicated the variables most strongly correlated with Soil Drainage were topographic wetness index (-0.34), mid-slope position (-0.29), multi-resolution valley bottom flatness index (-0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of Soil Drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 Soil Drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of Soil colour description, these results are acceptable. Furthermore, the spatial distribution of Soil Drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique Soil colour and Soil Drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.
-
Description and spatial inference of Soil Drainage using matrix Soil colours in the Lower Hunter Valley, New South Wales, Australia
PeerJ Inc., 2018Co-Authors: Brendan P Malone, Alex B Mcbratney, Budiman MinasnyAbstract:Soil colour is often used as a general purpose indicator of internal Soil Drainage. In this study we developed a necessarily simple model of Soil Drainage which combines the tacit knowledge of the Soil surveyor with observed matrix Soil colour descriptions. From built up knowledge of the Soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining Soils generally follows the colour sequence of red → brown → yellow → grey → black Soil matrix colours. For each Soil profile, Soil Drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference Soil colours of the Soil Drainage colour sequence. The estimation of Drainage index at each profile incorporates the whole-profile descriptions of Soil colour where necessary, and is weighted such that observation of Soil colour at depth and/or dominantly observed horizons are given more preference than observations near the Soil surface. The Soil Drainage index, by definition disregards surficial Soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of Soil Drainage we digitally mapped the index across our study area. Spatial inference of the Drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with Soil Drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of Soil Drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 Soil Drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of Soil colour description, these results are acceptable. Furthermore, the spatial distribution of Soil Drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique Soil colour and Soil Drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged
-
Digital mapping of a Soil Drainage index for irrigated enterprise suitability in Tasmania, Australia
Soil Research, 2014Co-Authors: D. B. Kidd, Brendan P Malone, Budiman Minasny, Alexander B. Mcbratney, M. A. WebbAbstract:An operational Digital Soil Assessment was developed to inform land suitability modelling in newly commissioned irrigation schemes in Tasmania, Australia. The Land Suitability model uses various Soil parameters, along with other climate and terrain surfaces, to identify suitable areas for various agricultural enterprises for a combined 70 000-ha pilot project area in the Meander and Midlands Regions of Tasmania. An integral consideration for irrigable suitability is Soil Drainage. Quantitative measurement and mapping can be resource-intensive in time and associated costs, whereas more ‘traditional’ mapping approaches can be generalised, lacking the detail required for statistically validated products. The project was not sufficiently resourced to undertake replicated field-Drainage measurements and relied on expert field Drainage estimates at ~930 sites (260 of these for independent validation) to spatially predict Soil Drainage for both areas using various terrain-based and remotely sensed covariates, using three approaches: (a) decision tree spatial modelling of discrete Drainage classes; (b) regression-tree spatial modelling of a continuous Drainage index; (c) regression kriging (random-forests with residual-kriging) spatial modelling of a continuous Drainage index. Method b was chosen as the best approach in terms of interpretation, and model training and validation, with a concordance coefficient of 0.86 and 0.57, respectively. A classified Soil Drainage map produced from the ‘index’ showed good agreement, with a linearly weighted kappa coefficient of 0.72 for training, and 0.37 for validation. The index mapping was incorporated into the overall land suitability model and proved an important consideration for the suitability of most enterprises.
Brendan P Malone - One of the best experts on this subject based on the ideXlab platform.
-
description and spatial inference of Soil Drainage using matrix Soil colours in the lower hunter valley new south wales australia
PeerJ, 2018Co-Authors: Brendan P Malone, Alex B Mcbratney, Budiman MinasnyAbstract:: Soil colour is often used as a general purpose indicator of internal Soil Drainage. In this study we developed a necessarily simple model of Soil Drainage which combines the tacit knowledge of the Soil surveyor with observed matrix Soil colour descriptions. From built up knowledge of the Soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining Soils generally follows the colour sequence of red → brown → yellow → grey → black Soil matrix colours. For each Soil profile, Soil Drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference Soil colours of the Soil Drainage colour sequence. The estimation of Drainage index at each profile incorporates the whole-profile descriptions of Soil colour where necessary, and is weighted such that observation of Soil colour at depth and/or dominantly observed horizons are given more preference than observations near the Soil surface. The Soil Drainage index, by definition disregards surficial Soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of Soil Drainage we digitally mapped the index across our study area. Spatial inference of the Drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson's correlation coefficients indicated the variables most strongly correlated with Soil Drainage were topographic wetness index (-0.34), mid-slope position (-0.29), multi-resolution valley bottom flatness index (-0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of Soil Drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 Soil Drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of Soil colour description, these results are acceptable. Furthermore, the spatial distribution of Soil Drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique Soil colour and Soil Drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.
-
Description and spatial inference of Soil Drainage using matrix Soil colours in the Lower Hunter Valley, New South Wales, Australia
PeerJ Inc., 2018Co-Authors: Brendan P Malone, Alex B Mcbratney, Budiman MinasnyAbstract:Soil colour is often used as a general purpose indicator of internal Soil Drainage. In this study we developed a necessarily simple model of Soil Drainage which combines the tacit knowledge of the Soil surveyor with observed matrix Soil colour descriptions. From built up knowledge of the Soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining Soils generally follows the colour sequence of red → brown → yellow → grey → black Soil matrix colours. For each Soil profile, Soil Drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference Soil colours of the Soil Drainage colour sequence. The estimation of Drainage index at each profile incorporates the whole-profile descriptions of Soil colour where necessary, and is weighted such that observation of Soil colour at depth and/or dominantly observed horizons are given more preference than observations near the Soil surface. The Soil Drainage index, by definition disregards surficial Soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of Soil Drainage we digitally mapped the index across our study area. Spatial inference of the Drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with Soil Drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of Soil Drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 Soil Drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of Soil colour description, these results are acceptable. Furthermore, the spatial distribution of Soil Drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique Soil colour and Soil Drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged
-
Digital mapping of a Soil Drainage index for irrigated enterprise suitability in Tasmania, Australia
Soil Research, 2014Co-Authors: D. B. Kidd, Brendan P Malone, Budiman Minasny, Alexander B. Mcbratney, M. A. WebbAbstract:An operational Digital Soil Assessment was developed to inform land suitability modelling in newly commissioned irrigation schemes in Tasmania, Australia. The Land Suitability model uses various Soil parameters, along with other climate and terrain surfaces, to identify suitable areas for various agricultural enterprises for a combined 70 000-ha pilot project area in the Meander and Midlands Regions of Tasmania. An integral consideration for irrigable suitability is Soil Drainage. Quantitative measurement and mapping can be resource-intensive in time and associated costs, whereas more ‘traditional’ mapping approaches can be generalised, lacking the detail required for statistically validated products. The project was not sufficiently resourced to undertake replicated field-Drainage measurements and relied on expert field Drainage estimates at ~930 sites (260 of these for independent validation) to spatially predict Soil Drainage for both areas using various terrain-based and remotely sensed covariates, using three approaches: (a) decision tree spatial modelling of discrete Drainage classes; (b) regression-tree spatial modelling of a continuous Drainage index; (c) regression kriging (random-forests with residual-kriging) spatial modelling of a continuous Drainage index. Method b was chosen as the best approach in terms of interpretation, and model training and validation, with a concordance coefficient of 0.86 and 0.57, respectively. A classified Soil Drainage map produced from the ‘index’ showed good agreement, with a linearly weighted kappa coefficient of 0.72 for training, and 0.37 for validation. The index mapping was incorporated into the overall land suitability model and proved an important consideration for the suitability of most enterprises.
Mogens Humlekrog Greve - One of the best experts on this subject based on the ideXlab platform.
-
Artificial neural networks and decision tree classification for predicting Soil Drainage classes in Denmark
Geoderma, 2019Co-Authors: Amélie Beucher, Anders Bjørn Møller, Mogens Humlekrog GreveAbstract:Abstract Soil Drainage constitutes a substantial factor affecting plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Consequently, Soil Drainage maps represent crucial tools for crop, forest and environmental management purposes. As extensive field surveys are time- and resource-consuming, alternative spatial modelling techniques have been previously applied for predicting Soil Drainage classes. The present study assessed the use of Artificial Neural Networks (ANN) for mapping Soil Drainage classes in Denmark and compared it to a Decision Tree Classification (DTC) technique. 1702 Soil observations and 31 environmental variables, including Soil and terrain parameters, and spectral indices derived from satellite images, were utilized as input data. Based on a 33% holdback validation dataset, the best performing ANN and DTC models yielded overall accuracy values of 54 and 52%, respectively. DTC models benefited from the use of all variables, but ANN models performed better after variable selection. Notably, ANN and DTC model performances were comparable although differential costs for misclassification were only implemented for DTC modelling. Nevertheless, both methods produced predictive Drainage maps in accordance with one another and demonstrated promising classification abilities over a large study area (c. 43,000 km2).
-
Prediction of Soil Drainage classes in Denmark by means of decision tree classification
Geoderma, 2019Co-Authors: Anders Bjørn Møller, Amélie Beucher, Bo V. Iversen, Mogens Humlekrog GreveAbstract:Abstract Soil Drainage conditions are highly important to farmers and the environment. To map Drainage classes efficiently, several analytical approaches, such as decision tree classification, can be used. Decision tree classification can be improved by combining the predictions of several trees with boosting and bagging techniques. This study tested the relative performance of boosting and bagging for the prediction of Drainage classes. Furthermore, as Drainage classes form an ordered series rather than unrelated classes, differential costs for misclassification were tested in combination with each technique. Decision tree models were trained from 1135 observations of Soil Drainage classes and validated using leave-one-out cross validation and a hold-out validation sample with 567 observations. The best model was achieved using bagging combined with differential costs for misclassification (overall accuracy = 52.0%). On the other hand, differential costs for misclassification reduced the overall accuracy of boosted decision trees from 50.8% to 49.2%. The best models obtained with boosting and bagging were used to produce maps of Drainage classes on a national extent. The maps predicted the same Drainage class in 81% of the study area. Finally, with boosting as well as bagging, the models had a high usage of the predictor variables wetlands, slope to channel network, clay content, land use and geology.
José Alexandre Melo Demattê - One of the best experts on this subject based on the ideXlab platform.
-
digital mapping of Soil Drainage using remote sensing dem and Soil color in a semiarid region of central iran
Geoderma Regional, 2020Co-Authors: Najmeh Asgari, José Lucas Safanelli, A. Jafari, Shamsollah Ayoubi, José Alexandre Melo Demattê, Ariane Francine Desiderio Da SilveiraAbstract:Abstract In this study, random forest (RF) and support vector machine (SVM) models were developed to evaluate different input variables for predicting and mapping Soil Drainage classes in the a part of Charmahal & Bakhtiari Province, central Iran. Input variables included digital elevation model (DEM) derived topographic attributes, remote sensing-derived vegetation indices and diffuse reflectance spectroscopy-derived Soil color qualifiers (chroma and value). Three Soil Drainage classes, comprising poorly drained (PD), moderately well drained (MWD) and well drained (WD) were identified. Totally, 102 profiles were described and Soil samples were collected from various genetic horizons. Results showed that the best classification results were acquired for two extreme Drainage classes (WD and PD) with 100% user accuracy and the greatest misclassification for MWD. Chroma following NDVI and SAVI were the most efficient predictors of Soil Drainage. The best performance of models was acquired when the topographic attributes, NDVI, SAVI, chroma and value were included as input variables for predicting Soil Drainage classes. The prediction overall accuracy and the Kappa coefficient of Drainage classification were 0.83 and 0.73 for RF and 0.86 and 0.74 for SVM, respectively. In overall, results indicated that color qualifiers combined with topographic attributes and vegetation indices can be employed to successfully predict Soil Drainage classes cost-effectively with acceptable overall accuracy.
-
Soil Drainage assessment by magnetic susceptibility measures in western Iran
Geoderma Regional, 2018Co-Authors: Najmeh Asgari, Shamsollah Ayoubi, José Alexandre Melo DemattêAbstract:Abstract The objective of the present study was to evaluate the efficiency of Soil magnetic parameters for assessment of Soil Drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four Soil Drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly Drainage (PD) were selected. A total of 89 Soil pedons were described and Soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all Soil samples were also measured. The results revealed that among the four Drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected Drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of Soil Drainage classes in the present case.
Shamsollah Ayoubi - One of the best experts on this subject based on the ideXlab platform.
-
digital mapping of Soil Drainage using remote sensing dem and Soil color in a semiarid region of central iran
Geoderma Regional, 2020Co-Authors: Najmeh Asgari, José Lucas Safanelli, A. Jafari, Shamsollah Ayoubi, José Alexandre Melo Demattê, Ariane Francine Desiderio Da SilveiraAbstract:Abstract In this study, random forest (RF) and support vector machine (SVM) models were developed to evaluate different input variables for predicting and mapping Soil Drainage classes in the a part of Charmahal & Bakhtiari Province, central Iran. Input variables included digital elevation model (DEM) derived topographic attributes, remote sensing-derived vegetation indices and diffuse reflectance spectroscopy-derived Soil color qualifiers (chroma and value). Three Soil Drainage classes, comprising poorly drained (PD), moderately well drained (MWD) and well drained (WD) were identified. Totally, 102 profiles were described and Soil samples were collected from various genetic horizons. Results showed that the best classification results were acquired for two extreme Drainage classes (WD and PD) with 100% user accuracy and the greatest misclassification for MWD. Chroma following NDVI and SAVI were the most efficient predictors of Soil Drainage. The best performance of models was acquired when the topographic attributes, NDVI, SAVI, chroma and value were included as input variables for predicting Soil Drainage classes. The prediction overall accuracy and the Kappa coefficient of Drainage classification were 0.83 and 0.73 for RF and 0.86 and 0.74 for SVM, respectively. In overall, results indicated that color qualifiers combined with topographic attributes and vegetation indices can be employed to successfully predict Soil Drainage classes cost-effectively with acceptable overall accuracy.
-
Using magnetic susceptibility measurements to differentiate Soil Drainage classes in central Iran
Studia Geophysica Et Geodaetica, 2019Co-Authors: Majid Gholamzadeh, Shamsollah Ayoubi, Fatemeh Sheikhi ShahrivarAbstract:We examine the potential of magnetic susceptibility measurements to discriminate different Soil Drainage classes in the Gandoman region, central Iran. Four Soil Drainage classes, comprising poorly drained (PD), somewhat poorly drained (SPD), moderately well drained (MWD) and well drained (WD), were identified, and a total number of 48 Soil profiles were excavated and studied. The Soil samples were collected from all studied profiles from the genetic horizons individually. Magnetic susceptibility was measured at both low (0.46 kHz) and high (4.6 kHz) frequencies. The crystallized and amorphous iron forms were also measured using citrate-bicarbonate-dithionite solution and oxalate-ammonium extracts, respectively. The highest magnetic susceptibility was observed in WD Soils, whereas the lowest susceptibility was observed in PD Soils. The results of the predictor models developed by discriminate analysis showed that the use of magnetic susceptibility and iron forms could correctly predict about 90.9, 78.6, 85.7 and 88.9% of all profiles in WD, MWD, SPD and PD classes, respectively. Overall, the results indicate that magnetic susceptibility could be applied as a marker for the discrimination of Drainage classes in the study area. Magnetic susceptibility is thus a quickly accessible and low-cost indicator for Soil Drainage classes for landownerships and subsequent analyses.
-
Soil Drainage assessment by magnetic susceptibility measures in western Iran
Geoderma Regional, 2018Co-Authors: Najmeh Asgari, Shamsollah Ayoubi, José Alexandre Melo DemattêAbstract:Abstract The objective of the present study was to evaluate the efficiency of Soil magnetic parameters for assessment of Soil Drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four Soil Drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly Drainage (PD) were selected. A total of 89 Soil pedons were described and Soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all Soil samples were also measured. The results revealed that among the four Drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected Drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of Soil Drainage classes in the present case.