The mapping of soil taxonomic units via fuzzy clustering – A case study from the Outer Carpathians, Czechia

Horáček, Matěj; Samec, Pavel; Minár, Jozef

doi:10.1016/j.geoderma.2018.04.012

Cited by 16 publications

(5 citation statements)

References 36 publications

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“…However, even if fine-textured soil layers lose their water supply and long-term saturation due to groundwater dropping, they retain the ability to accumulate water from precipitation, which may result in the development of stagnic properties under a sufficiently moist climate. Although reasonable, the stagnic properties in alluvial soils have not been commonly reported from Central Europe [45][46][47][48], which may indicate that many researchers do not expect this substantial change in soil moisture status and soil morphology. The change in a dominant moisture supply has significant consequences for land management, both in agriculture and forestry, including a noticeable rebuilding of the forest species composition in the managed forests [4,5,28].…”

Section: Discussionmentioning

confidence: 80%

Morphology and Physicochemical Properties of Alluvial Soils in Riparian Forests after River Regulation

Kawałko

Jezierski

Kabała

2021

Forests

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The elimination of flooding and lowering of the groundwater table after large-scale river regulation allow deep penetration of soils by plant roots, soil fauna, and microorganisms, thus creating favorable conditions for advanced pedogenesis. Although the changes of the morphology and properties of agriculturally used drained alluvial soils in Central Europe have been well characterized, studies in riparian forests remain insufficient. An analysis of 21 profiles of forest soils located on the Holocene river terrace (a floodplain before river regulation and embankment) in SW Poland confirmed a noticeable pedogenic transformation of soil morphology and properties resulting from river regulation. Gleyic properties were in most profiles replaced with stagnic properties, testifying to a transition from dominant groundwater supply to precipitation-water supply. The development of a diagnostic mollic and cambic horizons, correlated with the shift in soil classification from Fluvisols to Phaeozems, and in the majority, to Cambisols, demonstrated a substantial change in habitat conditions. The transformation of alluvial soils may result in an inevitable modification of forest management in the river valley, including quantitative alteration in species composition of primarily riparian forests.

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Section: Discussionmentioning

confidence: 80%

Morphology and Physicochemical Properties of Alluvial Soils in Riparian Forests after River Regulation

Kawałko

Jezierski

Kabała

2021

Forests

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“…However, Vincent, Lemercier, Berthier, and Walter () achieved between 41 and 72% for a regional study in France, while Pásztor, Laborczi, Bakacsi, Szabó, and Illés () achieved classification accuracies between 0.17 and 0.36 across different landscape types in Hungary using a 41‐class system. Horáček, Samec, and Minár () showed an absolute match of 26% for 22 classes in Czechia, although they also showed that a lot of the class designations per “partially accurate,” that is classification to similar classes as the target. An evaluation of the confusion matrix of the 30‐class model showed that in each case, the most common misclassifications occurred with classes that were more similar to the target class.…”

Section: Discussionmentioning

confidence: 99%

Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling—Part 1: Mapping of soil classes

Coull

2019

Soil Use and Management

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A digital mapping approach was applied to soils in Scotland, producing maps at 100-m resolution and different levels of classification. This used neural networks to predict fuzzy soil class weightings based upon site descriptors from existing soil survey data. The intention of this work was to produce a set of soil maps for Scotland, which provide greater spatial resolution mapping than currently available, and provide fuzzy data to be used in mapping of ecosystem services using a novel approach explained in a second paper. When selecting the class with the highest weighting for test data points, Producer's Accuracy of 70.7% was achieved in mapping the broadest classification (five classes), while Producer's Accuracy levels of 59.9% and 43.7%were achieved for 11 and 30 classes, respectively. Evaluation of confusion matrices generated from the neural network model tests showed that, particularly for the classification systems with 11 and 30 classes, misclassification errors were more common between similar soil classes than between classes that were very different from one another. This implies that straightforward estimation of classification accuracy that measures "correct" and "incorrect" gives misleading results, and that the fuzzy classification maps are more useful than the crisp classification accuracy values.These fuzzy classification maps are therefore potentially useful for evaluating ecosystem services relating to soil type. We conclude that the mapping approach used here provides a classification accuracy comparable to other approaches and that the outputs can be used for mapping soil properties, processes, functions and ecosystem services. K E Y W O R D Sdigital soil mapping, land management, remote sensing, soil classification, spatial resolution 206 |

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“…This is true if we use only cartographic data as input. In other cases, as Horáček M. et al (2018) show, other approaches may be successful.…”

Section: Modelling Based On Cartographic Datamentioning

confidence: 97%

“…A new method for the digital mapping of taxonomic soil units via fuzzy taxonomy and fuzzy clustering was proposed by Horáček et al (2018). Fuzzified taxonomic soil information from 106 soil pits with 75 geomorphometric parameters (potential environmental covariates of soil units) derived from a 10 m LIDAR DEM was used for the input (training) data for territory of 104.5 km 2 .…”

Section: Research Backgroundmentioning

confidence: 99%

“…In addition, we believe that there is still some potential for increasing the total value of κ, in particular through a more thorough selection of predictors of the model. For example, fuzzy c-means clustering analyses for selecting predictors as shown Horáček et al (2018) and the expansion of their number by incorporating remote sensing data, anthropogenic deposits map and more.…”

Section: Tab 3 Correlation Between Nature and Map Data For Test Polygonmentioning

confidence: 99%

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Methods of verification of soils prediction maps: a case study from Chernivtsi region, Ukraine

Cherlinka¹,

Dmytruk²,

Barabas³

2019

Geographia Cassoviensis

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Knowing the spatial distribution of individual soil taxonomic units is a key factor in managing efficient land use not only for agriculture but also for forestry. The use of a comprehensive soil surveys held in past decades and based on fieldwork created the basis for the initial spatial representation of the soil fund structure. However, the spatial distribution of the soil cover was the result of fieldwork and the experience of the person who drew this map. Often this led to some errors in determining the types of soils and their boundaries. To date, there is a growing need for precise methods of land taxation, based on correct information on soil cover. In countries with a large area, such as Ukraine, field surveys still do not cover the whole territory, often the density of the allocation of soil pits was too low, which in some cases led to an incorrect demarcation of soil boundaries. Since such a problem is very urgent for Ukraine, the search and identification of probable problem soil maps by constructing their predicted versions, their comprehensive analysis and cross-validation is an important task. The conducted investigations revealed that morphometric parameters of the relief and their derivatives obtaining from the analyses of DEM are a reliable basis for the predictive modelling of the spatial distribution of soil cover with sufficiently high accuracy, and the methodology based on 11 types of prognostic algorithms would have a significant prospect in solving scientific and production problems. Very important in this process is the selection of predictors derived from the DEM, as well as the structure and distribution of the training dataset, based on which the model will be built later. Afterwards the results need to be validated, in our case, on the basis of the cross-validation of the models and by comparing the results with field survey. The article presents the results of 11 simulations, evaluates the quality of predictive algorithms and the models obtained. Therefore, several possible ways to check the cartographic and simulation results of the spatial distribution of soil taxonomic units were described, as well as their comparison with those actually existing in nature. The most reliable method of the 11 presented is a direct study of the soil in the field and comparing them with the soil map. It is recommended to use it in case of suspicion of poorly executed maps, although financially it is very expensive. More preferred is a set of modelling methods that is based on the data already collected. With reliable sources, they provide an opportunity to predict the soil in places where the survey was not conducted at all. Verification of the quality of the tested models was carried out on a fragment of the Ukrainian region within the boundaries of the Chernivtsi region, confined to the Prut-Dniester and Prut-Siret interfluves.

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The mapping of soil taxonomic units via fuzzy clustering – A case study from the Outer Carpathians, Czechia

Cited by 16 publications

References 36 publications

Morphology and Physicochemical Properties of Alluvial Soils in Riparian Forests after River Regulation

Morphology and Physicochemical Properties of Alluvial Soils in Riparian Forests after River Regulation

Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling—Part 1: Mapping of soil classes

Methods of verification of soils prediction maps: a case study from Chernivtsi region, Ukraine

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