Geomorphometric relief classification with the k-median method in the Silesian Upland, southern Poland

Szypuła, Bartłomiej; Wieczorek, Małgorzata

doi:10.1007/s11707-019-0765-9

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Other approaches to classification of landforms, like the topographic position index [52], and k-median clustering [53], also had parameters that could be adjusted for the proposed correspondence of scales. Therefore, these techniques can be included and tested with the MGG framework in future research.…”

Section: Generalized Multiscale Geomorphometricsmentioning

confidence: 99%

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Araújo¹,

Oliveira²,

Beldini³

2021

JGIS

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Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geomorphologic scales. In this sense, the present study tested the hypothesis whether multiscale geomorphometric generalized covariables can improve pedometric modeling. To achieve this goal, this case study applied the Random Forest algorithm to a multiscale geomorphometric database to predict soil surface attributes. The study area is in phanerozoic sedimentary basins, in the Alter do Chão geological formation, Eastern Amazon, Brazil. The multiscale geomorphometric generalization was applied at general and specific geomorphometric covariables, producing groups for each scale combination. The modeling was run using Random Forest for A-horizon thickness, pH, silt and sand content. For model evaluation, visual analysis of digital maps, metrics of forest structures and effect of variables on prediction were used. For evaluation of soil textural classifications, the confusion matrix with a Kappa index, and the user's and producer's accuracies were employed. The geomorphometry generalization tends to smooth curvatures and produces identifiable geomorphic representations at sub-watershed and watershed levels. The forest structures and effect of variables on prediction are in agreement with pedological knowledge. The multiscale geomorphometric generalized covariables improved accuracy metrics of soil surface texture classification, with the Kappa Index going from 43% to 62%. Therefore, it can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Future development of the multiscale geomorphometric generalization framework could include generalization methods concerning preservation of features,

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Section: Generalized Multiscale Geomorphometricsmentioning

confidence: 99%

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Araújo¹,

Oliveira²,

Beldini³

2021

JGIS

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“…The first study compares unsupervised automatic classification with the traditional mapping for the Sudetes 25 . The second study also concerns unsupervised classification for the area of the Silesian Upland 26 . Another study on supervised classification was conducted by Janowski et al 27 , in which the authors compared machine learning algorithms for classifying glacial landforms in the Lubawa Upland and Gardno–Leba Plain areas using ground truth dataset.…”

Section: Introductionmentioning

confidence: 99%

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

Dyba

2024

Sci Rep

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In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model’s outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process.

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“…The weakest point of clustering is the dependency of results on numerous algorithms, free parameters, and variable selection, which makes unsupervised methods unsuitable for creating target cartographic works (Minar and Evans, 2008). However, recent studies proved the usefulness of clustering in optimizing mapping procedures (Wieczorek and Migoń, 2014;Szypuła and Wieczorek, 2020) and spatial analysis of complex geomorphological processes (Szymanowski et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Toward geomorphometry of plains - Country-level unsupervised classification of low-relief areas (Poland)

Dyba

Jasiewicz²

2022

Geomorphology

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Geomorphometric relief classification with the k-median method in the Silesian Upland, southern Poland

Cited by 5 publications

References 34 publications

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

Toward geomorphometry of plains - Country-level unsupervised classification of low-relief areas (Poland)

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