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001-es BibID:BIBFORM105337
035-os BibID:(WoS)000846416200001 (Scopus)85137317190 (cikkazonosító)1858
Első szerző:Hateffard, Fatemeh (digital soil mapping)
Cím:High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics / Fatemeh Hateffard, Kitti Balog, Tibor Tóth, János Mészáros, Mátyás Árvai, Zsófia Adrienn Kovács, Nóra Szűcs-Vásárhelyi, Sándor Koós, Péter László, Tibor József Novák, László Pásztor, Gábor Szatmári
Dátum:2022
ISSN:2073-4395
Megjegyzések:Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
salt-affected soils
digital soil mapping
ensemble modelling
geostatistics; uncertainty assessment
satellite remote sensing
unpiloted aerial vehicle
Megjelenés:Agronomy-Basel. - 12 : 8 (2022), p. 1-19. -
További szerzők:Balog Kitti Tóth Tibor Mészáros János Árvai Mátyás Kovács Zsófia Adrienn Szűcs-Vásárhelyi Nóra Koós Sándor (agrár) László Péter Novák Tibor (1973-) (geográfus) Pásztor László Szatmári Gábor
Pályázati támogatás:K-131820 and K-124290
OTKA
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM091585
035-os BibID:(cikkazonosító)333 (WoS)000621996400001
Első szerző:Phinzi, Kwanele
Cím:Mapping Permanent Gullies in an Agricultural Area Using Satellite Images : Efficacy of Machine Learning Algorithms / Phinzi Kwanele, Holb Imre, Szabó Szilárd
Dátum:2021
ISSN:2073-4395
Megjegyzések:Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
gully erosion
image classification
K-Nearest Neighbor
random forest
minimum distance
maximum likelihood
Megjelenés:Agronomy-Basel. - 11 : 2 (2021), p. 1-16. -
További szerzők:Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TNN 123457 NKFI
Egyéb
Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary projects
Egyéb
K131478
OTKA
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:
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