Digital soil mapping from conventional field soil observations
We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Rišňovce, Slovakia. We examine whether the soil profile descriptions collected me...
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2013-03-01
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| Series: | Soil and Water Research |
| Subjects: | |
| Online Access: | https://swr.agriculturejournals.cz/artkey/swr-201301-0002_digital-soil-mapping-from-conventional-field-soil-observations.php |
| _version_ | 1797897857500446720 |
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| author | Juraj BALKOVIČ Zuzana RAMPAŠEKOVÁ Vladimír HUTÁR Jaroslava SOBOCKÁ Rastislav SKALSKÝ |
| author_facet | Juraj BALKOVIČ Zuzana RAMPAŠEKOVÁ Vladimír HUTÁR Jaroslava SOBOCKÁ Rastislav SKALSKÝ |
| author_sort | Juraj BALKOVIČ |
| collection | DOAJ |
| description | We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Rišňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A-D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A-D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A "leave-one-out" cross-validation was calculated for the output maps yielding R2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations. |
| first_indexed | 2024-04-10T08:03:54Z |
| format | Article |
| id | doaj.art-413c74593bbb4fd996cd501067cb0a4d |
| institution | Directory Open Access Journal |
| issn | 1801-5395 1805-9384 |
| language | English |
| last_indexed | 2024-04-10T08:03:54Z |
| publishDate | 2013-03-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Soil and Water Research |
| spelling | doaj.art-413c74593bbb4fd996cd501067cb0a4d2023-02-23T03:48:22ZengCzech Academy of Agricultural SciencesSoil and Water Research1801-53951805-93842013-03-0181132510.17221/43/2012-SWRswr-201301-0002Digital soil mapping from conventional field soil observationsJuraj BALKOVIČ0Zuzana RAMPAŠEKOVÁ1Vladimír HUTÁR2Jaroslava SOBOCKÁ3Rastislav SKALSKÝ4International Institute for Applied Systems Analysis, Laxenburg, AustriaDepartment of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Slovak RepublicSoil Science and Conservation Research Institute, Bratislava, Slovak RepublicSoil Science and Conservation Research Institute, Bratislava, Slovak RepublicInternational Institute for Applied Systems Analysis, Laxenburg, AustriaWe tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Rišňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A-D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A-D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A "leave-one-out" cross-validation was calculated for the output maps yielding R2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations.https://swr.agriculturejournals.cz/artkey/swr-201301-0002_digital-soil-mapping-from-conventional-field-soil-observations.phpfield soil descriptionfuzzy k-meanspedometricsregression-krigingterrain |
| spellingShingle | Juraj BALKOVIČ Zuzana RAMPAŠEKOVÁ Vladimír HUTÁR Jaroslava SOBOCKÁ Rastislav SKALSKÝ Digital soil mapping from conventional field soil observations Soil and Water Research field soil description fuzzy k-means pedometrics regression-kriging terrain |
| title | Digital soil mapping from conventional field soil observations |
| title_full | Digital soil mapping from conventional field soil observations |
| title_fullStr | Digital soil mapping from conventional field soil observations |
| title_full_unstemmed | Digital soil mapping from conventional field soil observations |
| title_short | Digital soil mapping from conventional field soil observations |
| title_sort | digital soil mapping from conventional field soil observations |
| topic | field soil description fuzzy k-means pedometrics regression-kriging terrain |
| url | https://swr.agriculturejournals.cz/artkey/swr-201301-0002_digital-soil-mapping-from-conventional-field-soil-observations.php |
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