MARC details
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04671nam a22002417a 4500 |
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ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20221207160413.0 |
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221207b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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MSU |
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MSU |
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rda |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Sarkar, Shraban |
| Relator term |
athour |
| 245 ## - TITLE STATEMENT |
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| Title |
Altmetric Original Articles Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain |
| Statement of responsibility, etc. |
created Shraban Sarkar , Archana K. Roy & Tapas R. Martha |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
India: |
| Name of producer, publisher, distributor, manufacturer |
Taylor and Fancis, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2013. |
| 336 ## - CONTENT TYPE |
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rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
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rdamedia |
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unmediated |
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n |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
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volume |
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nc |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
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Volume , number , |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.<br/><br/>Keywords:<br/><br/> Darjeeling Himalayasdigital elevation modelregression krigingsoil depth<br/><br/>Previous article<br/>View issue table of contents<br/>Next article<br/>Acknowledgements<br/><br/>The first author is thankful to University Grants Commission (UGC), New Delhi, India for providing the fellowship to carry out the research work. He is also thankful to Dr. Edwin and his family for their support during field work.<br/><br/> More Share Options<br/><br/> <br/>Related research<br/><br/> People also read<br/> Recommended articles<br/> Cited by<br/> 20<br/><br/>Soil-landscape modelling and spatial prediction of soil attributes<br/>P. E. GESSLER et al.<br/>International journal of geographical information systems<br/>Published online: 5 Feb 2007<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/>Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model. <br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Darjeeling Himalayas |
| Form subdivision |
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| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
digital evaluationmodel |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
gression kriging |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
DOI:10.1080/13658816.2013.814780Corpus ID: 493068 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Journal Article |