Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia

Lydia Mumbi Chabala; Augustine Mulolwa; Obed Lungu

doi:doi:10.11648/j.aff.20130204.11

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Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia

Lydia Mumbi Chabala, Augustine Mulolwa, Obed Lungu

Published in Agriculture, Forestry and Fisheries (Volume 2, Issue 4)

Received: 10 July 2013 Published: 20 August 2013

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Abstract

This paper presents results of a landform classification of a section of the Chongwe-Rufunsa area, Zambia. The objective of the study was to separate the landscape into landform classes that indicate or suggest marked differences with respect to soil properties and agricultural suitability. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes. The generated landform map had an overall classification accuracy of 73.51%. The landform map provided a base for benchmark soil sampling for ongoing research on digital soil mapping.

Published in	Agriculture, Forestry and Fisheries (Volume 2, Issue 4)
DOI	10.11648/j.aff.20130204.11
Page(s)	156-160
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Landform, Digital Elevation Model, Terrain Attribute

References

[1]	Barka I., J. Vladovic and F. Malis. 2011. Landform classification and its application in predictive mapping of soil and forest units. GIS Ostrava, 1, 23 – 26. [Accessed 20 May 2013]. Available from: www.GIS.lsb.cz/GIS-ostrava/GIS_ova2011.
[2]	Barker V.R. 2009. Introduction: Regional landform analysis. Avalaible online from Goddard Earth Sciences Data and Information Center. [Accessed 6 June 2013]. Available from: www.disc.sci.gsfc.nasa.gov/geomorphology/
[3]	Burrough, P.A. and McDonnell R.A. 2004. Principles of Geographical Information Systems. Oxford University Press, New York, p. 190.
[4]	Dobos, E., J. Daroussin and L. Montanarella.. 2005. An SRTM-based procedure to delineate SOTER Terrain Units on 1:1 and 1:5 million scales. EUR 21571 EN, 55 pp. Office for Official Publications of the European Communities, Luxembourg.
[5]	Environmental Systems Research Institute (ESRI). 2013. Cell Statistics (Spatial Analyst). Anonymous. [Accessed 25 March 2013]. Available from: http://resources.arcgis.com/en/help/main/10.1/index.html#//009z0000007q000000
[6]	Huting J.R.M., J. A. Dijkshoorn and V.W.P van Engelen. 2008. GIS procedures for mapping SOTERlandform for the LADA partner countries (Argentina, China, Cuba, Senegal and The Gambia, South Africa and Tunisia). ISRIC report 2008/04 and GLADA report 2008/02, ISRIC – World Soil Information and FAO, Wageningen (30 pp with data set). Available from: http://www.isric.org/isric/Webdocs/Docs/ISRIC_Report_2008_04.pdf
[7]	Garrard, P. 1968. The geology of the Chainama Hills area: Explanation of the degree sheet 1528, NE Quarter. Geological Survey of Zambia. Report No. 24.
[8]	Iwahashi J. and R.J.Pike. 2006. Automated classifications of topography from DEMs by unsupervised nested-means algorithm and a three part geometric signature. Geomorphology 86, 409 – 440. [accessed 6 June 2013]. Available from: www.sciencedirect.com.
[9]	MacMillan R. 2011. Automated extraction of land forms from DEM data. Workshop presentation. [Accessed 18 May 2013]. Available from: www.slideshare.net/bob_macmillan/automated-extraction-of-landforms-from-dem-data-13062121
[10]	Saadat H., Bonnell R., Sharifi F., Mehuys M. and Ale-Ebrahim S. 2008. Landform classification from digital elevation model and satellite imagery. Geomorphology 100, 453 -464.[Accessed 20 May 2013]. Available from: www.sciencedirect.com.
[11]	Simpson, J.G. 1967. The geology of the Chinyunyu area: Explanation of the degree sheet 1529, NW Quarter. Geological Survey of Zambia. Report No. 19.
[12]	Thorne, C.R, Zevenbergen L.W, Burt T.P. and Butcher D.P. 1987. Terrain analysis for quantitative description of zero order basins. Ersosion and Sedimentation in the Pacific Rim. Proceedings of the Corvallis Symposium, August, 1987.
[13]	Wilson J.P and J. C. Gallant. 2000. Terrain Analysis: Principles and Applications. John Wiley and Sons, Inc. Chichester, Canada
[14]	Woode P. 1988. Field guide for soil surveyors. Technical guide no. 18. Soil Survey Unit, Department of Agriculture, Zambia.

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APA Style

Lydia Mumbi Chabala, Augustine Mulolwa, Obed Lungu. (2013). Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agriculture, Forestry and Fisheries, 2(4), 156-160. https://doi.org/10.11648/j.aff.20130204.11

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ACS Style

Lydia Mumbi Chabala; Augustine Mulolwa; Obed Lungu. Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agric. For. Fish. 2013, 2(4), 156-160. doi: 10.11648/j.aff.20130204.11

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AMA Style

Lydia Mumbi Chabala, Augustine Mulolwa, Obed Lungu. Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agric For Fish. 2013;2(4):156-160. doi: 10.11648/j.aff.20130204.11

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@article{10.11648/j.aff.20130204.11,
  author = {Lydia Mumbi Chabala and Augustine Mulolwa and Obed Lungu},
  title = {Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {2},
  number = {4},
  pages = {156-160},
  doi = {10.11648/j.aff.20130204.11},
  url = {https://doi.org/10.11648/j.aff.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20130204.11},
  abstract = {This paper presents results of a landform classification of a section of the Chongwe-Rufunsa area, Zambia. The objective of the study was to separate the landscape into landform classes that indicate or suggest marked differences with respect to soil properties and agricultural suitability. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes. The generated landform map had an overall classification accuracy of 73.51%. The landform map provided a base for benchmark soil sampling for ongoing research on digital soil mapping.},
 year = {2013}
}

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TY  - JOUR
T1  - Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia
AU  - Lydia Mumbi Chabala
AU  - Augustine Mulolwa
AU  - Obed Lungu
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.aff.20130204.11
DO  - 10.11648/j.aff.20130204.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 156
EP  - 160
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20130204.11
AB  - This paper presents results of a landform classification of a section of the Chongwe-Rufunsa area, Zambia. The objective of the study was to separate the landscape into landform classes that indicate or suggest marked differences with respect to soil properties and agricultural suitability. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes. The generated landform map had an overall classification accuracy of 73.51%. The landform map provided a base for benchmark soil sampling for ongoing research on digital soil mapping.
VL  - 2
IS  - 4
ER  -

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Author Information

Lydia Mumbi Chabala

Department of Soil Science, University of Zambia, School of Agricultural Sciences, P.O. Box 32379, Lusaka, Zambia
Augustine Mulolwa

Department of Geomatic Engineering, University of Zambia, School of Engineering, National Remote Sensing Center, Lusaka, Zambia
Obed Lungu

Department of Soil Science, University of Zambia, School of Agricultural Sciences, P.O. Box 32379, Lusaka, Zambia

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Plain Text BibTeX RIS

APA Style

Lydia Mumbi Chabala, Augustine Mulolwa, Obed Lungu. (2013). Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agriculture, Forestry and Fisheries, 2(4), 156-160. https://doi.org/10.11648/j.aff.20130204.11

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ACS Style

Lydia Mumbi Chabala; Augustine Mulolwa; Obed Lungu. Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agric. For. Fish. 2013, 2(4), 156-160. doi: 10.11648/j.aff.20130204.11

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AMA Style

Lydia Mumbi Chabala, Augustine Mulolwa, Obed Lungu. Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia. Agric For Fish. 2013;2(4):156-160. doi: 10.11648/j.aff.20130204.11

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@article{10.11648/j.aff.20130204.11,
  author = {Lydia Mumbi Chabala and Augustine Mulolwa and Obed Lungu},
  title = {Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {2},
  number = {4},
  pages = {156-160},
  doi = {10.11648/j.aff.20130204.11},
  url = {https://doi.org/10.11648/j.aff.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20130204.11},
  abstract = {This paper presents results of a landform classification of a section of the Chongwe-Rufunsa area, Zambia. The objective of the study was to separate the landscape into landform classes that indicate or suggest marked differences with respect to soil properties and agricultural suitability. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes. The generated landform map had an overall classification accuracy of 73.51%. The landform map provided a base for benchmark soil sampling for ongoing research on digital soil mapping.},
 year = {2013}
}

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TY  - JOUR
T1  - Landform Classification for Digital Soil Mapping in the Chongwe-Rufunsa Area, Zambia
AU  - Lydia Mumbi Chabala
AU  - Augustine Mulolwa
AU  - Obed Lungu
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.aff.20130204.11
DO  - 10.11648/j.aff.20130204.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 156
EP  - 160
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20130204.11
AB  - This paper presents results of a landform classification of a section of the Chongwe-Rufunsa area, Zambia. The objective of the study was to separate the landscape into landform classes that indicate or suggest marked differences with respect to soil properties and agricultural suitability. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes. The generated landform map had an overall classification accuracy of 73.51%. The landform map provided a base for benchmark soil sampling for ongoing research on digital soil mapping.
VL  - 2
IS  - 4
ER  -

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