Although there is an increasing need for spatial soil information, traditional methods of soil survey are too
cumbersome and expensive to supply in that need. Digital soil mapping (DSM) methods can fulfil that
need. Internationally, DSM is moving from the research to the production phase. As soil-landscape
interaction and availability of data varies between locations, local DSM research is needed to make its
application practical.
This research aims to produce a working DSM protocol which can be used for mapping large areas of
land in southern Africa. The protocol must meet soil surveyors where they are at, being easy enough to
follow, while also allowing for the creation of products needed by industry. To keep the link with industryâs
needs, a case study approach was followed. Four case studies were done in succession, with the
protocol being improved with every case study. The case studies cover an array of challenges faced by
soil surveyors.
In the first case study a baseline protocol was created when two land types near Madadeni were
disaggregated in a series of soil maps. With each map, more information was incorporated when creating
the map. For Map 1 only the land type inventory and terrain analysis were used. A reconnaissance field
visit with the land type surveyor was added for the second map. Field work and a simplified soil
association legend proved to improve the map accuracy for Maps 3 and 4, which were created using 30%
and 60% of the observations points as training data respectively. The accuracy of the maps increased
when more information was utilized. Map 1 reached an accuracy of 35%, while Map 4 achieved a
commendable accuracy of 67%. Principles which emerged was that field work is critical to DSM, more
data input improves the output and that simplifying the map legend improves the accuracy of the map.
An unrealistic demand for a soil survey of 37 000 ha of land in the Tete Province, northern Mozambique,
possibly infested with land mines, in 8 working days by two persons, created an opportunity to apply the
soil-land inference model (SoLIM) as a digital soil mapping tool. Dividing the area into smaller areas
where unique soil distribution rules would apply (homogeneous areas, HAâs) was introduced. A free
survey was conducted along the available roads of the area. The final soil map for 15 000 ha had an
accuracy of 69%. A principle which emerged was that inaccessible areas can be mapped, provided that
they occur within surveyed HAâs.
Near Namarroi, Mozambique, the potential of DSM soil survey methods to rapidly produce land suitability
maps for a large area with acceptable accuracy was evaluated. Conditioned Latin hypercube sampling (cLHS) was introduced to determine field observation positions. SoLIM was used to run an inference with
soil terrain rules derived from conceptual soil distribution patterns. A restriction of the expert knowledge
based approach was found in that only six soil map units (SMUâs) could be determined per HA. The map
achieved an overall accuracy of 80%. Land suitability maps were created based on the soil class map.
In the Kruger National Park a soil map was used to create and extrapolate 2-dimensional conceptual
hydrological response models (CHRMâs) to a 3-dimensional landscape. This is a very good example on
how value could be added to a soil map. An error matrix convincingly identified problem areas in the map
where future work could focus to improve the soil map.
The current data indicates that at least 28 soil observations are necessary to create a soil map to an
acceptable standard. When minimum observation criteria are met, observation density is irrelevant. The
cLHS method to pre-determine observation positions improved the usability of observations. Although
more research is needed to accurately determine the minimum observation criteria, an observation
strategy is suggested.
A 15 step protocol is produced with which it was shown that soil surveyors could produce a variety of
maps in diverse situations. The protocol relies on the expert knowledge of the soil surveyor, combined
with field observations. It has the advantages that fewer observations are necessary, map accuracy
assessment is possible, problem areas are identified and under certain conditions unsurveyed areas can
also be mapped. On the down side, there is a limitation of six SMUâs per HA.
Further research needs to be done to determine the minimum criteria for soil observations, and soil
distribution relationships between soil and remotely sensed covariates.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ufs/oai:etd.uovs.ac.za:etd-08072014-114640 |
| Date | 07 August 2014 |
| Creators | van Zijl, G M |
| Contributors | Prof PAL le Roux |
| Publisher | University of the Free State |
| Source Sets | South African National ETD Portal |
| Language | en-uk |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.uovs.ac.za//theses/available/etd-08072014-114640/restricted/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University Free State or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
Page generated in 0.0169 seconds