- Open Access
Use of remote sensing and geographical information system (GIS) for salinity assessment of Vaal-Harts irrigation scheme, South Africa
© Ochieng et al.; licensee Springer. 2013
- Received: 8 November 2012
- Accepted: 15 January 2013
- Published: 25 February 2013
Soil salinity is a critical environmental problem in many countries around the world especially the arid and semi-arid countries like South Africa. The problem has great impact on soil fertility which in turns has a great effect on soil productivity. This paper addresses the use of remote sensing and GIS in the assessment of salinity using Landsat enhanced thematic mapper plus (ETM+) data of the Vaal-Harts irrigation scheme acquired with other field data sets and a topographical map to show the spectral classes and salt-affected areas for the years under assessment (1991 to 2005).
The results of the study indicated that salinity problem exists and may get worse. The supervised classification maps show that most of the salinity problems are located along the entire scheme. The Normalized Difference Vegetation Index (NDVI) tends to be higher along the irrigation canals. A plot of NDVI values and temperature trend give a correlation of 67% this is an indication that temperature is a major factor in the build up of salinity in the study area. The low salinity class increased by 4, 8618 km2, while medium and high salinity classes decreased by 4,296.4 km2 and 485.4 km2, showing an increase in the salinity trend over the years.
Considering the trend of salinity development in VHS, there is an urgent need for management program to be established in order to control the spread of the menace and therefore reclaim the damaged land in order to make the scheme more viable.
- Remote sensing
- Vaal Harts
The problem of salinity has great impact on soil fertility which in turns has a great effect on soil productivity. According to a survey by the Department of Agriculture in 1990, it was discovered that out of 128, 000 ha of cultivated land, 54, 000 ha is seriously alkaline, waterlogged and moderately saline (Du 1991). An estimated 18% of the area under regular irrigation appears to be affected by water logging and salinization in the Vaal Harts irrigation scheme. In the 1960s, a number of soil profiles from all over the Scheme, contained more salts in the subsurface than measured during the initial soil survey, indicating a disturbing tendency, although not alarming at that stage. Within 35 years of the scheme's existence, the fine sandy soils of this scheme were severely salinised. Reclamation of some 30 000 ha saline or saline-sodic soils (depth 0.3 m) was reported at VHS. Salt-affected soils at VHS resulted in 1.4 - 2.1 million South African Rand gross income loss for the irrigation scheme farmers in 1975 (Du 1991). The installation of 218 drainage systems totaling 500 km of subsurface lateral drains at a cost of 2 million Rand were undertaken between 1975 and 1977. In the 1980s, a further 2 million Rand was invested to install sub-surface drains on farms and to link these drains to the partially developed system of open storm water drains, in an attempt to lower the water table and to leach salts. Since 2009, the service of a consultant was engaged to assess the scheme at an additional cost of about 5 million Rand (Ojo et al. 2009, 2011). Salinity problems are often measured by means of soil surveys, questionnaires and laboratory analyses. These traditional data collection methods analyses are neither enough for the assessment of this important environmental issue. Satellite image data are used to overcome most of these limitations with a need to better find the calibration between the data and real field situations. The spread of modeling techniques using distributed parameters has largely encouraged the use of input data from remote sensing with the support of GIS for manipulating large data sets. In the interim report submitted to the Institute for Applied Systems analysis, Brogaard and Prieler (1998) described how Landsat MSS can be used for the identification of broad land cover changes of the Western part of Horqin steppe, Inner Mongolia Autonomous Region. Van Trinh et al (2004) used Landsat images for studying land use dynamics and soil degradation in the Tamduong district of Vietnam. Eranani and Gabriels (2006) used Landsat data from 1976 to 2002 to detect changes in land cover in the Yazd-Ardakan basin, Iran. Latifovic et al (2005) analysed the land cover change of the Oil Sands Mining Development in Athabasca, Canada using information extraction method applied to two Landsat scenes. The objective of this study was to assess the salinity problem in Vaal Harts irrigation scheme using multi-temporal satellite data.
Data collection, processing and analysis
Landsat time-series used in the study
To delineate and ground truth the data, a Garmin hand-held GPS Receiver of 2 m accuracy was used to obtain geographical coordinate of water logged and salt affected area. The geographical coordinates were transformed to the Hartebeesthoek94 Datum and converted to ground coordinates. Supervised classifications were performed after extracting spectral signatures and training data sets were created.
The supervised classification made use of 15 training sites per habitat identified on the image. As a rule there should be an adequate sample of pixels for each cover type for statistical characterization. A general rule of thumb is that the number of pixels in each training set (i.e., all the training sites for a single cover class) should not be less than ten times the number of bands. Hence, the use of three bands in the classification processes with 45 training sites in each cover type, each has fifteen classes.
The band 3 (red band) provides the best separation for vegetation, while band 4 (near infrared band) provide the best separation for the area with salt, while water is distinct in all the bands. Band 4 provided a better separation of the area occupied by salt than band 5 (middle infrared band). Bands 4 performed better when it comes to the spectral separation of areas dominated by salt but band 3 was extremely excellent in the vegetation discrimination. The spectral signatures also aided in the classification processes of salinity, community/built-up, vegetation and water swamp areas.
Soil salinity is a critical environmental problem which has great impact on soil fertility and overall soil productivity. Remote sensing was proved useful in detecting salinity trend using Landsat enhanced thematic mapper plus (ETM +) data along with other field data and topographical maps to show the spectral classes and areas of salt-affected for the years under assessment. Detection of salinization, assessment of the degree of severity and the extent particularly in its early stage is vital as it was also shown in the results indicating a gentle increase in the rate. The results of the study indicated that a serious salinity problem exists on VHS and this may get worse unless more effort is geared toward effective management of the menace. This is in agreement with findings of Zuluaga, 1990; Vincent et al.,1996; Dehaan and Taylor, 2002 that field derived spectra of salinised soils and vegetation indexes are good indicators of irrigation induced soil salinisation for identification of saline soil regions.
The Normalized Difference Vegetation Index (NDVI) and temperature plot gave a correlation of 67% which is a good indicator of salinity trends. This can be improved on by combining detailed field measurements using a handy hyper spectrometer to increase the accuracy of detection. This is an indication that temperature is an important environmental factor in the build up of salinity in areas known with high temperature (semi-arid climatic zone in which the study area fell into). Conclusively, there is an urgent need for the establishment of management program to control the spread of this menace, thereby reclaiming the damaged land in order to make the scheme more economically viable.
The authors are thankful to the Tshwane University of Technology, Pretoria, South Africa for providing financial support which made this study possible. The support of the Vaal Harts irrigation water users association, Agricultural Research Council (ARC) and Department of water and environmental affairs is acknowledged for providing some data.
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