Remote sensing-based identification and mapping of salinised irrigated land between Upington and Keimoes along the lower Orange River, South Africa
Salinisation is a major environmental hazard that reduces agricultural yields and degrades arable land. Two main categories of salinisation are: primary and secondary soil salinisation. While primary soil salinisation is caused by natural processes, secondary soil salinisation is caused by human factors. Incorrect irrigation practices are the major contributor to secondary soil salinisation. Because of low costs and less time that is associated with the use of remote sensing techniques, remote sensing data is used in this study to identify and map salinised irrigated land between Upington and Keimoes, Northern Cape Province, in South Africa. The aim of this study is to evaluate the potential of digital aerial imagery in identifying salinised cultivated land. Two methods were used to realize this aim. The first method involved visually identifying salinised areas on NIR, and NDVI images and then digitizing them onscreen. In the second method, digital RGB mosaicked, stacked, and NDVI images were subjected to unsupervised image classification to identify salinised land. Soil samples randomly selected and analyzed for salinity were used to validate the results obtained from the analysis of aerial photographs. Both techniques had difficulties in identifying salinised land because of their inability to differentiate salt induced stress from other forms of stress. Visual image analysis was relatively successful in identifying salinised land than unsupervised image classification. Visual image analysis correctly identified about 55% of salinised land while only about 25% was identified by unsupervised classification. The two techniques predict that an average of about 10% of irrigated land is affected by salinisation in the study area. This study found that although visual analysis was time consuming and cannot differentiate salt induced stress from other forms; it is fairly possible to identify areas of crop stress using digital aerial imagery. Unsupervised classification was not successful in identifying areas of crop stress.