Use of Landsat series data to analyse the spatial and temporal variations of land degradation in a dispersive soil environment: A case of King Sabata Dalindyebo local municipality in the Eastern Cape Province, South Africa

Citation data:

Physics and Chemistry of the Earth, Parts A/B/C, ISSN: 1474-7065, Vol: 100, Page: 112-120

Publication Year:
2017
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DOI:
10.1016/j.pce.2017.01.023
Author(s):
Timothy Dube, Onisimo Mutanga, Mbulisi Sibanda, Khoboso Seutloali, Cletah Shoko
Publisher(s):
Elsevier BV
Tags:
Earth and Planetary Sciences
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article description
Land degradation as a result of inappropriate land use practices, such as overgrazing and cultivation on steep slopes, etc. is one of the major global environmental challenges. Specifically, land degradation threatens the productivity and sustainability of the natural environment, agriculture, and most importantly rural economies in most developing countries, particularly the sub-Saharan region. The main aim of this study was therefore, to assess the potential and strength of using the free or readily available Landsat series data in mapping degraded land areas at the King Sabata Dalindyebo local municipality in the Eastern Cape, South Africa (1984–2010). Data analysis was done using a robust non-parametric classification ensemble; Discriminant Analysis (DA). The results show that degraded areas vary over the years. For example, the results show that the year 1994 and 2004 incurred high degradation levels, when compared to the year 1984 and 2010. Moreover, the observed degradation significantly (α = 0.05) varies with soil type. The chromic acrisols have the highest levels of erosion (approx. 80% in 1984), when compared to humic-umbric acrisols (less than 10% for the entire period under study). It can also be observed that considerable part of degradation occurred in the northern part of the municipal district. Overall, the findings of this research underlines the importance and efficacy of multispectral Landsat series data-set in mapping and monitoring levels of land degradation in data-scarce catchments.

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