The South African land cover change detection derived from 2013_2014 and 2017_2018 land cover products

Abstract

The appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.

Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa

“…In this study, the widely adopted change detection method was employed in mapping and assessing riparian vegetation response in a time series interval of five years [48,49]. Post-classification assessments and image differencing techniques [50] were used to identify and approximate the extent of land cover changes in the study area between 1990, 1995, 2000, 2005, 2010, 2015, and 2020 respectively. The least-squares linear regression was used to evaluate the long-term vegetation trends [51] and the role of hydro-meteorological variables including annual precipitation and streamflow during the study period.…”

Investigating the effects of drought and lockdowns on smallholder and commercial agricultural production in KwaZulu-Natal using remotely sensed data

“…Georeferenced land cover points for the year 2018 were obtained from GEOTERRAIMAGE ( Ngcofe et al, 2019 ). They identified the land class of geolocations throughout the country, from which national land cover maps were developed.…”

“…Schoeman et al (2013) and Ngcofe et al (2019) produced land cover maps for South Africa in 2013 and 2018, respectively. They contributed to establishing the South African National Land Classification (SANLC 2013 & 2018) with 72 classes including crop land types.…”

“…The number of studies using remotely sensed images for land cover classification in South Africa has increased in the last decade (see Fig. 1 ) ( Schoeman et al, 2013 ; Odindi et al, 2016 ; Ngcofe et al, 2019 ; Gyamfi-Ampadu et al, 2020 ; Dlamini et al, 2021 ); those studies have the merit of showing how new methods are useful for diverse land cover classification tasks and NDVI extraction. While these studies have produced a number of land cover datasets in South Africa for specific years, there is a gap in producing crop land cover data, for the 2015-2016 drought years, as well as the 2019-2020 years, suitable to analyse the effect of the agricultural input supply chain disruption, caused by lockdown.…”

The relationship between the land and buildings Tax Act, B.E. 2562 (A.D. 2019), and the land use and land cover (LULC) of Bangkok, Thailand

“…Meanwhile, other LULC classes, especially vegetative ones that make up the fringes of Bangkok, are relatively easier to modify and change into other classes due to their lower rigidity. For these reasons, several authors mention that it is highly unlikely for built up areas to be converted into plant-related LULC or any other classes (Schoeman et al, 2013;Seto et al, 2002;Trisurat et al, 2009).…”