Land cover change in marginalised landscapes of South Africa (1984–2014): Insights into the influence of socio-economic and political factors

Rural landscapes in South Africa experience high conversion rates due to intense land use; however, the changes are site specific and depend on the socio-economic and political history of the area. Land cover change (LCC) was assessed in response to socio-economic and political factors in uThukela Municipal District, KwaZulu-Natal, using Landsat imagery from 1984 to 2014, while making comparisons to other studies in South Africa. Socio-economic/political data were used to gain insights into the observed LCC patterns. Land cover was classified using a random forest classifier, and accuracies ranging from 87% to 92% were achieved. Systematic and intensity analysis methods were used to describe patterns, rates, and transitions of LCC in Imbabazane (ILM) and Okhahlamba (OLM) local municipalities. The results showed a reduced rate of change intensity from 3.4% to 0.9% in ILM and from 3.1% to 1.1% in OLM between 1984 and 2014. Grassland was persistent, covering over 70% in both local municipalities between 1984 and 2014. Although persistent, grassland experienced respective losses of 3.7% and 14.3% in both observation periods in ILM and of 10.2% and 13.3% in OLM. During the analysis period, settlements and cropland gained actively in both local municipalities. The changes represent a degree of population, local authority, and people’s perception as influencers of land use and LCC. It is therefore argued that socio-economic and political changes can potentially influence land use and LCC; however, natural ecosystems can persist under those conditions, and this requires more research efforts. Significance: This study contributes towards a growing knowledge and understanding of land cover change studies in marginalised landscapes in South Africa. The findings enforce the notion that natural vegetation systems can be altered by human-induced land use such as expansion of settlement and commercial agricultural. We show that in recent times there has been a decline in the overall rate of land cover conversion, and a high persistence of grassland amid global change, although the quality of the vegetation needs further research. We argue that the changes observed in marginalised landscapes are potentially driven by socio-economic and political dynamics

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest ClassifiersRapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n \u3e 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous)” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands”) which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping

Vegetation type conservation targets, status and level of protection in KwaZulu-Natal in 2016

Background: Systematic conservation planning aims to ensure representivity and persistence of biodiversity. Quantitative targets set to meet these aims provide a yardstick with which to measure the current conservation status of biodiversity features and measure the success of conservation actions. Objectives: The conservation targets and current ecosystem status of vegetation types and biomes occurring in KwaZulu-Natal (KZN) were assessed, and their level of formal protection was determined, to inform conservation planning initiatives in the province. Method: Land cover maps of the province were used to determine the amount of natural habitat remaining in KZN. This was intersected with the vegetation map and assessed relative to their conservation targets to determine the ecosystem status of each vegetation type in KZN. The proclaimed protected areas were used to determine the level of protection of each vegetation type. Results: In 17 years (1994–2011), 19.7% of natural habitat was lost to anthropogenic conversion of the landscape. The Indian Ocean Coastal Belt and Grassland biomes had the least remaining natural habitat, the highest rates of habitat loss and the least degree of formal protection. Conclusion: These findings inform conservation priorities in the province. Vegetation type targets need to be revised to ensure long-term persistence. Business-as-usual is no longer an option if we are to meet the legislative requirements and mandates to conserve the environment for current and future generations

Predictive modelling of the potential future distribution of Vachellia nilotica within the KwaZulu-Natal province of South Africa

Many regions of South Africa are prone to woody plant thickening. This can have an ecologically detrimental effect on the open savannas and grasslands. KwaZulu-Natal, a province on the east coast of South Africa, is currently experiencing an increase in both the density and distribution of Vachellia nilotica. This research aims to gain better insight into the potential distribution of this plant and to determine some of the main environmental conditions that promote its thickening. Using the Maxent programme to determine the potential distribution, a map was developed to illustrate the possible extent of V. nilotica within KwaZulu-Natal. It is estimated that a possible 800 968 ha (8.5%) of the province has a greater than 50% distribution probability, whereas in 26.9% of the province there is 25%–50% probability of V. nilotica inhabiting these areas. Using Maxent, it was determined that geology and altitude were key determinants for V. nilotica habitat selection. This model-based map will be particularly useful for conservation and rangeland planning for future management and control of the plant through being able to predict which areas of the province are more likely to be high potential regions for the thickening of V. nilotica.Keywords: bush thickening, KwaZulu-Natal, Maxent, species distribution model, Vachellia nilotic

Rates and patterns of habitat loss across South Africa’s vegetation biomes

The loss of natural habitat resulting from human activities is the principal driver of biodiversity loss in terrestrial ecosystems globally. Metrics of habitat loss are monitored at national and global scales using various remote sensing based land-cover change products. The metrics go on to inform reporting processes, biodiversity assessments, land-use decision-making and strategic planning in the environmental and conservation sector. We present key metrics of habitat loss across South Africa at national and biome levels for the first time. We discuss the spatial patterns and trends, and the implications and limitations of the metrics. Approximately 22% of the natural habitat of South Africa has been lost since the arrival of European settlers. The extent and the rate of habitat loss are not uniform across South Africa. The relatively mesic Grassland, Fynbos and Indian Ocean Coastal Belt biomes have lost the most habitat, while the arid Nama-Karoo, Succulent Karoo and Desert have lost the least. Rates of loss increased across all biomes in recent years (2014–2018), indicating that the historical drivers of change (i.e. expansion of croplands, human settlements, plantation forestry and mining) are intensifying overall. We should caution that the losses we report are conservative, because the land-cover change products do not capture degradation within natural ecosystems. Preventing widespread biodiversity losses and securing the benefits we derive from biodiversity requires slowing and preventing further habitat degradation and loss by using existing land-use planning and regulatory tools to their full potential. Significance: The loss of natural habitat resulting from human activities is the principal driver of biodiversity loss in terrestrial ecosystems in South Africa. Monitoring trends and patterns of habitat loss at a national scale provides a basis for informed environmental decision-making and planning, thus equipping civil society and government to address habitat loss and protect biodiversity while also meeting key development and socio-economic needs. Open data set: https://doi.org/10.15493/SAEON.FYNBOS.1000001

Guest Editorial: Montane rangelands in a changing world

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