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

Spatio-temporal mixed pixel analysis of savanna ecosystems : a review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.The Deutscher Akademischer Austauschdienst and the Federal Ministry of Education and Research (BMBF) within the framework of the Strategy “Research for Sustainability” (FONA).http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa

Increasing woody cover and overgrazing in semi arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. The goal of this study is to synergistically combine SAR (Sentinel 1) and optical (Sentinel 2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities. We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentine 1 VH backscatter (vertical horizontal/cross polarization) and the Sentinel 2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out