Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km² to 49.5 km² in Umhlabuyalingana; 54 km² to 1.6 km² in Joe Morolong; and 254.6 km² to 7.4 km² in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas.

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.

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.

A flux tower with eddy covariance instruments was recently erected at Benfontein Nature Reserve (BNR) outside of Kimberley. An understanding of the landscape within the flux tower footprint, in this case natural vegetation, is necessary to interpret the data collected by the eddy covariance instruments. The tower was erected as part of the Expanded Freshwater and Terrestrial Environmental Observation Network (EFTEON) infrastructure initiative to establish long-term monitoring platforms focused on socially relevant terrestrial landscapes and their coupled hydrological systems. We sought to describe the vegetation within the flux tower footprint to set a baseline of the vegetation in the landscape at the inception of the long-term monitoring of flux measurements at BNR. Woody vegetation was surveyed in five square 1 ha plots while the herbaceous layer was sampled across 105 circular plots following the Socio-Ecological Observatory for Southern African Woodland (SEOSAW) protocol. Woody vegetation was quantified in terms of abundance, diversity and biomass, together with Size Class Distribution (SCD) methods to describe the woody vegetation structure. The herbaceous layer was described by species count, basal cover, biomass, and diversity which was assessed using the Shannon-Weiner Diversity Index. Five tree species, dominated by Vachellia erioloba, were found in the plots. Recruits (0 and 1 m tall) were significantly more abundant than large trees (1.2–9.5 m high). The SCD for the woody vegetation yielded a Type IIIa curve i.e. populations missing one or more size classes, for all tree species except Ziziphus mucronata which yielded a Type IIIb curve representing a population missing small sized trees. The herbaceous layer was diverse, consisting of 10 grass and 32 forb species. Schmidtia pappophoroides was the dominant grass species in terms of count, basal cover and aboveground biomass. Herbaceous biomass was two times higher than that of woody vegetation, with the North plot contributing the highest biomass for herbaceous and woody vegetation. The results indicate that the vegetation around the flux tower represents a wooded grassland, which is a type of a savanna. These vegetation data will contribute to the interpretation of data collected by the flux tower instruments now and in the future. Furthermore, long-term data collection in this reserve is needed to capture the woody vegetation dynamics and its interaction with the herbaceous layer.

Accurate mapping of woody biomass and its temporal change is critical for understanding the global carbon balance, informing restoration and supporting evidence‐based policy on climate mitigation and livelihoods. The vegetation of the dry tropics, comprised largely of dry forests and savannas, holds substantial woody carbon stocks, but both stocks and change over time remain uncertain. Where Earth Observation (EO) data have been used to estimate change over time, different products give conflicting results on the direction, magnitude and location of change. We present pan‐dry‐tropical maps of aboveground carbon density (AGCD) and AGCD change at 100 m resolution for the years 2015–2024, developed using ALOS ScanSAR satellite radar observations calibrated with in situ measurements ( n = 237). These maps suggest an aboveground woody carbon pool (AGC) in the dry tropics of 25.3 2.0 PgC, ~13.0% of global AGC. Net AGC has increased by 0.43 0.07 PgC yr ⁻¹ , ~20.0% of the global net land sink. The overall net increase (0.43 PgC yr ⁻¹ ) is comprised of gross losses from deforestation and degradation (0.27 0.05 PgC yr ⁻¹ ) and gains due to densification and extensification of 0.59 0.1 PgC yr ⁻¹ , with the balance accounted for by small changes (change < 20% of AGCD; 0.11 PgC yr ⁻¹ ). Losses are equally split between degradation and deforestation, while gains are divided roughly 2:1 between the densification of woody ecosystems and expansion into previously grass‐dominated areas. These new maps, created with a dry‐tropics‐specific EO method, highlight the importance of tropical dry forests and savannas in the global carbon cycle. The prominent role of degradation‐related losses suggests that climate change mitigation will need to focus much more on reducing losses from overharvesting than at present. The widespread increases in carbon density and the increase in tree biomass into open systems present major challenges for biodiversity conservation, restoration and livelihoods.