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This study aims to highlight the importance of south-to-south cooperation in remote sensing and present preliminary results from our joint project between China and South Africa, focusing on multi-sensor remote sensing data for food security. Scientometric analysis was used to demonstrate growing research output and cooperation among countries in the Global South. Furthermore, the first results of our crop classification methods were presented on a test site in the Sanjiang Plain, China. The preliminary crop classification results from this test site showed promising accuracy. South-to-south cooperation in remote sensing has the potential to address shared challenges and promote sustainable development through exchange of knowledge and resources. The ongoing joint project between China and South Africa demonstrates the benefits of such collaboration in developing robust remote sensing techniques for improved food security monitoring and decision making in the Global South. [ABSTRACT FROM AUTHOR]

Transfrontier Conservation Areas (TFCAs) are critical biodiversity areas for the conservation and sustainable use of biological and cultural resources while promoting regional peace, cooperation, and socio-economic development. Sustainable management of TFCAs is dependent on the availability of an eco-agriculture framework that promotes integrated management of conservation mosaics in terms of food production, environmental protection or the conservation of natural resources, and improved human livelihoods. As a developmental framework, eco-agriculture is significantly influenced by existing legal and governance structures at all levels; this study assessed the impact of existing legal and governance frameworks on eco-agriculture implementation in the Lubombo TFCA that cuts across the borders between Mozambique, Eswatini, and South Africa. The assessment used a mixed research method, including a document review, key informant interviews, and focus group discussions. Although the three countries have no eco-agriculture policies, biodiversity practices are directly or indirectly affected by some policies related to environmental protection, agriculture improvement, and rural development. The assessment found that South Africa has the most comprehensive policies related to eco-agriculture; Mozambican policies mainly focus on equity and involvement of disadvantaged social groups, while Eswatini is conspicuous for explicitly making it the responsibility of each citizen to protect and safeguard the environment. The protection of conservation areas is critical to preserving natural habitats and ensuring the continued provision of ecosystem services. The lack of transboundary governance structures results in the Lubombo TFCA existing as a treaty on paper, as there are no clear processes for transboundary cooperation and collaboration. [ABSTRACT FROM AUTHOR]

Informative and scalable cartography plays a pivotal role in curbing urban pollution, waste management, and mitigating environmental damage in the development of informal settlements. The contemporary capabilities of cloud computing facilitate streamlined access to comprehensive data repositories, computational infrastructure, and proficient tools that have rapidly advanced the execution of sprawl mapping procedures. This study tests the performance of four machine-learning algorithms, namely: Gradient Boost, K Nearest Neighbor [KNN], Random Forest [RF], and Support Vector Machine [SVM] with data extracted from cloud computing repositories for delineating informal settlements in Gcuwa, Eastern Cape, South Africa, using low-cost datasets. A systematic approach comprising iterative phases, encompassing data acquisition, the development of a training dataset, modeling, and evaluation was employed. The delineation process involved the extraction of both spectral and textural features from Sentinel-2 imagery. The Random Forest algorithm emerged as the top performer, exhibiting the highest levels of accuracy and F1 score, followed by the gradient boosting, support vector machine, and then the K-nearest neighbor algorithms. Consequently, this innovative use of machine learning algorithms with low-cost datasets and the scalable, resilient approach for detecting informal settlements offers a promising avenue for enhancing urban planning and addressing sustainable development challenges. [ABSTRACT FROM AUTHOR]

Accurate information on irrigated areas' spatial distribution and extent are crucial in enhancing agricultural water productivity, water resources management, and formulating strategic policies that enhance water and food security and ecologically sustainable development. However, data are typically limited for smallholder irrigated areas, which is key to achieving social equity and equal distribution of financial resources. This study addressed this gap by delineating disaggregated smallholder and commercial irrigated areas through the random forest algorithm, a non-parametric machine learning classifier. Location within or outside former apartheid "homelands" was taken as a proxy for smallholder, and commercial irrigation. Being in a medium rainfall area, the huge irrigation potential of the Inkomati-Usuthu Water Management Area (UWMA) is already well developed for commercial crop production outside former homelands. However, information about the spatial distribution and extent of irrigated areas within former homelands, which is largely informal, was missing. Therefore, we first classified cultivated lands in 2019 and 2020 as a baseline, from where the Normalised Difference Vegetation Index (NDVI) was used to distinguish irrigated from rainfed, focusing on the dry winter period when crops are predominately irrigated. The mapping accuracy of 84.9% improved the efficacy in defining the actual spatial extent of current irrigated areas at both smallholder and commercial spatial scales. The proportion of irrigated areas was high for both commercial (92.5%) and smallholder (96.2%) irrigation. Moreover, smallholder irrigation increased by over 19% between 2019 and 2020, compared to slightly over 7% in the commercial sector. Such information is critical for policy formulation regarding equitable and inclusive water allocation, irrigation expansion, land reform, and food and water security in smallholder farming systems. [ABSTRACT FROM AUTHOR]

Rainfall and air temperature variability pose the greatest risk to environmental change. Past trends in rainfall and air temperature facilitate projecting future climate changes for informed policy responses. We used a combination of the normalised difference vegetation index (NDVI) and observed data from 1968 to 2017 to assess changes in rainfall, moisture stress, and air temperature variability over time on bioclimatic regions of KwaZulu-Natal (KZN) Province, South Africa. Indicators used included consecutive dry days (CDDs), consecutive wet days (CWDs), very heavy rainfall days (R20), monthly maximum daily maximum air temperature (TXx), monthly minimum daily minimum air temperature (TNn), the total number of rainfall days, and monthly air temperature averages. Trends in rainfall and moisture stress are notable in different bioclimatic regions across the province. However, these trends are diverse, in general, and spatially different across and within the bioclimatic regions. Further, related rainfall indicators do not respond in the same way as would be expected. Air temperature trends were consistent with global trends and land–air temperature anomalies. Although daytime air temperatures showed a positive trend, extreme air temperature events and increases are predominant in inland regions. Night-time air temperatures showed an upward trend in most stations across KZN. Local weather-and-climate related characteristics are evolving due to climatic variability and change. The study shows that changes in climatic activities are detectable at a local level from existing historical weather data; therefore, adaptation strategies should be contextualised to respond to local and area-specific challenges. [ABSTRACT FROM AUTHOR]

The challenges emanating from the rapid urbanisation of the Gauteng City-Region in South Africa require innovative strategies to transform the urbanised province into a climate action and adaptation centre. We provide an analysis of the impacts of rapid urbanisation in the Gauteng City-Region, highlighting major challenges related to (i) land use management, (ii) service delivery (water, energy, food, and waste and sanitation), and (iii) social cohesion, applying geospatial techniques to assess spatio-temporal changes and variations in land surface temperatures. Massive impervious surfaces, rising temperatures, flooding and heatwaves are compounding the challenges associated with rapid urbanisation in the city region. We provide an outline of the response pathways towards a sustainable and resilient city-region and inform policy on formulating coherent adaptation urban planning strategies. If not well managed, rapid urbanisation could be a huge environmental and human health risk and a threat to sustainable cities by 2030. Nexus planning is proposed to provide the basis to achieve urban resilience as it integrates distinct, but interlinked sectors, considering both ecological and built infrastructures, in a balanced and integrated manner. • Nexus planning provides the lens and basis to achieve urban resilience in an integrated manner. • Urban nexus guides the formulation of informed and coherent adaptation urban planning strategies. • The developed urban nexus framework provides pathways towards sustainable cities. • Urban areas can become centres for sustainable socio-economic development and for climate action and adaptation. [ABSTRACT FROM AUTHOR]

Declining water resources in dry regions requires sustainable groundwater management as trends indicate increasing groundwater use, but without accountability. The sustainability of groundwater is uncertain, as little is known about its extent and availability, a challenge that requires a quantitative assessment of its current use. This study assessed groundwater use for irrigated agriculture in the Venda-Gazankulu area of Limpopo Province in South Africa using crop evapotranspiration and irrigated crop area derived from the normalised difference vegetation index (NDVI). Evapotranspiration data was derived from the Water Productivity through Open access of Remotely sensed Actual Evapotranspiration and Interception (WaPOR) dataset (250 m resolution), and irrigated areas were characterised using dry season NDVI data derived from Landsat 8. Field surveys were conducted for four years to assess accuracy and for post-classification correction. Daily ET for the dry season (May to September) was developed from the actual ET for the irrigated areas. The irrigated areas were overlaid on the ET map to calculate ET for only irrigated land parcels. Groundwater use during the 2015 dry period was 3627.49 billion m3 and the irrigated area during the same period was 26% of cultivated land. About 82 435 ha of cultivated area was irrigated using 44 million m3/ha of water, compared to 186.93 million m3/ha on a rainfed area of 237 847 ha. Groundwater management is essential for enhancing resilience in arid regions in the advent of water scarcity. • A presentation of a practical remote sensing approach to isolate irrigated croplands during the dry season using NDVI. • The use of crop evapotranspiration data has allowed estimating crop water requirements and consumptive groundwater use. • Evidence shows more water usage in rainfed agriculture than in irrigated agriculture where water is efficiently used. • Remote sensing is a cost and time effective means of assessing groundwater use in irrigated croplands. [ABSTRACT FROM AUTHOR]