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Most emerging economies rely on agriculture, yet over 90% of the sector remains rainfed, which is characterised by low productivity and is highly susceptible to climate change. The focus now is to increase the irrigated area to boost crop-water productivity under climate change. However, there is varied information on actually irrigated areas and no consensus on the factors that should be used to delineate areas suitable for irrigation. This study defined the factors required to delineate areas suitable for irrigation, including rainfall, landuse, closeness to waterbodies, soil characteristics, and groundwater depth. These physical factors were used to delineate irrigation suitability areas in Monze District, Zambia, applying an integrated geospatial technique and the Analytic Hierarchy Process (AHP), a multi-criteria decision method, in ArcGIS. Socio-economic factors were excluded in this instance as they are only ideal for indicating optimal areas to initiate irrigation projects under a set of given conditions, including crop-specific conditions. Accuracy was assessed by overlaying field points of currently irrigated lands obtained during fieldwork on geospatially delineated irrigation suitability areas created in this study. All the fieldwork points matched the modelled irrigation suitability areas, providing the best possible accuracy of 100%. However, there are vast lands that were also mapped as suitable but are not being irrigated, highlighting the underutilisation of the irrigation potential in the study area. The results are significant for policy decisions on irrigation expansion and development. [ABSTRACT FROM AUTHOR]

Botswana’s Notwane watershed is crucial for supplying water to Gaborone and surrounding areas. However, urbanization and climate variability threaten its water resources. Land Use and Land Cover (LULC) changes contribute to climate variability, impacting food security, water supply, and weakening rural economies and socio-cultural structures. This study employs geospatial techniques to model LULC changes and inform sustainable environmental management. This study utilized Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Random Forest (RF) algorithms to classify multi-temporal Landsat images from 1984 to 2022. The XGBoost model achieved the highest overall accuracy of 0.81%. Results indicated a 21,239.2% increase in built-up areas between the year, while agricultural land and natural vegetation decreased significantly by 9.38%. These shifts are driven by urbanization, which heightens climate change through increased greenhouse gas emissions and reduced carbon sinks. Variations in water-covered areas were, influenced by dam construction, droughts, and cyclones. A strong correlation between built-up areas and population data highlights the impact of urban expansion. To ensure sustainable urban growth and mitigate negative effects on biodiversity, urban planners must integrate sustainable land use strategies. These findings highlight the necessity for informed decision-making to balance development with environmental sustainability in the Notwane watershed. [ABSTRACT FROM AUTHOR]

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu. [ABSTRACT FROM AUTHOR]

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]

Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing. [ABSTRACT FROM AUTHOR]

Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%. [ABSTRACT FROM AUTHOR]