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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]

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]