The Revised Universal Soil Loss Equation (RUSLE), based on remotely sensed data, is an important tool for assessing erosion prone areas and serves as a guide towards soil conservation efforts. Besides being a crucial data source from which RUSLE parameters can be derived, remotely sensed data can also be used independently to delineate erosion features. This study aims to assess soil erosion risk in the Umzintlava catchment using two independent methods, i.e. RUSLE and Random Forest (RF), and explore the relationship between soil loss and erosion factors as represented by different RUSLE parameters. To achieve this, rainfall, soil, digital elevation, and satellite data were used. The results indicate that a considerable portion (>90%) of the catchment area is of ‘very low’ to ‘low’ erosion risk, while the remainder suffers ‘moderate’ to ‘extremely high’ erosion risk. Among erosion factors, the LS-factor (slope length and steepness) showed strong correlation with soil erosion (p < 0.001; R² = 0.954). This suggests that areas with steep slopes are the most vulnerable to hillslope erosion, whereas gully erosion is prominent in areas with gentle to nearly flat slopes. The integration of RUSLE-derived soil loss and RF-derived erosion features successfully delineated the spatial patterns of soil erosion across the Umzintlava catchment, providing useful information on erosion risk at least costs. This information is instrumental in targeted management interventions to combat soil erosion within the catchment.

Invasive alien plants (IAPs) are nonnative species that pose significant threats to the environment by outcompeting native vegetation and disrupting ecosystem functions. Efforts to monitor and eradicate IAPs have been limited due to the challenges in accurately identifying these plants using traditional remote sensing (RS) methods. This paper reviews the literature to identify the most accurate and reliable plant detection methods for IAPs. Advanced searches were conducted on ScienceDirect, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases using keywords such as ‘Remote Sensing (RS)’, ‘Machine Learning (ML)’, ‘Deep Learning (DL)’, ‘Invasive Alien Plant (IAP)’ and ‘detection’. The search yielded 1689 articles: 1129 focused on the RS methodologies, 303 on ML, 142 on DL and 115 combining all three approaches. The review found that the RF and support vector machine (SVM) algorithms are the most effective for detecting IAPs. This suggests that future research should prioritize the application of ML and DL techniques, particularly RF and SVM, due to their high potential for improving IAP identification and aiding in their eradication. These advancements align with Sustainable Development Goal (SDG) 15, which emphasizes the protection, restoration and sustainable use of terrestrial ecosystems, and SDG 8, which promotes sustained, inclusive and sustainable economic growth. Effective IAP management not only preserves biodiversity but also creates economic opportunities by improving land value and usability. This review underscores the importance of integrating advanced technological methods in environmental management to support both ecological and economic objectives.

This paper explores a regional GIS-based approach to identifying, assessing, and geovisualizing the spatial distribution and patterns of armed conflicts in the Great Lakes Region (GLR) of Central and East Africa from 1998 to 2017. Geospatial analytical techniques, including geocoding, Average Nearest Neighbor (ANN) analysis, and hot spot analysis (Getis-Ord Gi*), were used to identify, map, and visualize conflict patterns, clusters, and hot spots, while kernel density estimation (KDE) analyzed conflict densities per square kilometre. The results reveal that armed conflict in the GLR was significantly clustered from 1998 to 2017, with a 99% confidence level (p < 0.01). The eastern Democratic Republic of the Congo (DRC), bordering Burundi, Rwanda, and Uganda, emerged as the most intense conflict hot spot at a 99% confidence level. These findings align with the KDE results, which also identified pockets of high conflict densities along these borders. The study further highlights the significant role of mineral resources in armed conflicts in the GLR. A chi-square test result (p < 0.005) indicates a statistically significant relationship between rebel locations and mineral resource distribution. Unlike previous geospatial studies that presented fragmented, country-specific analyses of armed conflicts in the GLR, this study adopts a holistic approach by mapping regional conflict hot spots, risk areas, densities, and related factors. This proposed methodology can assist peace-building initiatives, policymakers and stakeholders in better visualizing the spatial distribution of conflict variables, understanding their underlying causes, and developing effective strategies for mitigating and preventing further armed conflicts in the region and beyond.