Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.

The successful implementation of the African Union’s Agenda 2063 strategic development blueprint is critical for the attainment of economic development, social prosperity, political stability, protection, and regional integration in Africa. Agenda 2063 is a strategic and endogenous development plan that seeks to strategically and competitively reposition the African continent to ensure poverty eradication and equitable people-centric socio-economic and technological transformation. Its impact areas include wealth creation, shared prosperity, sustainable environment, and transformative capacities. Monitoring and evaluation systems play a critical role in collecting, recording, storing, integrating, and evaluating and tracking performance information in the implementation of longer-term strategic plans. The usage of the geographic information system (GIS) as a monitoring and evaluation tool has gained traction in the last few decades due to its ability to support the collection, integration, storage, analysis, output, and distribution of location-based data. The advent of web-based GIS provides a powerful online platform to collect, integrate, discover, use and share geospatial data, information, and services related to sustainable development. In this paper, we aim to describe the implementation, architectural structural design, and the functionality of the pilot Agenda 2063 geoportal. The live prototype internet-based geoportal is intended to facilitate data collection, management, integration, analysis, and visualization of Agenda 2063 development indicators. This geoportal is meant to support the planning, implementation, and monitoring of the Agenda 2063 goals at the continental, regional, and national levels. As our results show, we successfully demonstrated that a web-geoportal is a powerful interactive platform to upload, access, explore, visualize, analyse, and disseminate geospatial data related to the sustainable development of the African continent. Although in the pilot phase, the geoportal demonstrates the primary functionality of geoportals in terms of its capability to discover, analyse, share, and download geospatial datasets.

Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.

The surge in the number of earth observation satellites being launched worldwide is placing significant pressure on the satellite-direct ground receiving stations that are responsible for systematic data acquisition, processing, archiving, and dissemination of earth observation data. Growth in the number of satellite sensors has a bearing on the ground segment payload data processing systems due to the complexity, volume, and variety of the data emanating from the different sensors. In this paper, we have aimed to present a generic, multi-mission, modularized payload data processing system that we are implementing to optimize satellite data processing from historical and current sensors, directly received at the South African National Space Agency’s (SANSA) ground receiving station. We have presented the architectural framework for the multi-mission processing system, which is comprised of five processing modules, i.e., the data ingestion module, a radiometric and geometric processing module, atmospheric correction and Analysis Ready Data (ARD) module, Value Added Products (VAPS) module, and lastly, a packaging and delivery module. Our results indicate that the open architecture, multi-mission processing system, when implemented, eliminated the bottlenecks linked with proprietary mono-mission systems. The customizable architecture enabled us to optimize our processing in line with our hardware capacities, and that resulted in significant gains in large-scale image processing efficiencies. The modularized, multi-mission data processing enabled seamless end-to-end image processing, as demonstrated by the capability of the multi-mission system to execute geometric and radiometric corrections to the extent of making it analysis-ready. The processing workflows were highly scalable and enabled us to generate higher-level thematic information products from the ingestion of raw data.