Geospatial technologies play an important role in understanding and monitoring of land cover and land use change which is critical in achieving Sustainable Development Goal (SDG) 11 and related goals. In this study, we assessed SDG Indicator 11.3.1, Ratio of Land Consumption Rate to Population Growth Rate (LCRPGR) and other urban growth trends of four cities in South Africa using Landsat 5 TM and SPOT 2&5 satellite images and census data collected in 1996, 2001 and 2011. The 2011 built-up areas were mapped using South Africa’s SPOT 5 Global Human Settlements Layer (GHSL) system whereas the 1996 and 2001 built-up areas were extracted from Landsat 5 and SPOT 2 satellite imagery using a kNN object-based image analysis technique that uses textural and radiometric features. We used the built-up area layer to calculate the land consumption per capita and total urban change for each city, both of which have been identified as being important explanatory indicators for the ratio of LCRPGR. The assessment shows that the two major cities, Johannesburg and Tshwane, recorded a decline in the ratio of LCRPGR between the periods 1996–2001 and 2001–2011. In contrast, the LCRPGR ratios for secondary cities, Polokwane and Rustenburg increased during the same periods. The results further show that Tshwane recorded an increase in land consumption per capita between 1996 and 2001 followed by a decrease between 2001 and 2011. Over the same time, Johannesburg experienced a gradual decrease in land consumption per capita. On the other hand, Polokwane and Rustenburg recorded a unique growth trend, in which the overall increase in LCRPGR was accompanied by a decrease in land consumption per capita. In terms of land consumption, Tshwane experienced the highest urban growth rate between 1996 and 2001, whereas Johannesburg and Polokwane experienced the highest urban growth rates between 2001 and 2011. The information derived in this study shows the significance of Indicator 11.3.1 in understanding the urbanization trends in cities of different sizes in South Africa and creates a baseline for nationwide assessment of SDG 11.3.1.

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.

Monitoring urban growth around Rustenburg, South Africa, using SPOT 5

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.

Research on the detection of informal settlements has increased in the past three decades owing to the availability of high- to very-high-spatial-resolution satellite imagery. The achievement of development goals, such as the Sustainable Development Goals, requires access to up-to-date information on informal settlements. This review provides an overview of studies that used object-based image analysis (OBIA) techniques to detect informal settlements using remotely sensed data. This paper focuses on three main aspects: image processing steps followed when detecting informal settlements using OBIA; informal settlement indicators and image-based proxies used to detect informal settlements; and a review of studies that extracted and analyzed informal settlement land use objects. The success of OBIA in detecting informal settlements depends on the understanding and selection of informal settlement indicators and image-based proxies used during image classification. To meet the local ontology of informal settlements, the transfer of OBIA mapping techniques requires the fine-tuning of the rulesets. Machine learning OBIA techniques using image proxies derived from multiple sensors increase the opportunities for detecting informal settlements on the city or national level.