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The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire severity), adaptive strategies (flowering months, water requirements, resprouter/seeders) and ecological status (increaser or decreaser) for the park. Vegetation recovery index derived from pre- and post-fire normalised difference vegetation index (NDVI) was used with correlation and regression analysis to validate the model. The results showed that amongst the environmental variables, elevation and fire were the most important factors influencing vegetation response-ability followed by soil, aspect, rainfall, and land surface temperature. Almost half (48%) of the park had a high vegetation response-ability, 43% medium, and 9% low. On the other hand, the vegetation recovery index showed 34% of the park fully recovered to pre-fire conditions, while 61% and 5% largely and slightly recovered, respectively. There was a strong correlation between vegetation response-ability and vegetation recovery index. The regression analysis showed a good relationship between vegetation response-ability and vegetation recovery index (R = 0.91) with 83.34% coefficient of determination.

Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by the accelerated changes in the environment due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER IM has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also show that other species had replaced four dominant grass species totaling an area of about 49 km2 throughout the study.

Understanding the spatial distribution of vegetation species is essential to gain knowledge on the recovery process of an ecosystem. Few studies have used deep learning and machine learning models ...

Little has been documented in literature concerning the manner of occurrence and spread of COVID-19 in Africa. Understanding the geographic nature of the corona virus pandemic may offer critical response signals for Africa. This paper employed analysis of variance (ANOVA) to show that significant variations exist among African countries', particularly total population as well as those using basic drinking water services, gross national income, expenditure on health, number of physicians and air transport passengers. Although we have only considered the number of confirmed corona virus infections noting that the fatality may be too early to discuss, we have relied on data from the European Centre for Disease Prevention and Control (ECDC) to establish a significant association between international mobility based on average annual air passenger carried (r ​= ​0.6) which also successfully predicted (R 2 ​= ​0.501) the number of COVID-19 cases reported in each country along with the population density (R 2 ​= ​0.418). We also detected that COVID-19 cases report y geometrically increased daily x (R 2 ​= ​0.860) with a 2nd order polynomial equation in the form of y ​= ​0.3993 â€‹× â€‹2-8.7569 x and a clustered spatial pattern with a nearest neighbour ratio of 0.025 significant at 0.05 α-level. African countries have responded to the pandemic in different ways including partial lockdown, closure of borders and airports as well as providing test centres. We concluded that 40% of Africa are categorized as emerging hot spots while responses differ significantly across regions.

With A. mearnsii being one of the most successful invaders globally, its management has become a large part of invasive alien species management programs, especially in South Africa, where a significant investment of about 561.9 million rands has been made to eliminate it. Despite this huge investment, there is still no sign that the spread of this species has diminished. To ensure that the damage caused by A. mearnsii is halted, professionals must understand how it spreads and persists. Accurate data on the spatial distribution of A. mearnsii might help to develop useful and successful management techniques for savannah environments, particularly in protected mountainous grasslands. This study, therefore, developed a GIS model for predicting the potential spatial spread of A. mearnsii in a protected mountainous grassland ecosystem using spatiotemporal data derived from Landsat and Sentinel-2 imageries. The study used environmental factors like slope aspect, elevation, distance from streams, and soil type and rainfall. Other climatic variables like land surface temperature derived from the thermal band of Landsat images were included in the environmental factors. The influence of species interactions with fire severity and animal populations was also included as indicator variables. The species' AUC of 0.83 is estimated, and the model fit is a sign of a successful model. The study showed that the spread of A. mearnsii was influenced primarily by the following factors: The average Land surface temperature, aspect, changes in land surface temperature, and rain from 2000 to the present, and distance to the river. The species increased in areas where there was a reduction in both land surface temperature and rainfall. The predicted circumstances of decreasing temperature and rainfall events may lead to widespread invasion by A. mearnsii while nevertheless changing community structure, composition, hydrological properties, and other essential ecosystem services in the examined region.

Soil erosion affects about half of the global land area resulting in annual average soil losses of eight to about 50 t/ha. Soil loss leads to challenges in agricultural production, threatens soil health, and ecosystem services, especially in mountainous regions. Ecosystem managers, therefore, must carry out an appropriate evaluation of the amount of soil lost by water erosion for sustainable soil management. Therefore, this study generated a small-scale soil erosion model with site-specific data obtained from field observations, Sentinel-2 multispectral images, 30 meters SRTM, and rainfall data using RUSLE with advanced GIS and remote sensing techniques. The results showed an average yearly soil erosion of 13 t/ha year, with a low to extremely high erosion covering about 21 % of the park. Areas with low vegetation protective cover of less than 40% experienced a high level of erosion, and areas with high topographic factors experienced a very low amount of soil loss. Validation was done using an error matrix between observed field soil erosion and predicted soil erosion resulting in an accuracy of 80%.

Rapid urbanization is having a considerable impact on various aspects of living, thereby altering the biophysical environment. This study adopted the use of remote sensing technique and geographical information system (GIS) to analyse the relationship between changing land use/land cover and land surface temperature in a rapidly urbanizing tropical city of Ibadan between 1984 and 2019. Landsat series TM, ETM+, and OLI satellite imageries of Ibadan region city for 1984, 2002, and 2019, respectively, were obtained from the US Geological Survey (USGS) Landsat series of Earth Observation satellites accessible on the Google earth engine (GEE) platform. Supervised classification was done using a random forest (RF) machine learning classifier in the GEE platform. Surface emissivity maps were obtained from the normalized difference vegetation index (NDVI) thresholds method and land cover information. The surface emissivity based on NDVI classes was used to retrieve land surface temperature (LST). The results showed an increase in urban cover from 341.72 km2 in 1984 to 520.58 km2 in 2019 with an average increase in land surface temperature from 17 °C to 38 °C, respectively. Temperature sampling in the north-south and west-east transect revealed that highly urbanized areas located at the city centre of Ibadan have the highest LST of about 38 °C. It dissipates to about 19 °C at the suburb that is less built up. A significant negative relationship exists between the health condition of vegetation (NDVI) and LST with a correlation coefficient of r = − 0.95. The study confirms the potential application of GIS and remote sensing for detecting urban growth as well as relates growth impact to LST, thereby suggesting that fitting strategies will be important for the sustainable management of the urban areas.

This study assessed transportation logistics for prospective consumers and marketers of white shrimp (Nematopalaemon hastatus) within a 500 km buffer to processing and marketing areas in Ondo State, Nigeria using ArcGIS network analyst tool. The result indicated that shorter routes between the towns and major shrimp market at Igbokoda provided buyers with a lower budget for logistics and product cost compared with higher budget computed for longer routes between the towns and Ugbonla waterside, Nigeria. However, due to lower price of smoked shrimp at processing areas, shrimp product would get to the towns/cities within the buffer per kilogramme at a lower cost compared to products directly sourced from marketing areas (Igbokoda). Thus a typical consumer/marketer within the buffer was expected to budget an average of $42-$110 (12,810-35,500) and $59-$123 (17,995-37,515) for 10.42kg of smoked shrimp from processing areas in a typical peak and off-peak month respectively. In the alternative, they are expected to budget an average of $50-$110 (15,250-33,550) and $60-$125 (18,300-38,125) to source same quantity of the species from Igbokoda in a typical peak and off-peak month respectively. Developing a shrimp market at Ugbonla waterside, Nigeria would open the community for more economic development and attraction due to easy accessibility to shrimping and processing areas. Therefore, prospective shrimp marketers and consumers within the 500km buffer who are interested in accessing this highly nutritional species and other aquatic products from the processing and marketing areascould be guided by this information for effective trade and marketing.

While empirical works that analyse rural–urban differentials in accessibility to health services are common in the developing countries, systematic studies focusing on intra-urban variations in accessibility to overall healthcare resources remain scant. Yet, many urban dwellers especially in the Sub-Saharan Africa have to travel long distances within the urban space to access basic health services. This study employs composed index of critical accessibility (CICA) to analyse differentials in access to health resources in Ibadan, a traditional African City. Data on healthcare centre location, ownership, doctors’ population were collected through field survey while spatial data on the administrative units and populated places were obtained from archival sources. Also, data on road networks was collected. Results show that the distribution of health facilities across local administrative units varies. In terms health facilities, Ibadan South West has 26 (36 %) of the total number while Ibadan South east has only 7 (9.8 %). The Doctor-Population Ratio also exhibits similar variations as Ibadan North has 20.5 as against 2.2, 1.9 and 0.3 for Ibadan SW, Ibadan NW and Ibadan NE respectively. The CICA analysis shows that 228,938 inhabitants are in high risk of negative accessibility while 49,234 are faced with condition of low risk of negative accessibility, which implies that a significant proportion of the population still finds it difficult to access basic health services as and when needed. The variations in the distribution of this deprived population are also noteworthy as the largest proportion of this group was localized in Ibadan NW. This has grave implication for the health and wellbeing of the population and raises once again the question of spatial equity in the delivery of urban public service. Curiously, these differences in the level of accessibility cut across administrative boundaries which brings to the fore the role of jurisdictional partitioning in ensuring equity in the delivery of urban public services. This opens a vista of research opportunity into the role jurisdictional partitioning in service provision in the urban centres.

The frequency and intensity of flood disasters have become serious issues in the development process as flood disasters have caused serious environmental damage, loss of human lives and wanton destruction of economic assets globally. Loss of human lives and development assets, rising costs of reconstruction efforts and associated hardship are putting the issue of disaster reduction and risk management higher on the policy agenda of affected governments, multilateral agencies and NGOs. The starting point of concrete flood disaster mitigation efforts is to identify the areas with higher risk levels and fashion out appropriate preventive and response mechanisms.