Map Of Africa Research Articles

Applications and challenges of digital soil mapping in Africa

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions.

Carbonatite research: The African Legacy

Almost all igneous rocks are composed of silicate minerals; carbonatites are the main exception to this rule. They form only a minor proportion of the continental crust but are of fundamental scientific and economic importance. These rocks, originally described as a limestone (Kaiserstuhl, Germany) or magmatic limestone (Alnö, Sweden), were recognized in 1921 by W. C. Brøgger as a distinct magmatic rock type under the name “Karbonatite” in the Fen complex (Norway). Extensive field mapping in Africa, and particularly studies within the Chilwa Alkaline Province (Malawi), have led to the discovery of diverse intrusive and extrusive carbonatites. The latter, including the Fort Portal volcanic field in Uganda, Rufunsa Province in Zambia, and Oldoinyo Lengai volcano in Tanzania (the only volcano ever to have been seen to erupt carbonatites), have been exceptionally important in the recognition of carbonatites as truly magmatic rocks. The possibility of the existence of carbonate melts has been confirmed and shown by experimental studies of diverse carbonate systems with added volatile components (H2O, F) and alkali elements (Na, K). The study of the Oldoinyo Lengai gregoryite-nyerereite carbonatites, which are mineralogically and compositionally different from all known carbonatites worldwide, has led to long-lasting discussions about the origin of carbonatites. This includes composition of primary/parental carbonate melt, derivation of carbonatites by either liquid immiscibility or fractional crystallization, carbonatite evolution, and especially, the possible genetic relationships between alkali-rich and alkali-poor carbonatites. The rapid alteration of Oldoinyo Lengai carbonatites and their transformation to calcite carbonatite-like rocks has been proposed as the explanation for the absence of alkali-rich carbonatites in the geological past. Detailed mineralogical studies have shown that the occurrence of nyerereite is not restricted to Oldoinyo Lengai and that this mineral is now known to occur in other carbonatites (e.g., Guly, Kovdor, Oka, Kerimasi), alkaline rocks, and kimberlites (and even in diamond). This would suggest compositionally different mantle-derived melts enriched in alkali elements. In addition, carbonatite tephra has an important role in the preservation of some key paleontologic and anthropologic localities in East Africa. Despite these important discoveries, several problems related to carbonatite petrogenesis are not yet resolved. Future work is required, and carbonatites within Africa, with its key localities, may help to solve these problems.

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review.

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

Lumumba in the Arts

Lumumba in the Arts

Geothermal renewable energy prospects of the African continent using GIS

Geothermal energy potential is identified through the deep investigation of geological, geothermal, and geophysical information over a small area; however, this task is both expensive and complex. Geographic Information Systems (GIS) can integrate different types of data (or thematic layers) over large regions and use them to identify zones of high geothermal potential. In this study, we use GIS as a tool to construct the first regional-scale geothermal potential map of Africa from different datasets. The key objective of this study is to estimate the geothermally promising areas within Africa by integrating geological thematic layers (rock units and faults), geophysical layers (heat flow derived from aeromagnetic data and seismicity), and geothermal layers (hot springs and volcanoes) within the GIS database. A weighted overlay technique within the GIS environment is applied to these data to generate the geothermal potential map. The result shows 14 regions with a high geothermal favorability index. The geothermal potential map of Africa is useful for targeting and exploring new geothermal renewable energy sites and can reduce exploration costs and pinpoint investigation areas during preliminary geothermal studies.

The Mapping of Africa on the Nautical Charts of the Age of Discovery

The shape of Africa on Iberian nautical charts of the late-fifteenth and early-sixteenth century is surprisingly mature. The portrayal of the outline of Africa on the Cantino planisphere of 1502 is so good that it was not surpassed in the next two, possibly three centuries. The African coastline on the Cantino planisphere is a mosaic of accurate regional charts on the plate-carrée projection, each with its own scale and orientation. The same holds for its Iberian predecessors. The shape of the parts of Africa depicted on these regional charts was essentially correct on the oldest chart (c. 1471) and was copied to later charts. The projective properties of the regional charts are incompatible with the navigation and charting techniques used in that period. Therefore, serious doubt is cast on the established view that Portuguese pilots and cartographers were the original creators of the source charts.

Toward digital agricultural mapping in Africa: evidence of Northern Nigeria

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

‘I Will Open a Path into the Interior (of Africa), or Perish’: David Livingstone and the Mapping of Africa

ABSTRACT Although David Livingstone came to southern Africa in 1840 as a medical missionary, he soon succumbed to the lure of geographical discovery. Between 1849 and his death in 1873 he travelled widely in south-central Africa and managed to irreversibly change the map of this part of the continent. Although much has been written about his character, adventures and travels, little has been said about the maps he compiled and even less about how he made those maps. This article is an attempt to elucidate this rather unknown facet of his legacy by referring to the instruments, methods and techniques he used to collect his data and the high premium he put on the accuracy of his observations. Attention is also given to his life-long friendship with HM Astronomer at the Cape, Sir Thomas Maclear to whom he regularly sent his observations to be checked and his occasionally tempestuous relationship with the official cartographer of the Royal Geographical Society, John Arrowsmith.

Application of Deep Learning in Satellite Image-based Land Cover Mapping in Africa

Deep Learning Networks (DLN), in particular, Convolutional Neural Networks (CNN) has achieved state-of-the-art results in various computer vision tasks including automatic land cover classification from satellite images. However, despite its remarkable performance and broad use in developed countries, using this advanced machine learning algorithm has remained a huge challenge in developing continents such as Africa. This is because the necessary tools, techniques, and technical skills needed to utilize DL networks are very scarce or expensive. Recently, new approaches to satellite image-based land cover classification with DL have yielded significant breakthroughs, offering novel opportunities for its further development and application. This can be taken advantage of in low resources continents such as Africa. This paper aims to review some of these notable challenges to the application of DL for satellite image-based classification tasks in developing continents. Then, review the emerging solutions as well as the prospects of their use. Harnessing the power of satellite data and deep learning for land cover mapping will help many of the developing continents make informed policies and decisions to address some of its most pressing challenges including urban and regional planning, environmental protection and management, agricultural development, forest management and disaster and risks mitigation.

Estimation of the HEC-HMS model parameters in data-scarce regions. Application to the Ouergha watershed (Sebou, Morocco)

The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.

S K Pottekkatt’s In the Land of the Kappiris: A Journey into the Heart of Africa

Exploration and mapping of Africa began around 1850 and continued intothe nineteenth century.S. K Pottekkatt, the Indian travel writer from Kerala State in India, set out to Africa. This study focusses on S. K Pottekkat as a travel writer who made Malayalis aware of the geography, life and culture of Africa. The Kappiri had no place in the land of his birth. Beira was a source of great relief for Indians travelling from other countries because Portuguese Africa was fairly free from racial discrimination. The Gujarati businessman was prominent. They had mastered the language of the natives and had in depth knowledge of their customs as well as their needs and so prospered well. Who had the greater right in Africa: the British or the Indians?

Looking at Africa with Lisbon Eyes

Suddenly, in the summer of 1960, the map of Africa gained a new political outline, with the proclamation of nine independences. But the majority of Luanda’s newspapers appeared not to see the ‘wind of change’ blowing through the continent. In its pages, the news of Africa would barely mention more than the crisis in one of its neighbours, the former Belgian Congo. The analysis of the news about the proclamation of independences leads us to formulate the hypothesis that the relative invisibility of the smooth transitions and the major emphasis given to turbulent situations in the continent contributed to value the Portuguese colonial model. Crossing the analysis with research made in archive, this article also contributes to the characterisation of the Angolan daily press on the eve of the Portuguese colonial war.

English

The aim of the present study is to test ESA’s Sentinel-2 (S2) satellites (S2A and S2B) for an efficient quantification of land cover (LC) and forest compositions in a tropical environment southwest of Mount Kenya. Furthermore, outcome of the research is used to validate ESA’s S2 prototype LC 20 m map of Africa that was produced in 2016. A decision tree that is based on significant altitudinal ranges was used to discriminate four natural tree compositions that occur within the investigation area. In addition, the classification process was supported by Google Earth images, and land use (LU) data that were provided by the local Kenyan Forest Service (KFS). Final classification products include four LC classes and five subclasses of forest (four natural forest subclasses plus one non-natural forest class). Results of the Jeffries-Matusita (JM) distance test show significant differences in spectral separability between all classes. Furthermore, the study identifies spectral signatures and significant wavelengths for a classification of all LC classes and forest subclasses where wavelengths of SWIR and the red-edge domain show highest importance for the discrimination of tree compositions. Finally, considerable differences can be seen between the utilized multi-temporal classification set (total of 39 bands from three acquisition dates) and ESA’s S2 prototype LC 20 m map of Africa 2016. A visual comparison of ESA’s prototype map within the investigation area indicates an overrepresentation of tree cover areas (as confirmed in previous studies) and also an underrepresentation of water.   Key words: Tropical tree composites, Mt. Kenya, Sentinel-2, ESA S2 LC 20 m map of Africa.

Map of Africa

Other| May 01 2020 Map of Africa Current History (2020) 119 (817): 201. https://doi.org/10.1525/curh.2020.119.817.201 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Map of Africa. Current History 1 May 2020; 119 (817): 201. doi: https://doi.org/10.1525/curh.2020.119.817.201 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentCurrent History Search This content is only available via PDF. © 2020 Current History. All rights reserved.2020The Regents of the University of California Article PDF first page preview Close Modal You do not currently have access to this content.

Understanding the effects of dichotomization of continuous outcomes on geostatistical inference

Diagnosis is often based on the exceedance or not of continuous health indicators of a predefined cut-off value, so as to classify patients into positives and negatives for the disease under investigation. In this paper, we investigate the effects of dichotomization of spatially-referenced continuous outcome variables on geostatistical inference. Although this issue has been extensively studied in other fields, dichotomization is still a common practice in epidemiological studies. Furthermore, the effects of this practice in the context of prevalence mapping have not been fully understood. Here, we demonstrate how spatial correlation affects the loss of information due to dichotomization, how linear geostatistical models can be used to map disease prevalence and thus avoid dichotomization, and finally, how dichotomization affects our predictive inference on prevalence. To pursue these objectives, we develop a metric, based on the composite likelihood, which can be used to quantify the potential loss of information after dichotomization without requiring the fitting of Binomial geostatistical models. Through a simulation study and two applications on disease mapping in Africa, we show that, as thresholds used for dichotomization move further away from the mean of the underlying process, the performance of binomial geostatistical models deteriorates substantially. We also find that dichotomization can lead to the loss of fine scale features of disease prevalence and increased uncertainty in the parameter estimates, especially in the presence of a large noise to signal ratio. These findings strongly support the conclusions from previous studies that dichotomization should be always avoided whenever feasible.

Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km2 and 200 km2, respectively, during the entire period, with an accelerated increase analysed during the last period (2010–2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land.

Tristes Tropique in Jacqueline Bishop's Patchwork Aesthetics

Tristes Tropique in Jacqueline Bishop's Patchwork Aesthetics

Map of Africa

Other| May 01 2019 Map of Africa Current History (2019) 118 (808): 201. https://doi.org/10.1525/curh.2019.118.808.201 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Map of Africa. Current History 1 May 2019; 118 (808): 201. doi: https://doi.org/10.1525/curh.2019.118.808.201 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentCurrent History Search This content is only available via PDF. © 2019 Current History. All rights reserved.2019The Regents of the University of California Article PDF first page preview Close Modal You do not currently have access to this content.

Gross morphological and biometrical Studies on the typical cervical vertebrae of Blue bull (Boselaphus tragocamelus)

The present study was carried out on the third (C3), fourth (C4) and fifth (C5) cervical vertebrae of six specimens of adult Blue bull (Boselaphus tragocamelus) of either sex. It was found that the transverse process was bifid with a transverse foramen located at its base. The upper part of the transverse process projected backward and was short and stout, while the lower part was directed downward and forward and was found to be longer and more plate-like. The bodies of C3-C5 tended to become shorter and wider from front to backwards. The supraspinous process was found to be short and centrally tuberous in C3 that gradually increased in height and length to C5 with forward inclination. The articular processes of the typical cervical vertebrae slightly convex cranially and concave caudally. The posterior articular facets of C5 were egg shaped, whereas those of C4 were like the map of Africa in the Blue bull. A well defined infraspinous process projected from the vertebral body that divided the base into two halves. The biometrical observations on different parameters of typical cervical vertebra reflected significance (P less than 0.05) differences between the sexes of this species.

Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt, Ethiopia, and South Africa

ABSTRACTAccurate land-use/land-cover mapping based on remote-sensing images depends on clear and frequent observations. This study aimed to explore how many Landsat images were needed within a year and when they should be acquired, for cropland mapping in Africa. Three Landsat footprints in Egypt (Path/Row: 177/039, 127 images), Ethiopia (Path/Row: 168/054, 98 images), and South Africa (Path/Row: 170/078, 207 images) from 1984 to 2016 were used together with spectral indices and a 30-m digital elevation model in a random forest-based supervised classification. Detailed exploration was conducted into the number and temporal distribution of Landsat images required. Our results indicated that average cropland mapping accuracies for these three sites ranged from 81.17% to 87.59% (Egypt), 54.43% to 79.72% (Ethiopia), and 28.11% to 59.35% (South Africa) using different numbers of images within a year. The overall cropland accuracies were improved with an increase in available Landsat images within a year and reached a relatively stable stage when more than five images were acquired in all three sites. Growing season images played a key role in identifying cropland, accounting for a 13.22% average accuracy improvement compared with non-growing season images. Therefore, at least five images are recommended from a computational efficiency perspective, although fewer images, as low as two growing season images, can also achieve good results in specific regions.