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Journal articles on the topic 'Sub-Saharan Remote sensing'
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Marshall, M., K. Tu, C. Funk, J. Michaelsen, P. Williams, C. Williams, J. Ardö, et al. "Combining surface reanalysis and remote sensing data for monitoring evapotranspiration." Hydrology and Earth System Sciences Discussions 9, no. 2 (February 2, 2012): 1547–87. http://dx.doi.org/10.5194/hessd-9-1547-2012.
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Abstract. Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.
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Sibanda, Mbulisi, Timothy Dube, Khoboso Seutloali, and Samuel Adelabu. "Operational applications of remote sensing in groundwater mapping across sub-Saharan Africa." Transactions of the Royal Society of South Africa 70, no. 2 (March 18, 2015): 173–79. http://dx.doi.org/10.1080/0035919x.2015.1017024.
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Marshall, M., K. Tu, C. Funk, J. Michaelsen, P. Williams, C. Williams, J. Ardö, et al. "Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach." Hydrology and Earth System Sciences 17, no. 3 (March 12, 2013): 1079–91. http://dx.doi.org/10.5194/hess-17-1079-2013.
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Abstract. Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.
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Ebhuoma, Osadolor, and Michael Gebreslasie. "Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa." International Journal of Environmental Research and Public Health 13, no. 6 (June 14, 2016): 584. http://dx.doi.org/10.3390/ijerph13060584.
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Dube, T., O. Mutanga, K. Seutloali, S. Adelabu, and C. Shoko. "Water quality monitoring in sub-Saharan African lakes: a review of remote sensing applications." African Journal of Aquatic Science 40, no. 1 (January 2, 2015): 1–7. http://dx.doi.org/10.2989/16085914.2015.1014994.
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Yiran, Gerald Albert Baeribameng, Austin Dziwornu Ablo, Freda Elikplim Asem, and George Owusu. "Urban Sprawl in sub-Saharan Africa: A review of the literature in selected countries." Ghana Journal of Geography 12, no. 1 (July 25, 2020): 1–28. http://dx.doi.org/10.4314/gjg.v12i1.1.
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Urban sprawl has gained popularity in academic discourse in recent times, but the majority of the research was conducted in developed countries. There is a marginal body of works on the character and nature of urban sprawl in Sub-Saharan Africa (SSA), although the region isexperiencing one of the fastest rates of sprawl. Urbanisation in SSA is very rapid, and in addition to the emerging challenges of globalisation, climate change and poverty, SSA cities have an enormous task to manage urban sprawl. This paper reviews the literature on urban sprawl in SSAto identify research gaps and propose a research agenda. Published articles from five Anglophone countries in three of the four regional blocks in SSA were selected. The literature was organised into the causes and effects of urban sprawl and showed that the previous research on the subjectfocused mainly on its environmental impacts. Few studies have looked at the effects of sprawl on rural livelihoods, agriculture and food security considering the challenges of global climate change and poverty. Other studies have used Remote Sensing and Geographic InformationSystems, but these were conducted largely for change detection. The paper recommends the deployment of a more comprehensive methodology incorporating remote sensing/GIS with ethnographic methods to capture better the complexity and impacts of urban sprawl in SSA.Additionally, further research attention must be paid to the effects of urban sprawl on rural livelihoods and overall sprawl-induced agrarian change.
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Bhaga, Trisha Deevia, Timothy Dube, Munyaradzi Davis Shekede, and Cletah Shoko. "Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review." Remote Sensing 12, no. 24 (December 21, 2020): 4184. http://dx.doi.org/10.3390/rs12244184.
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Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.
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Djurfeldt, Göran, Ola Hall, Magnus Jirström, Maria Archila Bustos, Björn Holmquist, and Sultana Nasrin. "Using panel survey and remote sensing data to explain yield gaps for maize in sub-Saharan Africa." Journal of Land Use Science 13, no. 3 (May 4, 2018): 344–57. http://dx.doi.org/10.1080/1747423x.2018.1511763.
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Jacob, Benjamin G., Robert J. Novak, Laurent D. Toe, Moussa Sanfo, Daniel A. Griffith, Thomson L. Lakwo, Peace Habomugisha, Moses N. Katabarwa, and Thomas R. Unnasch. "Validation of a Remote Sensing Model to Identify Simulium damnosum s.l. Breeding Sites in Sub-Saharan Africa." PLoS Neglected Tropical Diseases 7, no. 7 (July 25, 2013): e2342. http://dx.doi.org/10.1371/journal.pntd.0002342.
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Atai, Godwin, Ayansina Ayanlade, Isaac Ayo Oluwatimilehin, and Oluwatoyin Seun Ayanlade. "Geospatial Distribution and Projection of Aerosol over Sub-Saharan Africa: Assessment from Remote Sensing and Other Platforms." Aerosol Science and Engineering 5, no. 3 (June 15, 2021): 357–72. http://dx.doi.org/10.1007/s41810-021-00107-4.
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Hemerijckx, Lisa-Marie, Sam Van Emelen, Joachim Rymenants, Jac Davis, Peter H. Verburg, Shuaib Lwasa, and Anton Van Rompaey. "Upscaling Household Survey Data Using Remote Sensing to Map Socioeconomic Groups in Kampala, Uganda." Remote Sensing 12, no. 20 (October 21, 2020): 3468. http://dx.doi.org/10.3390/rs12203468.
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Sub-Saharan African cities are expanding horizontally, demonstrating spatial patterns of urban sprawl and socioeconomic segregation. An important research gap around the geographies of urban populations is that city-wide analyses mask local socioeconomic inequalities. This research focuses on those inequalities by identifying the spatial settlement patterns of socioeconomic groups within the Greater Kampala Metropolitan Area (Uganda). Findings are based on a novel dataset, an extensive household survey with 541 households, conducted in Kampala in 2019. To identify different socioeconomic groups, a k-prototypes clustering method was applied to the survey data. A maximum likelihood classification method was applied on a recent Landsat-8 image of the city and compared to the socioeconomic clustering through a fuzzy error matrix. The resulting maps show how different socioeconomic clusters are located around the city. We propose a simple method to upscale household survey responses to a larger study area, to use these data as a base map for further analysis or urban planning purposes. Obtaining a better understanding of the spatial variability in socioeconomic dynamics can aid urban policy-makers to target their decision-making processes towards a more favorable and sustainable future.
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Mulley, Maggie, Lammert Kooistra, and Laurens Bierens. "High-Resolution Multisensor Remote Sensing to Support Date Palm Farm Management." Agriculture 9, no. 2 (January 31, 2019): 26. http://dx.doi.org/10.3390/agriculture9020026.
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Date palms are a valuable crop in areas with limited water availability such as the Middle East and sub-Saharan Africa, due to their hardiness in tough conditions. Increasing soil salinity and the spread of pests including the red palm weevil (RPW) are two examples of growing threats to date palm plantations. Separate studies have shown that thermal, multispectral, and hyperspectral remote sensing imagery can provide insight into the health of date palm plantations, but the added value of combining these datasets has not been investigated. The current study used available thermal, hyperspectral, Light Detection and Ranging (LiDAR) and visual Red-Green-Blue (RGB) images to investigate the possibilities of assessing date palm health at two “levels”; block level and individual tree level. Test blocks were defined into assumed healthy and unhealthy classes, and thermal and height data were extracted and compared. Due to distortions in the hyperspectral imagery, this data was only used for individual tree analysis; methods for identifying individual tree points using Normalized Difference Vegetation Index (NDVI) maps proved accurate. A total of 100 random test trees in one block were selected, and comparisons between hyperspectral, thermal and height data were made. For the vegetation index red-edge position (REP), the R-squared value in correlation with temperature was 0.313 and with height was 0.253. The vegetation index—the Vogelmann Red Edge Index (VOGI)—also has a relatively strong correlation value with both temperature (R2 = 0.227) and height (R2 = 0.213). Despite limited field data, the results of this study suggest that remote sensing data has added value in analyzing date palm plantations and could provide insight for precision agriculture techniques.
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Müller, Marc F., Gopal Penny, Meredith T. Niles, Vincent Ricciardi, Davide Danilo Chiarelli, Kyle Frankel Davis, Jampel Dell’Angelo, et al. "Impact of transnational land acquisitions on local food security and dietary diversity." Proceedings of the National Academy of Sciences 118, no. 4 (January 19, 2021): e2020535118. http://dx.doi.org/10.1073/pnas.2020535118.
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Foreign investors have acquired approximately 90 million hectares of land for agriculture over the past two decades. The effects of these investments on local food security remain unknown. While additional cropland and intensified agriculture could potentially increase crop production, preferential targeting of prime agricultural land and transitions toward export-bound crops might affect local access to nutritious foods. We test these hypotheses in a global systematic analysis of the food security implications of existing land concessions. We combine agricultural, remote sensing, and household survey data (available in 11 sub-Saharan African countries) with georeferenced information on 160 land acquisitions in 39 countries. We find that the intended changes in cultivated crop types generally imply transitions toward energy-rich, but nutrient-poor, crops that are predominantly destined for export markets. Specific impacts on food production and access vary substantially across regions. Deals likely have little effect on food security in eastern Europe and Latin America, where they predominantly occur within agricultural areas with current export-oriented crops, and where agriculture would have both expanded and intensified regardless of the land deals. This contrasts with Asia and sub-Saharan Africa, where deals are associated with both an expansion and intensification (in Asia) of crop production. Deals in these regions also shift production away from local staples and coincide with a gradually decreasing dietary diversity among the surveyed households in sub-Saharan Africa. Together, these findings point to a paradox, where land deals can simultaneously increase crop production and threaten local food security.
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Hall, Ola, Sigrun Dahlin, Håkan Marstorp, Maria Archila Bustos, Ingrid Öborn, and Magnus Jirström. "Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery." Drones 2, no. 3 (June 22, 2018): 22. http://dx.doi.org/10.3390/drones2030022.
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Yield estimates and yield gap analysis are important for identifying poor agricultural productivity. Remote sensing holds great promise for measuring yield and thus determining yield gaps. Farming systems in sub-Saharan Africa (SSA) are commonly characterized by small field size, intercropping, different crop species with similar phenologies, and sometimes high cloud frequency during the growing season, all of which pose real challenges to remote sensing. Here, an unmanned aerial vehicle (UAV) system based on a quadcopter equipped with two consumer-grade cameras was used for the delineation and classification of maize plants on smallholder farms in Ghana. Object-oriented image classification methods were applied to the imagery, combined with measures of image texture and intensity, hue, and saturation (IHS), in order to achieve delineation. It was found that the inclusion of a near-infrared (NIR) channel and red–green–blue (RGB) spectra, in combination with texture or IHS, increased the classification accuracy for both single and mosaic images to above 94%. Thus, the system proved suitable for delineating and classifying maize using RGB and NIR imagery and calculating the vegetation fraction, an important parameter in producing yield estimates for heterogeneous smallholder farming systems.
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Forget, Yann, Michal Shimoni, Marius Gilbert, and Catherine Linard. "Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa." Remote Sensing 13, no. 3 (February 2, 2021): 525. http://dx.doi.org/10.3390/rs13030525.
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By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.
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Mapfumo, L., V. Muchenje, J. F. Mupangwa, M. M. Scholtz, and S. Washaya. "Dynamics and influence of environmental components on greenhouse gas emissions in sub-Saharan African rangelands: a review." Animal Production Science 61, no. 8 (2021): 721. http://dx.doi.org/10.1071/an20564.
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Sub-Saharan African (SSA) countries consist of ~200 million livestock owners who utilise marginal rangelands as a feed resource base for their animals. These rangelands offer various resources to the communities and are in-turn vulnerable to climate change related challenges. Currently, information on greenhouse gases (GHG) emission from SSA rangelands is heavily dependent on the generic values generated by the Intergovernmental Panel on Climate Change (IPCC) Tier I trajectories on various aspects of the environment. There is, therefore, a need to identify research gaps between the dynamics and influences of environmental components, highlight their magnitude and potential aggregate contribution towards GHG emission in an SSA context. Rangeland sustainability, weather patterns, soils, plant biodiversity, and current methods used to measure GHG emission from rangelands are influenced by institutional, community, and national policy frameworks. Various intertwined environmental components exist within the SSA rangeland ecosystems and research has not extensively covered such interactions. It is crucial to generate a database that includes information of in-situ trajectories on GHG emission from soil properties, vegetation image maps using remote sensing and geographic information system, plant biodiversity indices, climatology, and animal husbandry aspects.
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Haas, E. M., E. Bartholomé, and B. Combal. "Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa." Journal of Hydrology 370, no. 1-4 (May 2009): 52–63. http://dx.doi.org/10.1016/j.jhydrol.2009.02.052.
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BUCHANAN, GRAEME M., PAUL F. DONALD, LINCOLN D. C. FISHPOOL, JULIUS A. ARINAITWE, MARK BALMAN, and PHILIPPE MAYAUX. "An assessment of land cover and threats in Important Bird Areas in Africa." Bird Conservation International 19, no. 1 (March 2009): 49–61. http://dx.doi.org/10.1017/s0959270908007697.
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SummaryOver 1,200 Important Bird Areas (IBAs) have been identified in Africa, each meeting at least one of four objective criteria that identify it as an area of high conservation importance for birds. Despite their biodiversity value, many IBAs are threatened by habitat degradation and a high proportion lack legal protection. We integrate an inventory of these IBAs with remote sensing data to identify patterns that could be used to assess priorities for monitoring and conservation. Land cover composition in IBAs differed significantly from that in buffer zones of the same area immediately surrounding them and was significantly more homogeneous. Agriculture and deforestation were the most prevalent threats to IBAs, particularly in IBAs containing a high proportion of dense forest or shrub. Human population density within IBAs was no lower than that immediately outside IBAs, and was around three times higher than the average for sub-Saharan Africa. However, projected human population growth was lower than the average for sub-Saharan Africa, with the projected increase greatest in IBAs with a high proportional cover of dense forest and mosaic woodland and lowest in IBAs with a higher grassland component. Fifty seven percent of IBAs fell within or overlapped Protected Areas, though this percentage differed between different categories of IBA. IBAs that were included within Protected Areas supported a greater number of globally threatened bird species and contained proportionally more dense forest, woodland and shrub than IBAs falling outside Protected Areas. IBAs outside Protected Areas contained a high proportion of mosaic woodland and open water, suggesting that such habitats are under-protected in Africa. We suggest that because the most prevalent threats to IBAs involve changes in land cover that could be detected from satellites, remote sensing could play an important role in the monitoring of African IBAs. This would permit monitoring of a wider range of sites than is possible solely by conventional, ground-based approaches.
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Ruiz-Pérez, Guiomar, Julian Koch, Salvatore Manfreda, Kelly Caylor, and Félix Francés. "Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI." Hydrology and Earth System Sciences 21, no. 12 (December 8, 2017): 6235–51. http://dx.doi.org/10.5194/hess-21-6235-2017.
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Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.
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Wahab, Ibrahim, Ola Hall, and Magnus Jirström. "Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa." Drones 2, no. 3 (August 16, 2018): 28. http://dx.doi.org/10.3390/drones2030028.
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The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.
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Seutloali, Khoboso E., Timothy Dube, and Mbulisi Sibanda. "Developments in the remote sensing of soil erosion in the perspective of sub-Saharan Africa. Implications on future food security and biodiversity." Remote Sensing Applications: Society and Environment 9 (January 2018): 100–106. http://dx.doi.org/10.1016/j.rsase.2017.12.002.
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Sedano, F., S. N. Lisboa, R. Sahajpal, L. Duncanson, N. Ribeiro, A. Sitoe, G. Hurtt, and C. J. Tucker. "The connection between forest degradation and urban energy demand in sub-Saharan Africa: a characterization based on high-resolution remote sensing data." Environmental Research Letters 16, no. 6 (May 21, 2021): 064020. http://dx.doi.org/10.1088/1748-9326/abfc05.
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Wahab, Ibrahim. "In-season plot area loss and implications for yield estimation in smallholder rainfed farming systems at the village level in Sub-Saharan Africa." GeoJournal 85, no. 6 (June 28, 2019): 1553–72. http://dx.doi.org/10.1007/s10708-019-10039-9.
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Abstract The shortfalls in the quality, quantity, and reliability of agriculture performance data are neither new nor confined to Sub-Saharan Africa (SSA). It is, however, a more dire challenge given the overwhelming importance of agriculture in the economies of most countries in the region in terms of food security and poverty reduction. While farmers’ self-reported (SR) data on crop outputs and farm sizes remain popular variables for computing plot productivity and yields, especially in SSA, other methods such GPS measurement and remote sensing measurement of crop area, crop cuts (CC) as well as whole plot harvests have been touted as the gold standard methods for yield measurement. All these approaches to yield estimation are insufficient in capturing real agriculture productivity in rainfed farming systems due to the significant area loss that characterizes these farming systems in the course of each cropping season. This paper compares yield data of smallholder maize plots from two farming communities in the Eastern Region of Ghana based on farmer self-reported outputs and crop cuts, as well as GPS and aerial imagery measurement of plot area. The study finds a high level of agreement between GPS-measured plot area and that measured using remote sensing methods (R2 = 0.80) with the minor deviations between the two measures attributable to changes in farmers’ plans in the course of the season with regards to their cultivation extent. More interestingly, the study finds a substantial disparity between measured CC yields and SR yields; 2174 kg/ha for CC yields compared to 651 kg/ha for SR yields. The significant disparity between the two measures of yield is partly attributable to the significant intra-plot variability in crop performance leading to plot area loss in the course of the season. This area loss (ranging from 15 to 30% of the planted area) is usually not taken into account in current yield measurement approaches. Delineating the productive and planted-but-unproductive sections of plots has important implications not only for yield estimation methodologies but also for shedding more light on the factors underlying current poor yields and pathways to improving productivity on smallholder rainfed maize farms.
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Forget, Yann, Catherine Linard, and Marius Gilbert. "Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap." Remote Sensing 10, no. 7 (July 20, 2018): 1145. http://dx.doi.org/10.3390/rs10071145.
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The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database.
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Bhargava, Anil K., Tor Vagen, and Anja Gassner. "Breaking Ground: Unearthing the Potential of High-resolution, Remote-sensing Soil Data in Understanding Agricultural Profits and Technology Use in Sub-Saharan Africa." World Development 105 (May 2018): 352–66. http://dx.doi.org/10.1016/j.worlddev.2017.07.015.
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Wrable, M., A. Liss, A. Kulinkina, M. Koch, N. K. Biritwum, A. Ofosu, K. C. Kosinski, D. M. Gute, and E. N. Naumova. "LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 215–21. http://dx.doi.org/10.5194/isprsarchives-xli-b8-215-2016.
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90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R<sup>2</sup> as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.
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Wrable, M., A. Liss, A. Kulinkina, M. Koch, N. K. Biritwum, A. Ofosu, K. C. Kosinski, D. M. Gute, and E. N. Naumova. "LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 215–21. http://dx.doi.org/10.5194/isprs-archives-xli-b8-215-2016.
Full textAbstract:
90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R<sup>2</sup> as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.
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Gxokwe, Siyamthanda, Timothy Dube, and Dominic Mazvimavi. "Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions." Remote Sensing 12, no. 24 (December 21, 2020): 4190. http://dx.doi.org/10.3390/rs12244190.
Full textAbstract:
Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.
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Gedefaw, Abebaw, Clement Atzberger, Thomas Bauer, Sayeh Agegnehu, and Reinfried Mansberger. "Analysis of Land Cover Change Detection in Gozamin District, Ethiopia: From Remote Sensing and DPSIR Perspectives." Sustainability 12, no. 11 (June 2, 2020): 4534. http://dx.doi.org/10.3390/su12114534.
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Land cover patterns in sub-Saharan Africa are rapidly changing. This study aims to quantify the land cover change and to identify its major determinants by using the Drivers, Pressures, State, Impact, Responses (DPSIR) framework in the Ethiopian Gozamin District over a period of 32 years (1986 to 2018). Satellite images of Landsat 5 (1986), Landsat 7 (2003), and Sentinel-2 (2018) and a supervised image classification methodology were used to assess the dynamics of land cover change. Land cover maps of the three dates, focus group discussions (FGDs), interviews, and farmers’ lived experiences through a household survey were applied to identify the factors for changes based on the DPSIR framework. Results of the investigations revealed that during the last three decades the study area has undergone an extensive land cover change, primarily a shift from cropland and grassland into forests and built-up areas. Thus, quantitative land cover change detection between 1986 and 2018 revealed that cropland, grassland, and bare areas declined by 10.53%, 5.7%, and 2.49%. Forest, built-up, shrub/scattered vegetation, and water bodies expanded by 13.47%, 4.02%, 0.98%, and 0.25%. Household surveys and focus group discussions (FGDs) identified the population growth, the rural land tenure system, the overuse of land, the climate change, and the scarcity of grazing land as drivers of these land cover changes. Major impacts were rural to urban migration, population size change, scarcity of land, and decline in land productivity. The outputs from this study could be used to assure sustainability in resource utilization, proper land use planning, and proper decision-making by the concerned government authorities.
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Nooni, Isaac Kwesi, Daniel Fiifi T. Hagan, Guojie Wang, Waheed Ullah, Shijie Li, Jiao Lu, Asher Samuel Bhatti, et al. "Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa." Remote Sensing 13, no. 3 (February 2, 2021): 533. http://dx.doi.org/10.3390/rs13030533.
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Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.
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Enoguanbhor, Evidence Chinedu, Florian Gollnow, Blake Byron Walker, Jonas Ostergaard Nielsen, and Tobia Lakes. "Key Challenges for Land Use Planning and Its Environmental Assessments in the Abuja City-Region, Nigeria." Land 10, no. 5 (April 21, 2021): 443. http://dx.doi.org/10.3390/land10050443.
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Land use planning as strategic instruments to guide urban dynamics faces particular challenges in the Global South, including Sub-Saharan Africa, where urgent interventions are required to improve urban and environmental sustainability. This study investigated and identified key challenges of land use planning and its environmental assessments to improve the urban and environmental sustainability of city-regions. In doing so, we combined expert interviews and questionnaires with spatial analyses of urban and regional land use plans, as well as current and future urban land cover maps derived from Geographic Information Systems and remote sensing. By overlaying and contrasting land use plans and land cover maps, we investigated spatial inconsistencies between urban and regional plans and the associated urban land dynamics and used expert surveys to identify the causes of such inconsistencies. We furthermore identified and interrogated key challenges facing land use planning, including its environmental assessment procedures, and explored means for overcoming these barriers to rapid, yet environmentally sound urban growth. The results illuminated multiple inconsistencies (e.g., spatial conflicts) between urban and regional plans, most prominently stemming from conflicts in administrative boundaries and a lack of interdepartmental coordination. Key findings identified a lack of Strategic Environmental Assessment and inadequate implementation of land use plans caused by e.g., insufficient funding, lack of political will, political interference, corruption as challenges facing land use planning strategies for urban and environmental sustainability. The baseline information provided in this study is crucial to improve strategic planning and urban/environmental sustainability of city-regions in Sub-Saharan Africa and across the Global South, where land use planning faces similar challenges to address haphazard urban expansion patterns.
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Kumar, Sanath, Niall Hanan, Lara Prihodko, Julius Anchang, C. Ross, Wenjie Ji, and Brianna Lind. "Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data." Remote Sensing 11, no. 7 (April 4, 2019): 815. http://dx.doi.org/10.3390/rs11070815.
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Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest.
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Andersson Djurfeldt, Agnes, Ola Hall, Aida Isinika, Elibariki Msuya, and Genesis Tambang Yengoh. "Sustainable Agricultural Intensification in Four Tanzanian Villages—A View from the Ground and the Sky." Sustainability 12, no. 20 (October 9, 2020): 8304. http://dx.doi.org/10.3390/su12208304.
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Agricultural intensification based on smallholders is among many economists viewed as a necessary developmental path to ensure food security and poverty reduction in sub-Saharan Africa. Increasingly, a one-sided focus on raising productivity in cereals has been questioned on environmental grounds, with the concept of sustainable agricultural intensification (SAI) emerging from the natural sciences as a way of advancing environmental and social needs simultaneously. SAI approaches have, however, been criticized for being both conceptually and methodologically vague. This study combines socioeconomic survey data with remotely sensed land productivity data and qualitative data from four villages in Tanzania. By triangulating and comparing data collected through ground level surveys and ground-truthing with remote sensing data, we find that this combination of methods is capable of resolving some of the theoretical and methodological vagueness found in SAI approaches. The results show the problems of relying on only one type of data when studying sustainable agricultural intensification and indicate the poor environmental outcomes of cereal monocropping, even when social outcomes may be forthcoming. We identify land use practices that can be considered both socially and environmentally sustainable. Theoretically, we contribute to a further problematization of the SAI concept.
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Ceccato, Pietro, Christelle Vancutsem, Robert Klaver, James Rowland, and Stephen J. Connor. "A Vectorial Capacity Product to Monitor Changing Malaria Transmission Potential in Epidemic Regions of Africa." Journal of Tropical Medicine 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/595948.
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Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmitPlasmodiumspp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.
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Simwanda, Matamyo, Yuji Murayama, Darius Phiri, Vincent R. Nyirenda, and Manjula Ranagalage. "Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia." Remote Sensing 13, no. 5 (March 3, 2021): 942. http://dx.doi.org/10.3390/rs13050942.
Full textAbstract:
Forecasting scenarios of future intra-urban land-use (intra-urban-LU) expansion can help to curb the historically unplanned urbanization in cities in sub-Saharan Africa (SSA) and promote urban sustainability. In this study, we applied the neural network–Markov model to simulate scenarios of future intra-urban-LU expansion in Lusaka city, Zambia. Data derived from remote sensing (RS) and geographic information system (GIS) techniques including urban-LU maps (from 2000, 2005, 2010, and 2015) and selected driver variables, were used to calibrate and validate the model. We then simulated urban-LU expansion for three scenarios (business as usual/status quo, environmental conservation and protection, and strategic urban planning) to explore alternatives for attaining urban sustainability by 2030. The results revealed that Lusaka had experienced rapid urban expansion dominated by informal settlements. Scenario analysis results suggest that a business-as-usual setup is perilous, as it signals an escalating problem of unplanned settlements. The environmental conservation and protection scenario is insufficient, as most of the green spaces and forests have been depleted. The strategic urban planning scenario has the potential for attaining urban sustainability, as it predicts sufficient control of unplanned settlement expansion and protection of green spaces and forests. The study proffers guidance for strategic policy directions and creating a planning vision.
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Mashame, Gofamodimo, and Felicia Akinyemi. "TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-8 (June 7, 2016): 137–44. http://dx.doi.org/10.5194/isprsannals-iii-8-137-2016.
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Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.
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Mashame, Gofamodimo, and Felicia Akinyemi. "TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-8 (June 7, 2016): 137–44. http://dx.doi.org/10.5194/isprs-annals-iii-8-137-2016.
Full textAbstract:
Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.
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Andela, Niels, Guido R. van der Werf, Johannes W. Kaiser, Thijs T. van Leeuwen, Martin J. Wooster, and Caroline E. R. Lehmann. "Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite." Biogeosciences 13, no. 12 (June 28, 2016): 3717–34. http://dx.doi.org/10.5194/bg-13-3717-2016.
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Abstract. Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, Sub-Saharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned-area estimates. The fuel consumption estimates based on the geostationary and polar-orbiting instruments showed good agreement in terms of spatial patterns. We used field measurements of fuel consumption to constrain our results, but the large variation in fuel consumption in both space and time complicated this comparison and absolute fuel consumption estimates remained more uncertain. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub-Saharan Africa. Strikingly, we found the infrequently burning interior of Australia to have higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, by affecting both fuel build-up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build-up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa or differences in landscape management. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned-area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite-derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emission estimates.
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Ngie, A., S. Tesfamichael, and F. Ahmed. "MONITORING THE IMPACTS OF EL NIÑO ON THE EXTENT OF CULTIVATED FIELDS USING SAR DATA AROUND THE AGRICULTURAL REGION OF THE FREE STATE, SOUTH AFRICA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 151–55. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-151-2017.
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There is continuous challenge in crop monitoring from erratic climatic phenomena such as irregular rainfall episodes during required farming seasons or cloud cover. Remote sensing has offered vital support in the monitoring of such scenarios and informs relevant authorities for better decision making. While optical sensors measure the greenness of vegetation to enable monitoring of its status, their usage is constrained by the continuous cloud cover during crop growth seasons in sub Saharan Africa. Synthetic aperture radar data (SAR) are on the other hand capable of penetrating clouds and are sensitive to the structure and moisture content of target features, thereby providing complementary information for monitoring crop cultivated fields. This study sought to evaluate the sensitivity of Sentinel-1 SAR data to the status of cultivated crop fields that experienced varying rainfall amounts between 2015/2016 and 2016/2017 growing seasons as a result of El Niño induced drought in 2015. Dual polarization composites per season were classified and through sample farms delineated from Google Earth image, backscatter values were extracted for statistical comparisons. The two sample t-test was applied to test significance of the differences between the two seasons at the level of farm status. Results showed an overall significant difference (p-value of 0.003&thinsp;<&thinsp;0.005) in SAR backscatter sensitivity to cultivated crop fields during and after the El Niño phenomenon. While these results are encouraging for areas that experience clouds during growing seasons, further improvements can be expected by factoring in other variables such as topographic and moisture conditions of farms.
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Karst, Isabel G., Isabel Mank, Issouf Traoré, Raissa Sorgho, Kim-Jana Stückemann, Séraphin Simboro, Ali Sié, Jonas Franke, and Rainer Sauerborn. "Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso." Remote Sensing 12, no. 11 (May 27, 2020): 1717. http://dx.doi.org/10.3390/rs12111717.
Full textAbstract:
Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R² between 0.4 and 0.54 (adj. R² between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change.
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Luetkemeier, Robert, and Stefan Liehr. "Household Drought Risk Index (HDRI): Social-Ecological Assessment of Drought Risk in the Cuvelai-Basin." Journal of Natural Resources and Development 8 (July 19, 2018): 46–68. http://dx.doi.org/10.5027/jnrd.v8i0.06.
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Droughts threaten many regions worldwide, in particular semi-arid environments of sub-Saharan Africa such as the Cuvelai-Basin in Angola and Namibia, as the population depends on critical water-related ecosystem services. Since droughts are multi-layered phenomena, risk assessment tools that capture the societal relations to nature and identify those individuals that are most threatened are required. This study presents the integrated Household Drought Risk Index (HDRI) that builds upon empirical data from the study area to provide insights into drought hazard and vulnerability conditions of households in different socio-economic and environmental settings. The composite indicator integrates environmental measures of drought (frequency, severity, duration) from multiple remote sensing products (precipitation, soil moisture, vegetation) and the vulnerability of households (sensitivity, coping capacity) obtained from a structured survey that comprised 461 households. The results reveal that the Angolan population shows higher levels of risk, particularly caused by less developed infrastructural systems, weaker institutional capabilities and less coping capacities. Overall, urban dwellers follow less drought-sensitive livelihood strategies, but are still connected to drought conditions in rural areas due to family relations with obligations and benefits. The study results provide knowledge for decision-makers to respond to drought in the short and long-term. The latter may build upon the extension of centralized and decentralized water and food supply/production systems as well as the support of households via targeted educational and community-building measures. Specific HDRI components may be included in census surveys to receive continuous drought risk data.
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Van Passel, Johanna, Wanda De Keersmaecker, and Ben Somers. "Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using Sentinel-2 Satellite Imagery." Remote Sensing 12, no. 8 (April 17, 2020): 1276. http://dx.doi.org/10.3390/rs12081276.
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Dry forests in Sub-Saharan Africa are of critical importance for the livelihood of the local population given their strong dependence on forest products. Yet these forests are threatened due to rapid population growth and predicted changes in rainfall patterns. As such, large-scale woody cover monitoring of tropical dry forests is urgently required. Although promising, remote sensing-based estimation of woody cover in tropical dry forest ecosystems is challenging due to the heterogeneous woody and herbaceous vegetation structure and the large intra-annual variability in the vegetation due to the seasonal rainfall. To test the capability of Sentinel-2 satellite imagery for producing accurate woody cover estimations, two contrasting study sites in Ethiopia and Tanzania were used. The estimation accuracy of a linear regression model using the Normalised Difference Vegetation Index (NDVI), a Partial Least Squares Regression (PLSR), and a Random Forest regression model using both single-date and multi-temporal Sentinel-2 images were compared. Additionally, the robustness and site transferability of these methods were tested. Overall, the multi-temporal PLSR model achieved the most accurate and transferable estimations (R2 = 0.70, RMSE = 4.12%). This model was then used to monitor the potential increase in woody coverage within several reforestation projects in the Degua Tembien district. In six of these projects, a significant increase in woody cover could be measured since the start of the project, which could be linked to their initial vegetation, location and shape. It can be concluded that a PLSR model combined with Sentinel-2 satellite imagery is capable of monitoring woody cover in these tropical dry forest regions, which can be used in support of reforestation efforts.
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Marshall, Michael, Michael Norton-Griffiths, Harvey Herr, Richard Lamprey, Justin Sheffield, Tor Vagen, and Joseph Okotto-Okotto. "Continuous and consistent land use/cover change estimates using socio-ecological data." Earth System Dynamics 8, no. 1 (February 8, 2017): 55–73. http://dx.doi.org/10.5194/esd-8-55-2017.
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Abstract. A growing body of research shows the importance of land use/cover change (LULCC) on modifying the Earth system. Land surface models are used to stimulate land–atmosphere dynamics at the macroscale, but model bias and uncertainty remain that need to be addressed before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modeling exercises. The method is driven by projectable socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa and yielded continuous (annual) estimates of LULCC at 5 km × 5 km spatial resolution. The method was developed with 2252 sample area frames of 5 km × 5 km consisting of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction, and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest-ranked predictors (p ≤ 10) to estimate LULCC. The predictors explained 62 and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest-ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macroscale LULCC detection, and outperformed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983 to 2012, with the largest increases (declines) occurring in densely populated high agricultural production zones. Future work should address the improvement (development) of existing (new) geospatial predictors and issues of model scalability and transferability.
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Robert, Elodie, Manuela Grippa, Dayangnéwendé Edwige Nikiema, Laurent Kergoat, Hamidou Koudougou, Yves Auda, and Emma Rochelle-Newall. "Environmental determinants of E. coli, link with the diarrheal diseases, and indication of vulnerability criteria in tropical West Africa (Kapore, Burkina Faso)." PLOS Neglected Tropical Diseases 15, no. 8 (August 17, 2021): e0009634. http://dx.doi.org/10.1371/journal.pntd.0009634.
Full textAbstract:
In 2017, diarrheal diseases were responsible for 606 024 deaths in Sub-Saharan Africa. This situation is due to domestic and recreational use of polluted surface waters, deficits in hygiene, access to healthcare and drinking water, and to weak environmental and health monitoring infrastructures. Escherichia coli (E. coli) is an indicator for the enteric pathogens that cause many diarrheal diseases. The links between E. coli, diarrheal diseases and environmental parameters have not received much attention in West Africa, and few studies have assessed health risks by taking into account hazards and socio-health vulnerabilities. This case study, carried out in Burkina Faso (Bagre Reservoir), aims at filling this knowledge gap by analyzing the environmental variables that play a role in the dynamics of E. coli, cases of diarrhea, and by identifying initial vulnerability criteria. A particular focus is given to satellite-derived parameters to assess whether remote sensing can provide a useful tool to assess the health hazard. Samples of surface water were routinely collected to measure E. coli, enterococci and suspended particulate matter (SPM) at a monitoring point (Kapore) during one year. In addition, satellite data were used to estimate precipitation, water level, Normalized Difference Vegetation Index (NDVI) and SPM. Monthly epidemiological data for cases of diarrhea from three health centers were also collected and compared with microbiological and environmental data. Finally, semi-structured interviews were carried out to document the use of water resources, contact with elements of the hydrographic network, health behavior and condition, and water and health policy and prevention, in order to identify the initial vulnerability criteria. A positive correlation between E. coli and enterococci in surface waters was found indicating that E. coli is an acceptable indicator of fecal contamination in this region. E. coli and diarrheal diseases were strongly correlated with monsoonal precipitation, in situ SPM, and Near Infra-Red (NIR) band between March and November. Partial least squares regression showed that E. coli concentration was strongly associated with precipitation, Sentinel-2 reflectance in the NIR and SPM, and that the cases of diarrhea were strongly associated with precipitation, NIR, E. coli, SPM, and to a lesser extent with NDVI. Moreover, E. coli dynamics were reproduced using satellite data alone, particularly from February to mid-December (R2 = 0.60) as were cases of diarrhea throughout the year (R2 = 0.76). This implies that satellite data could provide an important contribution to water quality monitoring. Finally, the vulnerability of the population was found to increase during the rainy season due to reduced accessibility to healthcare and drinking water sources and increased use of water of poor quality. During this period, surface water is used because it is close to habitations, easy to use and free from monetary or political constraints. This vulnerability is aggravated by marginality and particularly affects the Fulani, whose concessions are often close to surface water (river, lake) and far from health centers.
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Popp, C., P. Wang, D. Brunner, P. Stammes, Y. Zhou, and M. Grzegorski. "MERIS albedo climatology for FRESCO+ O<sub>2</sub> A-band cloud retrieval." Atmospheric Measurement Techniques Discussions 3, no. 5 (October 27, 2010): 4603–44. http://dx.doi.org/10.5194/amtd-3-4603-2010.
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Abstract. A new global albedo climatology for Oxygen A-band cloud retrievals is presented. The climatology is based on MEdium Resolution Imaging Spectrometer (MERIS) Albedomap data and its favourable impact on the derivation of cloud fraction is demonstrated for the FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm. To date, a relatively coarse resolution (1° × 1°) surface reflectance dataset from GOME (Global Ozone Monitoring Experiment) Lambert-equivalent reflectivity (LER) is used in FRESCO+. The GOME LER climatology does not account for the usually higher spatial resolution of UV/VIS instruments designed for trace gas remote sensing which introduces several artefacts, e.g. in regions with sharp spectral contrasts like coastlines or over bright surface targets. Therefore, MERIS black-sky albedo (BSA) data from the period October 2002 to October 2006 were aggregated to a grid of 0.25° × 0.25° for each month of the year and for different spectral channels. In contrary to other available surface reflectivity datasets, MERIS includes channels at 754 nm and 775 nm which are located close to the spectral windows required for O2 A-band cloud retrievals. The MERIS BSA in the near infrared compares well to Moderate Resolution Imaging Spectroradiometer (MODIS) derived BSA with an average difference lower than 1% and a correlation coefficient of 0.98. However, when relating MERIS BSA to GOME LER a distinctly lower correlation (0.80) and enhanced scatter is found. Effective cloud fractions from two exemplary months (January and July 2006) of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) data were subsequently derived with FRESCO+ and compared to those from the Heidelberg Iterative Cloud Retrieval Utilities (HICRU) algorithm. The MERIS climatology generally improves FRESCO+ effective cloud fractions. In particular small cloud fractions are in better agreement with HICRU. This is of importance for atmospheric trace gas retrieval which relies on accurate cloud information at small cloud fractions. In addition, overestimates along coastlines and underestimates in the Intertropical Convergence Zone introduced by the GOME LER were eliminated. While effective cloud fractions over the Saharan desert and the Arabian peninsula are successfully reduced in January, they are still too high in July relative to HICRU due to FRESCO+'s large sensitivity to albedo inaccuracies of highly reflecting targets and inappropriate aerosol information which hampers an accurate albedo retrieval. Apart from FRESCO+, the new MERIS albedo data base is applicable to any cloud retrieval algorithms using the O2 A-band or the O2-O2 absorption band around 477 nm. Moreover, the by-product of BSA at 442 nm can be used in NO2 remote sensing and the BSA at 620 nm, 665 nm, and 681 nm could be integrated in current H2O retrievals.
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Popp, C., P. Wang, D. Brunner, P. Stammes, Y. Zhou, and M. Grzegorski. "MERIS albedo climatology for FRESCO+ O<sub>2</sub> A-band cloud retrieval." Atmospheric Measurement Techniques 4, no. 3 (March 8, 2011): 463–83. http://dx.doi.org/10.5194/amt-4-463-2011.
Full textAbstract:
Abstract. A new global albedo climatology for Oxygen A-band cloud retrievals is presented. The climatology is based on MEdium Resolution Imaging Spectrometer (MERIS) Albedomap data and its favourable impact on the derivation of cloud fraction is demonstrated for the FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm. To date, a relatively coarse resolution (1° × 1°) surface reflectance dataset from GOME (Global Ozone Monitoring Experiment) Lambert-equivalent reflectivity (LER) is used in FRESCO+. The GOME LER climatology does not account for the usually higher spatial resolution of UV/VIS instruments designed for trace gas remote sensing which introduces several artefacts, e.g. in regions with sharp spectral contrasts like coastlines or over bright surface targets. Therefore, MERIS black-sky albedo (BSA) data from the period October 2002 to October 2006 were aggregated to a grid of 0.25° × 0.25° for each month of the year and for different spectral channels. In contrary to other available surface reflectivity datasets, MERIS includes channels at 754 nm and 775 nm which are located close to the spectral windows required for O2 A-band cloud retrievals. The MERIS BSA in the near-infrared compares well to Moderate Resolution Imaging Spectroradiometer (MODIS) derived BSA with an average difference lower than 1% and a correlation coefficient of 0.98. However, when relating MERIS BSA to GOME LER a distinctly lower correlation (0.80) and enhanced scatter is found. Effective cloud fractions from two exemplary months (January and July 2006) of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) data were subsequently derived with FRESCO+ and compared to those from the Heidelberg Iterative Cloud Retrieval Utilities (HICRU) algorithm. The MERIS climatology generally improves FRESCO+ effective cloud fractions. In particular small cloud fractions are in better agreement with HICRU. This is of importance for atmospheric trace gas retrieval which relies on accurate cloud information at small cloud fractions. In addition, overestimates along coastlines and underestimates in the Intertropical Convergence Zone introduced by the GOME LER were eliminated. While effective cloud fractions over the Saharan desert and the Arabian peninsula are successfully reduced in January, they are still too high in July relative to HICRU due to FRESCO+'s large sensitivity to albedo inaccuracies of highly reflecting targets and inappropriate aerosol information which hampers an accurate albedo retrieval. Finally, NO2 tropospheric vertical column densities and O3 total columns were derived with the FRESCO+ cloud parameters from the new dataset and it is found that the MERIS BSA climatology has a pronounced and beneficial effect on regional scale. Apart from FRESCO+, the new MERIS albedo dataset is applicable to any cloud retrieval algorithms using the O2 A-band or the O2-O2 absorption band around 477 nm. Moreover, the by-product of BSA at 442 nm can be used in NO2 remote sensing and the BSA at 620 nm, 665 nm, and 681 nm could be integrated in current H2O retrievals.
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Thamaga, Kgabo Humphrey, Timothy Dube, and Cletah Shoko. "Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa." Geocarto International, June 3, 2021, 1–23. http://dx.doi.org/10.1080/10106049.2021.1926552.
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Krüger, Kerstin, and Jacquie E. van der Waals. "Potato virus Y and Potato leafroll virus management under climate change in sub-Saharan Africa." South African Journal of Science 116, no. 11/12 (November 26, 2020). http://dx.doi.org/10.17159/sajs.2020/8579.
Full textAbstract:
Potato has increased in importance as a staple food in sub-Saharan Africa, where its production is faced with a multitude of challenges, including plant disease development and spread under changing climatic conditions. The economically most important plant viruses affecting potatoes globally are Potato virus Y (PVY) and Potato leafroll virus (PLRV). Disease management relies mostly on the use of insecticides, cultural control and seed certification schemes. A major obstacle in many sub-Saharan Africa countries is the availability of disease-free quality seed potatoes. Establishment and implementation of quality control through specialised seed production systems and certification schemes is critical to improve seed potato quality and reduce PVY and PLRV sources. Seed could be further improved by breeding virus-resistant varieties adapted to different environmental conditions combined with management measures tailored for smallholder or commercial farmers to specific agricultural requirements. Innovative technologies – including more sensitive testing, remote sensing, machine learning and predictive models – provide new tools for the management of PVY and PLRV, but require support for adoption and implementation in sub-Saharan Africa.
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Walz, Yvonne, Martin Wegmann, Benjamin Leutner, Stefan Dech, Penelope Vounatsou, Eliézer K. N'Goran, Giovanna Raso, and Jürg Utzinger. "Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling." Geospatial Health 10, no. 2 (November 30, 2015). http://dx.doi.org/10.4081/gh.2015.398.
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Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in <em>Schistosoma</em> infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.
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Rushingabigwi, G., W. Kalisa, P. Nsengiyumva, F. Zimulinda, D. Mukanyiligira, and L. Sibomana. "Analysis of Effects of Selected Aerosol Particles to the Global Climate Change and Health using Remote Sensing data: The Focus on Africa." Rwanda Journal of Engineering, Science, Technology and Environment 3, no. 1 (July 10, 2020). http://dx.doi.org/10.4314/rjeste.v3i1.5s.
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The desert's dust and anthropogenic biomass burning's black carbon (BC) in the tropical regions are associated with many effects on climate and air quality. The dust and BC are the selected aerosols, which affect health by polluting the breathable air. This research discusses the effects of both the aerosols, especially while they interact with the clouds. The respective aerosol extinction optical thickness (AOT) extinction was analysed with the sensible heat from Turbulence. The research purposes to quantitatively study the remote sensing data for fine particulate matter, PM2.5, heterogeneously mixing both the dust and the pulverized black carbon's soot or ash, to analyse at which levels PM2.5 can endanger human health in the sub-Saharan region. The mainly analysed data had been assimilated from different remote sensing tools; the Goddard interactive online visualization and analysis infrastructure (GIOVANNI) was in the centre of data collection; GIS, the research data analysis software. In results, the rise and fall of the averaged sensible heat were associated with the rise and fall of averaged aerosol extinction AOT; the direct effects of the selected aerosols on the clouds are also presented. Regarding the health effects, PM2.5 quantities are throughout beyond the tolerably recommended quantity of 25μg/m3; thus, having referred to erstwhile research, inhabitants would consume food and drug supplements which contain vanillic acid during dusty seasons.
Keywords: Geographic Information System (GIS), remotely sensed data, spatio-temporal (data) analysis