Analysis of Sub-Saharan Surface Emission from the Scanning-High Resolution Infrared Sounder during SAFARI 2000

2001 ◽  
Author(s):  
D. H. DeSlover ◽  
S. Nasari ◽  
R. O. Knuteson ◽  
H. E. Revercomb
Keyword(s):  
High Resolution ◽  
Surface Emission ◽  
Sub Saharan

scholarly journals A Back Migration from Asia to Sub-Saharan Africa Is Supported by High-Resolution Analysis of Human Y-Chromosome Haplotypes

2002 ◽  
Vol 70 (5) ◽  
pp. 1197-1214 ◽  
Author(s):  
Fulvio Cruciani ◽  
Piero Santolamazza ◽  
Peidong Shen ◽  
Vincent Macaulay ◽  
Pedro Moral ◽  
...  
Keyword(s):  
High Resolution ◽  
Y Chromosome ◽  
Sub Saharan Africa ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Sub Saharan ◽  
Human Y Chromosome

scholarly journals Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Stefanos Georganos ◽  
Oscar Brousse ◽  
Sébastien Dujardin ◽  
Catherine Linard ◽  
Daniel Casey ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
High Resolution ◽  
Urban Population ◽  
Informal Settlements ◽  
Malaria Prevalence ◽  
Urban Malaria ◽  
Parasite Rate ◽  
Sub Saharan ◽  
Very High

Abstract Background The rapid and often uncontrolled rural–urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa’s population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. Methods Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2–10 years (PfPR2–10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2–10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. Results The results suggest that the spatial distribution of PfPR2–10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2–10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. Conclusions The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.

Processes, Products & Policy: Investigating how drought events are perceived differently across spatial scales

2020 ◽  
Author(s):  
Josie Baulch ◽  
Justin Sheffield ◽  
Jadu Dash
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Sub Saharan Africa ◽  
Scaling Analysis ◽  
Technological Advancement ◽  
High Resolution Data ◽  
New Era ◽  
Drought Assessment ◽  
Satellite Technology ◽  
Sub Saharan

<p>Traditionally, availability of consistent, high quality, high-resolution data for Sub-Saharan Africa (SSA) has been limited, with political barriers, poverty and slow technological advancement all contributing to this issue. Over the past 30 years, a rapid increase in the advancement of satellite technology has led to the new era of ‘big data’, which includes a number of high-resolution, global remote sensing datasets. With an overwhelming amount of data now being downloaded and processed, we need to be sure that the best products are being used, in the most appropriate way, to determine the onset and evolution of extreme hydrological events and to influence policy implementation. This study uses scaling analysis of a number of hydrological and agricultural variables to investigate how spatial resolution influences monitoring of drought events. By studying the 2016/17 drought in Kenya, and assessing the drought footprint at various resolutions, it is evident that the data and its scale largely influences the apparent drought signal. Across all the variables, coarser data showed a significantly reduced drought extent than finer data, with a number of regions appearing to not fall below the drought threshold, when in reality, that area was experiencing drought. The implications of these scale issues could be significant, as drought policies in Kenya are implemented on a county level basis. By understanding the importance of effective scaling between the decision-making scale (policy), the data used for drought assessment (products) and the impacts of drought on the ground (processes), updated drought management and mitigation techniques can be used, with potential to reduce vulnerability to future drought events.</p>

scholarly journals RCP 8.5 Ghana. High-end climate change impacts on crop production

2020 ◽  
Author(s):  
Sylvia Tramberend ◽  
Günther Fischer ◽  
Harrij van Velthuizen
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Crop Production ◽  
Agricultural Sector ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Development Planning ◽  
Sub Saharan Africa ◽  
Sub Saharan

<p>Climate change threatens vulnerable communities in sub-Saharan Africa who face significant challenges for adaptation. Agriculture provides the livelihood for the majority of population. High-resolution assessments of the effects of climate change on crop production are urgently needed for targeted adaptation planning. In Ghana, next to food needs, agriculture plays an important role on international cocoa markets. To this end, we develop and apply a National Agro-Ecological Zoning system (NAEZ Ghana) to analyze the impacts of high-end (RCP8.5) global warming on agricultural production potentials until the end of this century. NAEZ Ghana uses an ensemble of the CORDEX Africa Regional Climate Model, a regional soil map, to assess development trends of crop production potentials for 19 main crops. Results highlight differential impacts across the country. Especially due to the significant increase in the number of days exceeding high-temperature thresholds, rain-fed production of several food and export crops could be reduced significantly compared to the historical 30-year average (1981-2010). Plantain production, an important food crop, could achieve under climate change less than half of its current potential already in the 2050s and less than 10% by the 2080s. Suitable areas for cocoa production decrease strongly resulting in only one third of production potential compared to today. Other crops with detrimental effects of climate change include oil palm, sugarcane, coffee, and rubber. Production of maize, sorghum, and millet cope well with a future warmer climate. The NAEZ Ghana database provides valuable high-resolution information to support agricultural sector development planning and climate change adaptation strategies. The expansion of irrigation development will play a central role in some areas. This requires further research on Ghana’s linkages between food, water, and energy, taking into account climate and socio-economic changes.</p>

High-resolution weather simulation for sub-Saharan Africa on the World Community Grid

2021 ◽  
Author(s):  
Camille Le Coz ◽  
Qidi Yu ◽  
Lloyd A. Treinish ◽  
Manuel Garcia Alvarez ◽  
Ashley Cryan ◽  
...  
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Sub Saharan Africa ◽  
Large Area ◽  
World Community ◽  
The World ◽  
Sub Saharan ◽  
Socio Economic Impact ◽  
One Year ◽  
The Impact

<p>Rainfall in Africa is difficult to estimate accurately due to the large spatial variability. Most of the monsoon rainfall is generated by convective rainstorms that can be very localized, sometimes covering less than 100 km2. The goal of the African Rainfall Project is to run the Weather and Research Forecast (WRF) model for sub-Saharan Africa at a convection-permitting resolution in order to better represent such rainfall events. The resolution will be 1km, which is finer than most studies over Africa, which typically use resolutions of 3km or more. Running WRF for such a large area at such a high resolution is computationally expensive, which is where IBM’s World Community Grid comes in. The World Community Grid (WCG) is part of the Social Corporate Responsibility of IBM that crowdsources unused computing power from volunteers devices and donates it to scientific projects.</p><p>The simulation was adapted to the WCG by dividing the simulation of one year over sub-Saharan Africa in many smaller simulations of 48h over 52 by 52 km domains. These simulations are small enough to be calculated on a single computer of a volunteer at the required resolution. In total, 35609 overlapping domains are covering the whole of sub-Saharan Africa. During the post-processing phase, the smaller simulations are merged back together to obtain one consistent simulation over the whole continent.</p><p>Our main focus is rainfall, as this is the variable with the highest socio-economic impact in Africa. However, the outputs of the simulations include other variables such as the 2m-temperature, the 10m-wind speed and direction. These variables are outputted every 15min. At the end of this project, we will have over 3 billion files for a total of 0.5 PB. The data will be reorganized so that the different variables can be stored, searched and retrieved efficiently. After the reorganization, the data will be made publicly available.</p><p>The first validation step will be to examine the impact of dividing sub-Saharan Africa into many smaller domains. This will be done by comparing the simulation from this project to one large simulation. This simulation is obtained by running WRF at a 1km resolution on a large domain (500km by 1000km) for a shorter period, using Cartesius, the Dutch national computer. The second validation step will be to compare the simulations with satellite data and with in-situ measurements from the TAHMO network (www.tahmo.org).</p>

scholarly journals Technical Note: The effect of sensor resolution on the number of cloud-free observations from space

2007 ◽  
Vol 7 (11) ◽  
pp. 2881-2891 ◽  
Author(s):  
J. M. Krijger ◽  
M. van Weele ◽  
I. Aben ◽  
R. Frey
Keyword(s):  
High Resolution ◽  
South America ◽  
Technical Note ◽  
Atmospheric Composition ◽  
Focal Points ◽  
Surface Emission ◽  
Sensor Resolution ◽  
Northern South America ◽  
Footprint Area ◽  
Cloud Mask

Abstract. Air quality and surface emission inversions are likely to be focal points for future satellite missions on atmospheric composition. Most important for these applications is sensitivity to the atmospheric composition in the lowest few kilometers of the troposphere. Reduced sensitivity by clouds needs to be minimized. In this study we have quantified the increase in number of useful footprints, i.e. footprints which are sufficient cloud-free, as a function of sensor resolution (footprint area). High resolution (1 km×1 km) MODIS TERRA cloud mask observations are aggregated to lower resolutions. Statistics for different thresholds on cloudiness are applied. For each month in 2004 four days of MODIS data are analyzed. Globally the fraction of cloud-free observations drops from 16% at 100 km2 resolution to only 3% at 10 000 km2 if not a single MODIS observation within a footprint is allowed to be cloudy. If up to 5% or 20% of a footprint is allowed to be cloudy, the fraction of cloud-free observations is 9% or 17%, respectively, at 10 000 km2 resolution. The probability of finding cloud-free observations for different sensor resolutions is also quantified as a function of geolocation and season, showing examples over Europe and northern South America (ITCZ).

scholarly journals A high-resolution gridded dataset to assess electrification in sub-Saharan Africa

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Giacomo Falchetta ◽  
Shonali Pachauri ◽  
Simon Parkinson ◽  
Edward Byers
Keyword(s):  
High Resolution ◽  
Sub Saharan Africa ◽  
Gridded Dataset ◽  
Sub Saharan

scholarly journals Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap

2018 ◽  
Vol 10 (7) ◽  
pp. 1145 ◽  
Author(s):  
Yann Forget ◽  
Catherine Linard ◽  
Marius Gilbert
Keyword(s):  
High Resolution ◽  
Supervised Learning ◽  
Spatial Data ◽  
Urban Areas ◽  
Supervised Classification ◽  
Large Scale ◽  
Landsat Imagery ◽  
Training Samples ◽  
Wide Range ◽  
Sub Saharan

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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