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All the latest news on AARSE and remote sensing.
  • 13 Mar 2016 2:54 PM | Anonymous

    Source: The Citizen


    The recent intermittent rains are raising hopes in parts of South Africa as dam levels continue to stabilise, but more rain is needed as the dams are far from full, according to the department of water and sanitation. Rainfall is also needed over vast expanses of agricultural land fed by the dams. A report released by the National Disaster Management Committee on Monday indicated that Sterkfontein Dam on the borders of KwaZulu-Natal and Free State is 88% full.

     

    The dam is largely used as a reserve for Vaal Dam in Gauteng in case of an emergency. Gariep Dam, the biggest in the country, is almost half-full at 49% while Bloemhof Dam in North West is dangerously low at 21.1%. “The country is still benefiting from the rain that fell earlier in January, as well as a few isolated rainfalls observed last week,” the report stated.

     

    Using state-of-the-art earth observation technologies, the Council for Scientific and Industrial Research (CSIR) recently revealed the extent and severity of the drought affecting six of the nine provinces. Free State and North West provinces are worst affected. “December 2015 has been recorded as the driest December in South Africa in 15 years,” said CSIR remote sensing specialist Moses Cho.

     

    Cho used remote sensing technologies that reveal an index of vegetation greenness to indicate the extent and magnitude of the drought in South Africa. The index shows a 15-year average of vegetation greenness for the North West, Gauteng, Free State, Limpopo, Mpumalanga and KwaZulu-Natal provinces, revealing a severe decrease in vegetation greenness in December 2015.

     

    “The satellite imagery derived shows that there has been an up to 60% decrease in vegetation greenness in December 2015 in some parts of the Free State and North West,” said Cho. In September last year, CSIR principal researcher professor Francois Engelbrecht revealed in a study that 2015 may well be the warmest year yet recorded in Africa. This was partially due to climate change as well as the El Nino cycle in the Pacific Ocean.

     

    Temperatures over subtropical southern Africa have risen at more than twice the global rate over the past five decades.


    Original article

  • 13 Mar 2016 2:46 PM | Anonymous

    Source: HTXT


    Large areas of the country have been gripped by crippling drought, causing cattle to perish and crops to fail. Scientists has estimated that this might be the worst drought that South Africa has seen in a very long time.


    To offset the effects of the drought as much as they can, or at least be better prepared for the future, the South African National Space Agency (SANSA) has created SANSA Drought Observatory (SANSA-DO).




    The team has already been hard at work, monitoring agricultural drought through remote sensing data, various field indicators and satellite imagery. The idea behind the formation is to provide a comprehensive depiction of the drought phenomena and its adverse impacts on agriculture and food security.


    “The team has developed a vegetation change visualisation showing the temporal and spatial progression of vegetation stress during the 2014/2015 growing cycle. The visuals are presented using animated maps which gives quick insight into the development of drought disaster currently being experienced across the country,” SANSA explains.

     

    By creating SANSA-DO and making use of as such technology as possible to track and monitor droughts, the technology will help in taking immediate action while also identifying areas that are extremely affected. There is also a prevention angle to this, as it will be able to identify areas that are more prone to drought, which will naturally aid future planning and give farmers enough time to make the right decision.

     

    “The technology has the ability to further provide an effective means for mapping the location, extent and changes of surface water over-time which will assist in assessing water availability across the country,” SANSA explained.


    Original article

  • 13 Mar 2016 2:37 PM | Anonymous

    Source: StarAfrica


    Botswana’s Minister of Minerals, Energy and Water Resources Kitso Mokaila is due in Cape Town, South Africa where he will meet a delegation from Japan’s Ministry of Mining at the ongoing mining indaba conference.The Ministry’s spokesperson Potso Thari said that Mokaila will meet with Japan’s delegation from Ministry of Mining on 9th February to discuss issues surrounding the remote sensing technology.


    Remote sensing usually refers to the technology of acquiring information about the earth’s surface (land and ocean) and atmosphere using sensors onboard airborne (aircraft, balloons) or spaceborne (satellites, space shuttles) platforms. Thari said Mokaila will also meet with South African Energy Minister Tina Joemat-Pettersson discuss export power stations based in Botswana to South Africa. The minister is also expected to meet with Kenyan authorities at the event and discuss bilateral issues.


    Mokaila is also expected to give a presentation at the event on mining situation in Botswana. The conference that will be held in Cape Town attracts over 7800 individuals representing about 1500 international companies from 100 countries and is one of the largest mining conferences in Africa. It is attended by investors, mining analysts and financiers.


    Original article

  • 13 Mar 2016 2:31 PM | Anonymous

    Source: AfricaBusiness.com


    Africa can now rely on the services of a satellite constellation tailored for the continent to provide reliable data over a wide range of essential human activities and for the protection of the environment.




    The constellation which combines the services of ten satellites with the capability of covering any part of the continent at least once a day was introduced at an African Satellite Remote Sensing Conference in Pretoria yesterday by Africa’s leading private space company the Space Commercial Services Aerospace Group (SCS AG).


    “Satellite technology is set to become an indispensable component of smart governance and economic development in Africa to ensure growth and prosperity for all the peoples of the continent,” said Dr. Sias Mostert, CEO of SCS AG in his address to the delegates.

    “Governments agencies and private companies can now have reliable, dependable, real-time high-quality data obtained through satellite imagery to support a wide range of services such as crop assessments, forestry management and deforestation, environmental protection, fire warnings, insurance risk assessments, address validation, infrastructure monitoring, urban and rural development, population counts, border control and maritime security.


    “We are now in the position to provide monitoring and management services anywhere in Africa at least once a day with a minimum turn-around time of 30 seconds to 6 hours and a resolution down to 0.5 meter.  Some types of services can be provided regardless of cloud cover or time of night or day,” said Dr. Mostert.

     

    The SCSGi African Satellite Constellation combines the capacity of the following ten satellites over Africa: the Chinese TripleSat Constellation, RadarSat-2, Deimos 1 & 2; KazEOSat 1 & 2; Landsat 8, MODIS and the Urthecast cameras IRIS, Theis and HRC-DM on the International Space Station. These satellites can be tasked to collect near real-time data on a 24/7-basis globally to deliver timely reliable information services.

     

    This service will be provided by SCS Global Information (SCSGi) is a subsidiary of the SCS Aerospace Group headquartered in Cape Town. The SCS AG group consists of three lines of business that include small satellite engineering, satellite component manufacturing and global information services.

     

    The SCSGi African Satellite Constellation combines the capacity of the following satellites over Africa – the Chinese TripleSat Constellation, RadarSat-2, Deimos 1 & 2; KazEOSat 1 & 2; Landsat 8, MODIS and the Urthecast cameras IRIS, Theis and HRC-DM on the International Space Station.  These satellites can be tasked to collect near real-time data on a 24/7-basis globally to deliver timely reliable information services.

     

    Services are provided through an online ordering system which makes it easy for customers to order their imagery. The use of such an African virtual satellite constellation makes it possible for processes to be monitored in hours, minutes and seconds, instead of days.  See http://www.scsgi.com for more information.

     

    “The time is right for Africa to become part of the worldwide outer space industry bolstered by amazing technological feats such as rovers on Mars, close-up satellite fly-by’s of Mercury and Pluto, a craft landing on an asteroid, NASA’s aim to send humans to Mars within two decades and commercial space travel now almost a reality. Within a decade every African in even the remotest part of the continent will be able to access the internet on a mobile device directly through satellites,” says Dr. Mostert.


    Original article

  • 13 Mar 2016 2:21 PM | Anonymous

    Source: RCMRD


    The Regional Centre for Mapping of Resources for Development (RCMRD) and IGAD Climate Prediction and Applications Centre (ICPAC) within the framework of the Monitoring for Environment and Security in Africa (MESA) is holding a two-week thematic training for forest monitoring and land degradation assessment. In this context, participants from 10 countries including the Intergovernmental Authority on Development (IGAD) member States and Rwanda and Burundi have been given the opportunity to process Sentinel 2 data for their own regions.

     

    The Sentinel 2 satellite mission is jointly implemented by the European Commission (EC) and European Space Agency (ESA) and provides data at 10m and 20m for land cover mapping in different spectral channels, 13 in all. It currently has a revisit period of 10 days. When Sentinel 2 satellite will be joined by Sentinel 2b in 2017, the revisit period will be every 5 days.

     

    The European Commission’s Joint Research Centre (JRC), with whom RCMRD has a long standing cooperation and a Memorandum of Understanding, provided the technical support to process the data. JRC provided training on their open-source image processing software, IMPACT. The software, originally designed to provide a rapid processing of Landsat data, has been modified to process Sentinel 2 data from the raw data files provided on the ESA web site, through to classified images, which can be used in GIS. Given the restrictions of data download in a number of countries, Sentinel data is difficult to access, with data files of close to 6 Gb. JRC aims to set up an online user interface where partner countries can select images and order a ‘light’ version so as to reduce downloading time. The interface will contain data held both at JRC and RCMRD.


    Original article


  • 01 Mar 2016 3:33 PM | AARSE Admin (Administrator)

    Source: EARSC


    The purpose of the MESA programme is to increase the capacity in information management, decision-making and planning of African continental, regional and national institutions mandated for environment, climate, and food security.


    Commissioner for Rural Economy and Agriculture of the African Union Commission revealed that the Monitoring for Environment and Security in Africa (MESA) Project is one of the tools for implementing continental strategies, programmes and decisions in Africa. Some of the strategies include: the Integrated African strategy on Meteorology (Weather and Climate Services); the High Level Work Programme on climate Change Action in Africa (WPCCAA), that facilitate Member States in the implementation of the Paris Agreement adopted at COP21 in December 2015; the Africa Regional Strategy for Disaster Risk Reduction and its Programme of Action, which is currently aligned with the Post – 2015 (Sendai) Framework for Disaster Risk Reduction; Africa Water Vision 2025; the Draft African Climate Change strategy; the Comprehensive Africa Agriculture Development Programme.


    It was disclosed in an opening statement delivered on behalf of Mrs Rhoda Peace Tumusiime, Commissioner for Rural Economy and Agriculture of the African Union Commission during the opening of the 5th meeting of the Programme Steering Committee of the MESA Project which was held 08-11 February 2016 in Accra Ghana. “The MESA Project also significantly contributes to the achievements of the aspirations of the regions, and ultimately to regional integration” added Mrs. Rhoda Peace.


    Mrs. Sherry Ayitte, Minister of Fisheries and Aquaculture Development of the Republic of Ghana on her part said “Africa’s choice to utilize earth observation technologies, allows Africa to deal with environmental challenges in near real time. Thus, the MESA priorities identified by our regions are a testimony of Africa’s commitment to address environmental changes in a timely manner”

    During the opening Ceremony, Mr. Benoist BAZIN, Head of Section Infrastructure and Sustainable Development – EU Delegation to Ghana also stated “The successive PUMA-AMESD-MESA programmes achievements are certainly the network of excellent experts and expertise established all over Africa and the collaboration between Africa and Europe, which is truly impressive.”


    The 5th MESA PSC meeting elected SADC as chair and CEMAC as deputy chair of the MESA Programme Steering Committee. The meeting which was held from 08 to 12 February 2016 in Accra, Ghana reviewed achievements of the project and passed recommendations.


    The MESA Programme Steering Committee (PSC) meeting is held at least once every year and organised by the Department of the Rural Economy and Agriculture – MESA Project. Voting members are representatives from ACP, AUC, CEMAC, ECOWAS, IGAD, IOC and SADC. EUMETSAT is the Secretariat; EU Delegation to the AU, MESA Continental and Regional Implementing Centres, JRC and UN Agencies are observers.


    Original article

  • 25 Feb 2016 3:28 PM | AARSE Admin (Administrator)

    Source: Laboratory Equipment


    One of the biggest challenges in fighting poverty is the lack of reliable information. In order to aid the poor, agencies need to map the dimensions of distressed areas and identify the absence or presence of infrastructure and services. But in many of the poorest areas of the world such information is rare.


    “There are very few data sets telling us what we need to know,” says Marshall Burke, an assistant professor of Earth system science at Stanford University. “We have surveys of a limited number of households in some countries, but that’s about it. And conducting new surveys in hard-to-reach corners of the world, such as parts of sub-Saharan Africa, can be extremely time-consuming and expensive.”


    A new technique to map poverty offers cause for hope. It’s based on millions of high-resolution satellite images of likely poverty zones. To analyze these images, the researchers used machine learning, a discipline within the broader field of artificial intelligence.


    In machine learning, scientists provide a computational model with raw data and an objective—but do not directly program the system to solve the problem. Instead, the idea is to design an algorithm that learns how to solve the puzzle by combing through the data without direct human intervention.


    The researchers began their poverty-mapping project knowing that nighttime lights provide an excellent proxy for economic activity by revealing the presence of electricity and the creature comforts it represents. That was half of the raw data that their system needed.


    “Basically, we provided the machine-learning system with daytime and nighttime satellite imagery and asked it to make predictions on poverty,” says Stefano Ermon, assistant professor of computer science. “The system essentially learned how to solve the problem by comparing those two sets of images.”


    The method is a variant of machine learning known as transfer learning. Ermon likens this to how the skills for driving a car are transferable to riding a motorcycle. In the case of poverty mapping, the model used daytime imagery to predict the distribution and intensity of nighttime lights—and hence relative prosperity.


    It then “transferred” what it learned to the task of predicting poverty. It did this by constructing “filters” associated with different types of infrastructure that are useful in estimating poverty. The system did this time and again, making day-to-night comparisons and predictions and constantly reconciling its machine-devised analytical constructs with details it gleaned from the data.


    “As the model learns, it picks up whatever it associates with increasing light in the nighttime images, compares that to daytime images of the same area, correlates its observations with data obtained from known field-surveyed areas and makes a judgment,” says David Lobell, an associate professor of Earth system science.

    Those judgments were exceptionally accurate.


    “When we compared our model with predictions made using expensive field-collected data, we found the performance levels were very close,” Ermon says.

    Highly effective machine-learning models can be very complex.


    The model the team developed has more than 50 million tunable, data-learned parameters. So although the researchers know what their mapping model is doing, they don’t know exactly how it is doing it.


    “To a very real degree we only have an intuitive sense of what it is doing,” says Lobell. “We can’t say with certainty what associations it is making, or precisely why or how it is making them.”


    Ultimately, the researchers believe, this model could supplant the expensive and time-consuming ground surveys currently used for poverty mapping.


    “This offers an unbelievable opportunity for cheap, scalable, and surprisingly accurate measurement of poverty,” Burke says. “And the beauty with developing and working with these huge data sets is that the models should do a better and better job as they accumulate more and more information.”


    The availability of information is something of a limiting factor. Right now satellite coverage of impoverished areas is spotty. More imagery, acquired on a more consistent basis, would be needed to give their system the raw material to take the next step and predict whether locales are inching toward prosperity or getting further bogged down in misery.


    But such data restraints could soon be lifted—or at least mitigated.


    “There’s a huge number of new high-resolution satellite images that are being taken right now that should be available in the next 18 months,” says Burke. “That should help us predict in time as well as space. Also, there are several micro-sat companies that plan to provide images of the planet almost daily, and we’re rapidly getting enough satellites up to do that.


    “I don’t think it will be too long before we’re able to do cheap, scalable, highly accurate mapping in time as well as space.”


    Even as they consider what they might be able to do with more abundant satellite imagery, the researchers are contemplating what they could do with different raw data—say, mobile phone activity. Mobile phone networks have exploded across the developing world, says Burke, and he can envision ways to apply machine-learning systems to identify a wide variety of prosperity indicators.


    “We won’t know until we try,” says Lobell. “The beauty of machine learning in general is that it’s very useful at finding that one thing in a million that works. Machines are quite good at that.”


    The team detailed their approach in a paper for the proceedings of the 30th AAAI Conference on Artificial Intelligence.


    Original article

  • 22 Feb 2016 11:21 AM | AARSE Admin (Administrator)


    Source: Marine Electronics & Communications


    SpeedCast International has expanded satellite communications services in and around Africa by agreeing to use Ku-band capacity on a Gazprom Space Systems (GSS) satellite. SpeedCast will use Ku-band beams on the Yamal-402 satellite to deliver broadband into the African region. It will also use an uplink, based in Germany, to deliver broadband communications into Europe.


    GSS’ constellation consists of four satellites - Yamal-202, Yamal-300K, Yamal-401 and Yamal-402 and operates ground telecommunications infrastructure. Other service providers have taken capacity on the Yamal fleet to deliver broadband to shipping. Recently, NSSLGlobal took capacity on Yamal-402 to deliver communications offshore East Africa. In September 2015, Radio Holland secured capacity on the northern beam of the Yamal 402 satellite to deliver broadband services for ships operating in the Barents Sea and Kara Sea.


    Original article


  • 04 Feb 2016 1:07 PM | AARSE Admin (Administrator)

    Source: Shephard News Team


    The SpaceDataHighway programme has reached a significant milestone with the first EDRS-A relay satellite launched into geostationary orbit on 30 January. The satellite will now undergo a test period before becoming operational for the first customer by mid-2016.

    The SpaceDataHighway system will provide high-speed laser communication in space at up to 1.8 gigabits per second. The €500 million programme is the result of a public-private partnership between the European Space Agency (ESA) and Airbus Defence and Space. 

    The SpaceDataHighway will use communication relay satellites such as EDRS-A to transfer high-volume information from Earth observation satellites, UAVs and surveillance aircraft, or even from a space station such as the ISS. With lasers able to transmit data at up to 1.8 Gbit/s, up to 50 terabytes per day can be transmitted securely in near-real-time to Earth, as opposed to the delay of several hours currently experienced.

    The laser technology is being developed by Tesat Spacecom as a highly precise pointing capability that enables two laser terminals located 75,000km apart to be connected. Airbus Defence and Space will validate the broadband (1.8 Gbps) laser link concept between EDRS-A and an Airbus A310 MRTT in mid-2016.  

    EDRS-A is a hosted payload carried on Eutelsat 9B, a Eurostar E3000-type satellite built by Airbus Defence and Space. It will be positioned at 9° East and will be able to establish laser links with orbiting observation satellites and UAVs positioned over Europe, Africa, Latin America, the Middle East and the eastern coast of North America. 

    A second satellite will be launched in 2017, which will extend the coverage, capacity and redundancy of the system. A third is expected by 2020 to extend coverage over the Asia-Pacific.


    Original article

  • 04 Feb 2016 12:42 PM | AARSE Admin (Administrator)

    Source: Phys.org; Article: University of Twente

    Unique high-resolution map on bat diversity in Africa

    Map on species richness of African bats


    Researchers of the ITC Faculty of Geo-Information Science and Earth Observation of the University of Twente have developed a unique map of all 250 African bat species on a high-resolution scale (1 km2). There are very few examples of biodiversity richness based on quantitative data at a continental scale, especially for challenging guilds like bats. The findings of the research are presented in the January edition of the scientific journal Ecological Modelling.

    The researchers have created state-of-the-art species distribution models (SDMs) for a large taxonomic group and demonstrated that by stacking these, a plausible model of fine-grained continental species diversity and endemism patterns can be obtained despite often scarce and biased occurrence data (the so-called 'Wallacean shortfall'). Very few such studies have hitherto been published that cover a large and complete taxonomic group with fine resolution at continental extent.


    Bats in Africa

    Bats are the second-most species-rich mammal group numbering more than 1270 species globally. Knowledge of their geographic distributions and diversity patterns however is very limited – possibly the poorest among mammals – mainly due to their nocturnal and volant life history, and challenging fieldwork conditions in the tropics where most bat species occur.

    The research findings suggests that African bat species richness generally increases towards the equator, varies substantially within the equatorial zone of elevated richness, often showing a positive association with high topo diversity at relatively low elevations, and accommodates surprisingly steep gradients over a few kilometers, especially near rivers in savanna biomes.

    Centers of endemism (hotspots of summed range size rarity) are mostly found in or near areas characterized by substantial elevational ranges – on tropical mountains often at higher elevations than hotspots of species richness. Spatial congruence between richness and rarity hotspots is relatively low although this depends on the definition of both rare species and hotspot size.


    Further deployment of the approach

    The approach in general, and the presented model in particular, should prove valuable for a range of applications because the maps of African bat diversity and endemism presented constitute one of the few published datasets featuring high spatial resolution, large geographic extent, and broad taxonomic scope.

    Owing to these properties, and in combination with the underlying individual SDMs, the model may help optimize protected area networks, support survey planning, and feed into biodiversity monitoring schemes. The generated data also lend themselves to a range of macro ecological analyses, including tests of hypotheses across spatial grains finer than the common limit of 1° as well as studies distinguishing taxonomic subsets and functional groups.


    Original article

    Researchers of the ITC Faculty of Geo-Information Science and Earth Observation of the University of Twente have developed a unique map of all 250 African bat species on a high-resolution scale (1 km2). There are very few examples of biodiversity richness based on quantitative data at a continental scale, especially for challenging guilds like bats. The findings of the research are presented in the January edition of the scientific journal Ecological Modelling.

    The researchers have created state-of-the-art species distribution models (SDMs) for a large taxonomic group and demonstrated that by stacking these, a plausible model of fine-grained continental species diversity and endemism patterns can be obtained despite often scarce and biased occurrence data (the so-called 'Wallacean shortfall'). Very few such studies have hitherto been published that cover a large and complete taxonomic group with fine resolution at continental extent.

    Bats in Africa

    Bats are the second-most species-rich mammal group numbering more than 1270 species globally. Knowledge of their geographic distributions and diversity patterns however is very limited – possibly the poorest among mammals – mainly due to their nocturnal and volant life history, and challenging fieldwork conditions in the tropics where most bat species occur.

    The research findings suggests that African bat species richness generally increases towards the equator, varies substantially within the equatorial zone of elevated richness, often showing a positive association with high topo diversity at relatively low elevations, and accommodates surprisingly steep gradients over a few kilometers, especially near rivers in savanna biomes.

    Centers of endemism (hotspots of summed range size rarity) are mostly found in or near areas characterized by substantial elevational ranges – on tropical mountains often at higher elevations than hotspots of species richness. Spatial congruence between richness and rarity hotspots is relatively low although this depends on the definition of both rare species and hotspot size.

    Further deployment of the approach

    The approach in general, and the presented model in particular, should prove valuable for a range of applications because the maps of African bat diversity and endemism presented constitute one of the few published datasets featuring high spatial resolution, large geographic extent, and broad taxonomic scope.

    Owing to these properties, and in combination with the underlying individual SDMs, the model may help optimize protected area networks, support survey planning, and feed into biodiversity monitoring schemes. The generated data also lend themselves to a range of macro ecological analyses, including tests of hypotheses across spatial grains finer than the common limit of 1° as well as studies distinguishing taxonomic subsets and functional groups.



    Read more at: http://phys.org/news/2016-01-unique-high-resolution-diversity-africa.html#jCp
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