Member nations of the five-nation group BRICS have agreed to share spatial data on natural resources from their remote-sensing satellites.
The move is geared towards making optimal use of space assets. According to Indian Space Research Organisation (ISRO) officials the nations will be exchanging data, including images of natural resources. Though only four of them – Brazil, Russia, India and China have remote-sensing satellites in the sun-synchronous orbit, they will give data to South Africa (SA) as it does not have a satellite of its own. Top space officials of BRICS met at the United Nations Committee on the Peaceful Uses of Outer Space Scientific and Technical Subcommittee’s 54th session at Vienna in Austria from January 30 to February 10.
Through this particular agreement, BRICS will be able to share the resources and bring developing nations under the umbrella of space, opening possibilities of using excess capacities in the satellites. As the BRIC satellites spin around the earth in lower orbit, capturing enormous data on the planet and its resources in each country, they will share it in real time for mutual benefit.
India plans to use its Resourcesat-2A, launched on December 7 from its spaceport Sriharikota in Andhra Pradesh, as part of its earth observation satellite for remote sensing data services to global users.
Going forward, the space agencies of the BRICS nations plan to share similar data for tele-education, tele-medicine and a host of societal applications, utilising the excess capacity of their respective satellites for their mutual benefit.
Every year Bihar is deluged by floods that submerge roads, destroy homes and wash away crops, leaving the disaster management authority struggling to monitor and assess the damage, and to distribute aid effectively. But new satellite mapping of flood-prone areas should transform disaster response by equipping authorities with near real-time information about inundated villages, officials said.
Bihar, which borders the Himalayan nation of Nepal, is India’s most flood-prone state. More than 70 percent of its total geographical area is at risk of annual floods, which put lives at risk and lead to heavy financial losses.
A major challenge for the Bihar state disaster management authority (BSDMA) has been mapping and monitoring flood-hit areas, according to the International Centre for Integrated Mountain Development (ICIMOD), which works to promote development across the Hindu Kush Himalayas.
(Flood-affected villagers use temporary rafts as they navigate through the floodwaters of river Ganges and move to safer grounds, after heavy rains at Patna district in Bihar August 29, 2013. Credit: REUTERS)
Since floods started in the state last month, more than 200 people have died and more than 300,000 have been forced from their homes, disaster officials said. ICOMOD has helped generate innovative flood mapping for 33 districts in Bihar and an online flood information system that is allowing faster response to a crisis, quicker damage assessment, and better risk management than with conventional methods, said officials from ICIMOD, based in Kathmandu.
“Traditionally, field teams are organised and dispatched to flooded areas to map floods. This can be time-consuming and operationally difficult during a flooding event,” Shahriar M. Wahid, a senior ICIMOD hydrologist, told the Thomson Reuters Foundation via email.
While “satellite-sourced flood maps alone cannot provide early warning to (the) at-risk population”, he said, satellite data, in combination with flood simulations, can do this. If flash floods triggered by torrential rain occur in Nepal, Bihar’s residents can expect to see inundations about eight hours later, according to data from the BSDMA. Wahid said the new flood maps will be most useful for the distribution of relief, assessment of damages and to determine crop insurance payouts, among other benefits.
The project uses satellite technology that penetrates cloud cover, unlike optics-based satellite imagery. This is useful in the Himalayan region where monsoons bring thick clouds. Flood maps can be generated within five to six hours after raw satellite data is received. The floods are circulated to government officials and relief agencies through a satellite communication network. Space satellite technology is often touted by disaster relief experts as an important tool in managing the growing number of climate-linked disasters around the world. But the cost of such technology for developing countries, even fast-growing ones like India, can be a challenge.
Satellite maps can also aid prevention because they act as a template for years to come, recording rainfall patterns and data from the water department, among other factors, ICIMOD said.
Chairman, ISRO and Secretary, Department of Space has released first year of MOM Long-term archive data (Sept 24, 2014 to Sept 23, 2015) to public.
India’s Mars Orbiter Mission (MOM) has successfully completed its designed life of 6 months on 24 March 2015 and accomplished its planned mission objectives.MOM is continuing to function normally and is completing two years around Mars on September 24, 2016. At present MOM and all its scientific payloads are in good health and it continues to provide valuable data of Mars surface and its atmosphere.
Mars Orbiter Mission is ISRO’s first interplanetary mission and is orbiting around Mars in an elliptical orbit of about 343 km x 71191 km as on 16th September 2016. Mars Orbiter Mission (MOM) is a complex technological mission considering the critical mission operations and stringent requirements on propulsion, communications and other bus systems of the spacecraft.
MOM carries a suite of five scientific payloads with a total mass of about 14 kg. The three payloads which have been designed, developed and delivered by Space Applications Centre are:
1. Mars Colour Camera (MCC)
2. Methane Sensor for Mars (MSM)
3. Thermal Infra-Red Imaging Spectrometer (TIS)
4. The Mars Exospheric Neutral Composition Analyzer (MENCA)
5. The Lyman-Alpha Photometer (LAP)
Snapshots from the portal:
Fig-1. Typical coverage data points MCC , TIS, MSM over Mars (Credit: ISRO)
Fig.-2. MSM Coverages
Online MOM data sets archive
The MOM archive will contain instrument raw data, derived or merged (wherever applicable) apart from spacecraft and instrument ancillary or housekeeping data. The MOM data sets are disseminated through an online archive, where the data are delivered electronically. A data set will include the instrument data as well as the ancillary data, software, and necessary documentation that support the use of these data products. In general, the data products from different instruments are contained in separate data sets. Data sets may include data products from one or separate data sets.
Acknowledge the source of data, funding etc.
Researchers and common public who is downloading the Mars Orbiter Mission data sets, are required to acknowledge the ISRO for data, funding (if granted) and the research writeups taken from the archive.
1. When publishing a paper using the Mars Orbiter Mission data, please
a) mention on “Mars Orbiter Mission (MOM)” in abstract and
b) include the following statement in acknowledgment –
“We acknowledge the use of data from the Mars Orbiter Mission (MOM), first inter-planetary mission of the Indian Space Research Organisation (ISRO), archived at the Indian Space Science Data Centre (ISSDC)”
2. If you are using the results of Mars Orbiter Mission which are already published and carrying out further interpretation or modeling, please include the following statement in acknowledgment –
“The research is based partially / to a significant extent (whichever is applicable) on the results obtained from the Mars Orbiter Mission (MOM), first inter-planetary mission of the Indian Space Research Organisation (ISRO), archived at the Indian Space Science Data Centre (ISSDC)”
Global wind data, which is very crucial for cyclone detection and weather forecasting applications, was gathered by Scatterometer instrument flown as one of the payloads in OCEANSAT- 2 satellite. This data was utilised by national and international users and proved to be a very important tool for oceanographic studies. SCATSAT-1 is the continuity mission for Scatterometer payload carried by the earlier Oceansat-2 satellite.
The magnitude and direction of the wind vector at the ocean surface is a key parameter for weather prediction as well as detection and tracking of cyclones. The objectives of SCATSAT-1 are to facilitate the weather forecasting services to the user communities through the generation of wind vector products. The Ku-band Scatterometer payload carried by SCATSAT-1 has enhanced features compared to the similar one carried by Oceansat-2 launched in 2009
Scatterometer operates on the principle of radar. When the radar radiates energy pulses towards the ocean’s surface, a backscatter effect is produced due to interaction between electromagnetic waves and sea surface waves, which is function of speed and direction of surface winds over the oceans.
This process of receiving back-scattered signal is carried out while conically scanning or rotating the antenna along with the motion of the satellite giving a swath of 1400 km. The collected data is processed onboard to generate estimates of backscattered power/signals and stored on a data recorder. This recorded data is then transmitted to ground station which is converted into wind vectors for the global user.
SCATSAT-1 is built around ISRO’s small satellite ‘IMS-2 BUS’ and the mass of the spacecraft is 371 kilograms. The spacecraft will work in sun synchronous orbit of 720 km. altitude with an inclination of 98.1 deg. This will be a polar orbiting satellite and will take two days to cover the entire globe. The expected life span of the satellite is 5 years with non-stop 24 X 7 all weather operations. Wind speed is measured in the range of 3m/s to 30m/s and 0-360 deg directions. Finally, wind vector grids of 25kms*25kms over oceans will be generated for the entire globe.
These wind vectors will help meteorologists in accurately predict the cyclone formation, its movement and estimated landfall. It may be recalled that Ocean wind vectors data helped in accurately predicting cyclone ‘Phailin’ in the Odisha coast in 2013, which helped in mitigation and saving of mankind and livestock.
SCATSAT-1 is a global mission and data generated from the Scatterometer, developed by ISRO will also be utilized by the American space agency NASA and European Space Agency organization, EUMETSAT to provide global weather data to all those involved in weather studies and global climate change studies.
Geospatial Network community congratulates ISRO scientists for this achievement.
Researchers have combined satellite imagery with AI to predict areas of poverty across the world. There’s little reliable data on local incomes in developing countries, which hampers efforts to tackle the problem. A team from Stanford University were able to train a computer system to identify impoverished areas from satellite and survey data in five African countries.
“The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below $1 a day,” Dr Burke, assistant professor of Earth system science at Stanford, told the BBC’s Science in Action programme.
“We traditionally collect poverty data through household surveys. We send survey enumerators around to houses and we ask lots of questions about income, consumption – what they’ve bought in the last year and we use that data to construct our poverty measures.”
However, surveys are costly, infrequent and sometimes impossible to carry out in particular regions of countries because of, for example, armed conflict.
So there is a need for other accurate measures of household consumption and income in the developing world. The idea of mapping poverty from satellite imagery is not completely new. Recent studies have shown that space-based data that capture night lights can be used to predict wealth in a given area. But night lights are not such a good indicator at the bottom end of the income distribution, where satellite images are dark across the board.
The latest study looked at daylight images that capture features such as paved roads and metal roofs – markers that can help distinguish different levels of economic wellbeing in developing countries. They then used a sophisticated computer model to categorise the various indicators in daytime satellite images of Nigeria, Tanzania, Uganda, Rwanda and Malawi.
“If you give a computer enough data it can figure out what to look for. We trained a computer model to find things in imagery that are predictive of poverty,” said Dr Burke.
“It finds things like roads, like urban areas, like farmland, it finds waterways – those are things we recognise. It also finds things we don’t recognise. It finds patterns in imagery that to you or I don’t really look like anything… but it’s something the computer has figured out is predictive of where poor people are.”
The researchers used imagery from countries for which survey data were available to validate the computer model’s findings. “These things [that the computer model found] are surprisingly predictive of economic livelihoods in these countries,” Dr Burke explained.
The researchers say their ambition is to scale up the technique to cover all of sub-Saharan Africa and, afterwards, the whole of the developing world.
NASA researchers have helped produce the first map showing what parts of the bottom of the massive Greenland Ice Sheet are thawed – key information in better predicting how the ice sheet will react to a warming climate.
Greenland’s thick ice sheet insulates the bedrock below from the cold temperatures at the surface, so the bottom of the ice is often tens of degrees warmer than at the top, because the ice bottom is slowly warmed by heat coming from the Earth’s depths. Knowing whether Greenland’s ice lies on wet, slippery ground or is anchored to dry, frozen bedrock is essential for predicting how this ice will flow in the future, But scientists have very few direct observations of the thermal conditions beneath the ice sheet, obtained through fewer than two dozen boreholes that have reached the bottom. Now, a new study synthesizes several methods to infer the Greenland Ice Sheet’s basal thermal state –whether the bottom of the ice is melted or not– leading to the first map that identifies frozen and thawed areas across the whole ice sheet.
(This first-of-a-kind map, showing which parts of the bottom of the Greenland Ice Sheet are likely thawed (red), frozen (blue) or still uncertain (gray), will help scientists better predict how the ice will flow in a warming climate. Credits: NASA Earth Observatory/Jesse Allen)
“We’re ultimately interested in understanding how the ice sheet flows and how it will behave in the future,” said Joe MacGregor, lead author of the study and a glaciologist at NASA’s Goddard Space Flight Center in Greenbelt, Md. “If the ice at its bottom is at the melting point temperature, or thawed, then there could be enough liquid water there for the ice to flow faster and affect how quickly it responds to climate change.”
As farmers in Nepal prepare for the benefits of the monsoon season, Dalia Kirschbaum anticipates the dangers of those torrential rains—mainly, the loosening of earth on steep slopes that can lead to landslides. In this mountainous country, 60 to 80 percent of the annual precipitation falls during the monsoon (roughly June to August). That’s when roughly 90 percent of Nepal’s landslide fatalities also occur, according to a 2015 report from the United Nations Office for the Coordination of Humanitarian Affairs.
The Sudden Landslide Identification Product (SLIP) combs through Earth imagery and analyses consecutive images of the same location to spot changes in soil moisture, muddiness, and other surface features. The program also compares the hill slopes with topographic information derived from digital elevation models, such as those built from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emissions and Reflection Radiometer (ASTER). By combining this information, SLIP can automatically pinpoint the locations of possible landslides each time a new, cloud-free land image is acquired.
(The left and middle images above were acquired by the Landsat 8 satellite on September 15, 2013, and September 18, 2014—before and after the Jure landslide in Nepal on August 2, 2014. The image on the right shows that 2014 Landsat image processed with the new SLIP algorithm. The red areas show most of the traits of a landslide, while yellow areas exhibit a few of the proxy traits.)
What the Goddard team cannot determine from images alone is when a landslide occurred. Landsat, for instance, takes 16 days before it passes over the same spot on Earth. To more precisely pin a date on each landslide, Kirschbaum and colleagues turn to rainfall measurements from the Global Precipitation Measurement (GPM) mission. The GPM core satellite measures rain and snow several times daily, allowing researchers to create maps of rain accumulation over 24-, 48-, and 72-hour periods for given areas of interest—a product they call Detecting Real-time Increased Precipitation or DRIP. When a certain amount of rain has fallen in a region, an email can be sent to emergency responders and other interested parties.
Though still in the testing phase, the SLIP-DRIP software is open source and available to the public. Ahamed, Kirschbaum, and colleagues believe it could significantly improve landslide inventories, leading to better risk management. This information will ultimately be fed into NASA’s Global Landslide Catalog—the first and only global database of rainfall-triggered landslides. The catalogue is accessible to emergency response teams, researchers, and the public. To date, the catalogue has only included landslides reported in news outlets, online journals, and disaster databases. SLIP-DRIP products will make that information more current and comprehensive.