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The government has warned domestic airlines of `consequences’ if they did not adopt the Rs 774 crore GPS-Aided Geo Augmented Navigation system. The warning came during a meeting called by the DGCA earlier this week with all stakeholders, including the airlines, who have not availed of the system even 18 months after its launch.Jointly developed by Indian Space Research Organisation (ISRO) and Airports Authority of India (AAI), at an investment of Rs 774 crore, the GAGAN system was officially launched by Civil Aviation Minister Ashok Gajapathi Raju in July last year. It is said to make airline operations more efficient and cut down costs as it reduces the separation between aircraft, increases air safety and fuel efficiency. The National Civil Aviation Policy, announced by the government in June, makes it mandatory for all aircraft registered in India from January 1, 2019 to be GAGAN-enabled.
A notice issued by the DGCA dated December 19 stated that most aircraft registered in India are still not equipped with this technology. “This assumes significance as many airlines and operators have placed orders for many more aircraft which may not be equipped with necessary airborne equipment and thus not be GAGANcompliant,” DGCA director general B S Bhullar said. (Image Source: http://indiandefence.com/threads/irnss-and-gagan-explained.6981/)
However, in order for the domestic airlines to availing of the GAGAN system, they would have to make their aircraft GAGAN-compliant, which would entail a huge investment on their part. A DGCA source said that while smaller aircraft like ATRs and Bombardiers which are currently in the Indian carriers’ fleet are already equipped with the GAGAN system, bigger planes such as the Airbus A320, A330, Boeing 737, B777 and B 787s, among others, need to be retrofitted. Eight major domestic carriers – Air India, Air India Express, Jet Airways, JetLite, IndiGo, SpiceJet, GoAir, Vistara and AirAsia – have a total of 427 such planes currently in service.
“An airline will have to shell out as much as Rs 1-2 crore per aircraft to install the GAGAN system. Going by the number of planes that need to be retrofitted, the minimum investment will be at least Rs 400 crore,” the source said.
According to an ISRO spokesperson, GAGAN’s GEO footprint extends from Africa to Australia and has expansion capability for seamless navigation services across the region. “GAGAN provides the additional accuracy, availability, and integrity necessary for all phases of flight, from en route through approach for all qualified airports,” the spokesperson said.
The system is inter-operable with other international satellite based tracking systems such as the WAAS (US), EGNOS (Europe) and MSAD (Japan).
The new three-dimensional map of Earth has been completed. Mountain peaks and valley floors across the globe can now be seen with an accuracy of just one metre. The global elevation model was created as part of the TanDEM-X satellite mission; it offers unprecedented accuracy compared with other global datasets and is based on a uniform database. The approximately 150 million square kilometres of land surface were scanned from space by radar sensors. “TanDEM-X has opened up a whole new chapter in the field of remote sensing. The use of radar technology based on two satellites orbiting in close formation is still unique and was key to the high-precision remapping of Earth. In this way, DLR has demonstrated its pioneering role and satisfied the prerequisites for the next major development step in satellite-based Earth observation – the Tandem-L radar mission,” says Pascale Ehrenfreund, Chair of the Executive Board of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR).
(The first TanDEM-X mosaic of Iceland. Credit: DLR)
The quality of the global elevation model has surpassed all expectations. Exceeding the required 10-metre accuracy, the topographic map has an elevation accuracy of a single metre. This is a result of excellent system calibration. The distance between the two satellites in formation flight, for example, is determined with millimetre precision. The global coverage achieved by TanDEM-X is also unparalleled – all land surfaces were scanned multiple times and the data was then processed to create elevation models. In this process, DLR’s remote sensing specialists created a digital world map consisting of more than 450,000 individual models with pixel by pixel height detail – creating a special kind of three-dimensional mosaic.
TerraSAR-X and TanDEM-X have long exceeded their specified service lives and continue operating faultlessly and in such an efficient way that they still have enough propellant for several more years. Completion of the 3D world map does not signify the end of the mission. Due to the special nature of the formation flight, further scientific experiments are scheduled. Moreira points out: “Earth as a system is highly dynamic, which is also reflected in its topography. Through frequent updates, we could capture such dynamic processes systematically in the future. This is the primary goal of the Tandem-L mission that we have proposed.” (Image Comparing the Shuttle Radar Topography Mission (SRTM) elevation model from 2000 and the data acquired by TanDEM-X over the opencast lignite mine at Hambach, near the German town of Jülich, the improved accuracy is impressively demonstrated. In addition, the changes over the past 10 years can be seen – the mining activity has progressed considerably.)
New Synthetic Aperture Radar (SAR) methods will enable diverse data for exploration of the global ecosystem to be provided within short periods of time. The Tandem-L successor mission could provide a current elevation image of Earth’s entire landmass every eight days and thereby capture dynamic processes in a timely manner. This would also make it possible to contribute to the review of international climate and environmental agreements. New radar methods and innovative missions such as Tandem-L are set to contribute to gaining a better understanding of dynamic processes in order to protect and preserve Earth Completion of the TanDEM-X global elevation model has now paved the way for the next dimension of radar remote sensing.
About the mission
TanDEM-X is being implemented on behalf of DLR using funds from the Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie). It is a Public Private Partnership (PPP) project operated in conjunction with Airbus Defence and Space. DLR is responsible for providing TanDEM-X data to the scientific community, mission planning and implementation, radar operation and calibration, control of the two satellites, and generation of the digital elevation model. To this end, DLR has developed the necessary ground-based facilities. The DLR Microwaves and Radar Institute, the DLR Earth Observation Center and the DLR Space Operations Facility in Oberpfaffenhofen are participating in the development and operation of the ground segment of TerraSAR-X and TanDEM-X. Scientific coordination is the responsibility of the DLR Microwaves and Radar Institute. Airbus Defence and Space built the satellites and is sharing the development and operating costs. The company is also responsible for the commercial marketing of the TanDEM-X data.
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.
Raleigh, North Carolina, is one of the fastest growing areas in the country. Between 2000 and 2014, the city’s population increased by 59 percent. Downtown Raleigh has experienced dramatic growth in new residents and businesses as well. The downtown’s retail base has increased by over 35 percent in the last four years, and events, festivals, museums, and attractions bring more than 3.5 million visitors to the downtown area each year.
Last fall, residents, and business owners raised concerns about litter and cleanliness in downtown. Although multiple groups—including the Downtown Raleigh Alliance’s (DRA) Clean Ambassadors and staff from the departments of Parks, Recreation and Cultural Resources and Solid Waste Services—contribute to keeping downtown clean, the demand was outweighing available services. So the City of Raleigh turned to its robust GIS—and used AppStudio for ArcGIS for the first time to figure out what to do.
A Tool to Gather Location-Based Litter Data
Raleigh’s Office of Sustainability and DRA worked with their service partners to form a task force and create a plan of action for tackling the city’s litter problem. An eight-member team from the Parks, Recreation and Cultural Resources and Solid Waste Services departments was looking for a tool to gather location-based information about the density and types of litter in downtown. The City of Raleigh’s sustainability manager, Megan Anderson, contacted Raleigh’s GIS team to get help.
Collecting Litter Data
The litter audit took place in October 2015. The eight city staff members doing the audit received less than 15 minutes of training on the user-friendly mobile app, called Litter Reporter, just before they went out for the first time. Following the Clean Ambassadors’ cleaning routes, the auditors walked downtown six times a day at specific intervals over a period of three days to collect litter data. When they spotted litter, they photographed it; geotagged the location; and used the app’s quick-select menu to categorise it as paper, cigarette butts, containers, bottles, cans, food, or cardboard, for example. (Image: The Litter Reporter app allows users to photograph litter, geotag its location, and select its type.)
Growing Cities as Smart Cities
The department continues to use Litter Reporter every quarter to monitor trends and figure out how to efficiently manage litter downtown. Supplemental audits follow the same methodology, routes, and times as the first audit to ensure that the city is monitoring accurate trends.
“In general, there is a lot of information and buzz around smart cities and how cities are utilizing technology,” said Anderson. “The litter application is an example of how quickly the tools can be deployed to help cities gather data and make informed, smart decisions about how they deliver service. The process is an excellent model for understanding challenges faced by growing cities.”
ArcGIS—and especially AppStudio for ArcGIS—allowed the GIS team to collaborate deftly with the task force, providing its members with the tools they needed to gather data quickly and create actionable reports. Staff at the City of Raleigh will continue to use data and reports from the litter audit app to work cross-departmentally with DRA to evaluate options for increased levels of service downtown.