One might think that if there was already a landslide in a particular location that there’d be nothing left to make another landslide in the future. Regrettably that is not always the case. Indeed, in some geologic settings evidence of a preexisting landslide plays a role in the mapping of future landslide hazards. The deadliest individual landslides in the U.S. recently were in places where there had previously been a landslide. In the small beach community of La Conchita, CA, just south of Santa Barbara along Highway 101, a landslide occurred in 1995 followed by a debris flow in 2005, killing 10 people and damaging 36 homes. In Oso, WA situated next to the North Fork of the Stillaguamish River about 50 miles SW of Seattle, a 2006 landslide was reactivated in 2014 as a debris-avalanche flow that killed 43 people and damaged private property and local highways. A few months later a large rock avalanche near the remote town of Collbran, Colorado occurred from the location of a preexisting rockslide, resulting in the deaths of 3 people. These are just a few examples of many repeat landslides that have been observed.
(Map of Puget Sound Washington, showing the location of the field site in Mukilteo. The gray hillshade inset shows a digital elevation map with the location of the two hillslope monitoring sites, labelled LS and VH.)
USGS landslide scientists Ben Mirus, Joel Smith, and Rex Baum have been studying the coastal bluffs of Puget Sound, WA near Mukilteo where landslides often interrupt railway service. They instrumented two contrasting hillslopes: a steep but stable slope with dense vegetation, and another nearby slope that had experienced a recent landslide. They installed various sensors at 5 locations down the two slopes and waited for rain. They monitored the slopes and collected data for one year and then analyzed what they had. They were curious whether their data might show why landslides were happening in the same place they had before, instead of on nearby slopes that appeared to be just as likely, if not more likely, to slide.
(Topography and aerial imagery of the two slopes LS and VH with locations of the monitoring instrumentation. The top slope, LS, is the one with a previous landslide, and the bottom slope, VH, is the one without a landslide.)
From their measurements, they were able to tell that there were a couple of reasons why the no-landslide location remained stable compared to the preexisting landslide location that remained unstable. Not only did the non-landslide slope have roots from vegetation that stabilized the soil, but also the vegetated slope drained better after rainstorms, shedding the water that would otherwise make the slope more unstable and landslide-prone. The preexisting landslide slope, on the other hand, with less vegetation and roots, had more unstable soil made even more so by the moisture that stayed in the soil after a rainfall, rather than draining away. Repeated rainfalls added more and more moisture to the slope, increasing the instability and potential for a landslide during the wet season.
So despite intuition that a landslide might mitigate further landslides, the disruption by a landslide can actually create a situation that makes the slope even more unstable and prone to further landsliding.
For the first time, scientists can look at landslide threats anywhere around the world in near real-time, thanks to satellite data and a new model developed by NASA. The model (Landslide Hazard Assessment for Situational Awareness (LHASA)), developed at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, estimates potential landslide activity triggered by rainfall. Rainfall is the most widespread trigger of landslides around the world. If conditions beneath Earth’s surface are already unstable, heavy rains act as the last straw that causes mud, rocks or debris or all combined – to move rapidly down mountains and hillsides.
(A new model has been developed to look at how potential landslide activity is changing around the world. A global Landslide Hazard Assessment model for Situational Awareness (LHASA) has been developed to provide an indication of where and when landslides may be likely around the world every 30 minutes. Credits: NASA’s Goddard Space Flight Center/ Joy Ng)
The model is designed to increase our understanding of where and when landslide hazards are present and improve estimates of long-term patterns. “Landslides can cause widespread destruction and fatalities, but we really don’t have a complete sense of where and when landslides may be happening to inform disaster response and mitigation,” said Dalia Kirschbaum, a landslide expert at Goddard and co-author of the study. “This model helps pinpoint the time, location and severity of potential landslide hazards in near real-time all over the globe. Nothing has been done like this before.”
The model estimates potential landslide activity by first identifying areas with heavy, persistent and recent precipitation. Rainfall estimates are provided by a multi-satellite product developed by NASA using the NASA and Japan Aerospace Exploration Agency’s Global Precipitation Measurement (GPM) mission, which provides precipitation estimates around the world every 30 minutes. The model considers when GPM data exceeds a critical rainfall threshold looking back at the last seven days.
(This animation shows the potential landslide activity by month averaged over the last 15 years as evaluated by NASA’s Landslide Hazard Assessment model for Situational Awareness model. Here, you can see landslide trends across the world. Credits: NASA’s Goddard Space Flight Center / Scientific Visualization Studio)
In places where precipitation is unusually high, the model then uses a susceptibility map to determine if the area is prone to landslides. This global susceptibility map is developed using five features that play an important role in landslide activity: if roads have been built nearby if trees have been removed or burned, if a major tectonic fault is nearby, if the local bedrock is weak and if the hillsides are steep. If the susceptibility map shows the area with heavy rainfall is vulnerable, the model produces a “nowcast” identifying the area as having a high or moderate likelihood of landslide activity. The model produces new nowcasts every 30 minutes.
“The model has been able to help us understand immediate potential landslide hazards in a matter of minutes,” said Thomas Stanley, a landslide expert with the Universities Space Research Association at Goddard and co-author of the study. “It also can be used to retroactively look at how potential landslide activity varies on the global scale seasonally, annually or even on decadal scales in a way that hasn’t been possible before.”
A first-of-its-kind laser instrument designed to map the world’s forests in 3-D is moving toward an earlier launch to the International Space Station than previously expected. The Global Ecosystem Dynamics Investigation – or GEDI, pronounced like “Jedi,” of Star Wars fame – the instrument is undergoing final integration and testing this spring and summer at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. The instrument is expected to launch aboard SpaceX’s 16th commercial resupply services mission, targeted for late 2018. GEDI is being led by the University of Maryland, College Park; the instrument is being built at NASA Goddard.
“Scientists have been planning for decades to get comprehensive information about the structure of forests from space to deepen our understanding of how this structure impacts carbon resources and biodiversity across large regions and even globally, as well as a host of other science issues,” said Ralph Dubayah, GEDI principal investigator and a professor of geographical sciences at the University of Maryland. “This is why seeing the instrument built and racing toward launch is so exciting.” From its perch on the exterior of the orbiting laboratory, GEDI will be the first space-borne laser instrument to measure the structure of Earth’s tropical and temperate forests in high resolution and three dimensions. These measurements will help fill in critical gaps in scientists’ understanding of how much carbon is stored in the world’s forests, the potential for ecosystems to absorb rising concentrations of carbon dioxide in Earth’s atmosphere, and the impact of forest changes on biodiversity.
GEDI will accomplish its science goals through an ingenious use of light. The instrument is a lidar, which stands for light detection and ranging. It captures information by sending out laser pulses and then precisely measuring the light that is reflected back.
(From its perch on the exterior of the orbiting laboratory, GEDI will be the first space-borne laser instrument to measure the structure of Earth’s tropical and temperate forests in high resolution and three dimensions. Credits: NASA’s Goddard Space Flight Center)
GEDI’s three lasers will produce eight ground tracks – two of the lasers will generate two ground tracks each, and the third will generate four. As the space station and GEDI orbit Earth, laser pulses will reflect off clouds, trees and the planet’s surface. While the instrument will gather height information about everything in its path, it is specifically designed to measure forests. The amount and intensity of the light that bounces back to GEDI’s telescope will reveal details about the height and density of trees and vegetation, and even the structure of leaves and branches within a forest’s canopy.
NASA has flown multiple Earth-observing lidars in space, notably the ICESat (Ice, Cloud and land Elevation Satellite) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) missions. But GEDI will be the first to provide high-resolution laser ranging of Earth’s forests.
“GEDI originally was scheduled to launch aboard a resupply mission in mid-2019, but the team at Goddard who is building and testing GEDI was always on track to deliver a finished instrument by the fall of this year,” said Project Manager Jim Pontius, making the move to an earlier resupply mission feasible. The team is now preparing to put GEDI through a battery of pre-launch tests to ensure it is ready to withstand the rigours of launch and operating in space.
NASA selected the proposal for GEDI in 2014 through the Earth Venture Instrument program, which is run by NASA’s Earth System Science Pathfinder (ESSP) office. ESSP oversees a portfolio of projects ranging from satellites, instruments on the space station, and suborbital field campaigns on Earth that are designed to be lower-cost and more focused in scope than larger, free-flying satellite missions.
IRS-1A, the first of the series of indigenous state-of-art operating remote sensing satellites, was successfully launched into a polar sun-synchronous orbit on March 17, 1988, from the Soviet Cosmodrome at Baikonur.
The successful launch of IRS-1A was one of the proudest moments for the entire country, which depicted the maturity of the satellite to address the various requirements for managing natural resources of the nation. Its LISS-I had a spatial resolution of 72.5 meters with a swath of 148 km on the ground. LISS-II had two separate imaging sensors, LISS-II A and LISS-II B, with the spatial resolution of 36.25 meters each and mounted on the spacecraft in such a way to provide a composite swath of 146.98 km on the ground. The IRS-1A satellite, with its LISS-I and LISS-II sensors, quickly enabled India to map, monitor and manage its natural resources at coarse and medium spatial resolutions. The operational availability of data products to the user organisations further strengthened the operationalisation of remote sensing applications and management in the country.
IRS-1A was followed by the launch of IRS-1B, an identical satellite, in 1991. IRS-1A and 1B in tandem provided 11-day repetivity. These two satellites in the IRS series have been the workhorses for generating natural resources information in a variety of application areas, such as agriculture, forestry, geology and hydrology etc.
From then onwards, series of IRS spacecraft was launched with enhanced capabilities in payloads and satellite platforms. The whole gamut of the activities from the evolution of IRS missions by identifying the user requirements to the utilisation of data from these missions by user agencies is monitored by National Natural Resources Management System (NNRMS), which is the nodal agency for natural resources management and infrastructure development using remote sensing data in the country.
IRS-1A being lowered into Thermovac Chamber for Simulation Tests at ISRO Satellite Centre, Bangalore (1987-88)
Apart from meeting the general requirements, definition of IRS missions based on specific thematic applications like natural resources monitoring, ocean and atmospheric studies and cartographic applications resulted in the realisation of theme based satellite series, namely, (i) Land/water resources applications (RESOURCESAT series and RISAT series); (ii) Ocean/atmospheric studies (OCEANSAT series, INSAT-VHRR, INSAT-3D, Megha-Tropiques and SARAL); and (iii) Large-scale mapping applications (CARTOSAT series).
IRS-1A development was a major milestone in the IRS programme. On this occasion of 30 years of IRS-1A and fruitful journey of Indian remote sensing programme, it is important to look back at the achievements of Indian Space Programme particularly in remote sensing applications, wherein India has become a role-model for the rest to follow. Significant progress continued in building and launching the state-of-the-art Indian Remote Sensing Satellite as well as in the operational utilisation of the data in various applications to the nation.
The “VOSTOK” ready for Lift-off with IRS-1A on board (March 17, 1988)
Today, the array of Indian Earth Observation (EO) Satellites with imaging capabilities invisible, infrared, thermal and microwave regions of the electromagnetic spectrum, including hyperspectral sensors, have helped the country in realising major operational applications. The imaging sensors have been providing spatial resolution ranging from 1 km to better than 1m; repeat observation (temporal imaging) from 22 days to every 15 minutes and radiometric ranging from 7 bit to 12 bit, which has significantly helped in several applications at the national level. In the coming years, the Indian EO satellites are heading towards further strengthened and improved technologies, taking cognizance of the learnings/ achievements made in the yesteryears, while addressing newer observational requirements and the technological advancements including high agility spacecraft.
India’s Polar Satellite Launch Vehicle, in its 42nd flight (PSLV-C40), has launched the 710 kg Cartosat-2 Series Satellite for earth observation and 30 co-passenger satellites together weighing about 613 kg at lift-off. PSLV-C40 was launched from the First Launch Pad (FLP) of Satish Dhawan Space Centre (SDSC) SHAR, Sriharikota. In its first mission of 2018, the Indian Space Research Organisation (ISRO) successfully launched its 100th satellite. The mission comes a little over four months after the space agency’s unsuccessful launch of IRNSS-1H. Prime Minister Narendra Modi, congratulating ISRO for its success, said the launch signifies the bright future of India’s space programme.
(PSLV-C40 on First Launch Pad – Evening View. Source: ISRO)
The co-passenger satellites comprise one Microsatellite and one Nanosatellite from India as well as 3 Microsatellites and 25 Nanosatellites from six countries, namely, Canada, Finland, France, Republic of Korea, UK and USA. The total weight of all the 31 satellites carried onboard PSLV-C40 is about 1323 kg.
The 28 International customer satellites are being launched as part of the commercial arrangements between Antrix Corporation Limited (Antrix), a Government of India company under Department of Space (DOS), the commercial arm of ISRO and the International customers.
Cartosat-2 Series Satellite is the primary satellite carried by PSLV-C40. This remote sensing satellite is similar in configuration to earlier satellites in the series and is intended to augment data services to the users.
The imagery sent by satellite will be useful for cartographic applications, urban and rural applications, coastal land use and regulation, utility management like road network monitoring, water distribution, creation of land use maps, change detection to bring out geographical and manmade features and various other Land Information System (LIS) as well as Geographical Information System (GIS) applications.
New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said.
(Photo Source: ICRISAT)
In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP).
“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications,” Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.
The non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. In collaboration with ICRISAT, Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI.
“The app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages,” the company said.
To calculate the crop-sowing period, historic climate data spanning over 30 years – from 1986 to 2015 – for the Devanakonda area in Andhra Pradesh was analysed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. MAI is the standardised measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops. This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.
This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season). Predictive analysis in agriculture is not limited to crop growing alone. The Karnataka government will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state. Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state, Microsoft said.
Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalised to many other regions and crops.
India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world. A new map was released on 14th November 2017, detailing croplands worldwide in the highest resolution yet, helping to ensure global food and water security in a sustainable way.
The map establishes that there are 1.87 billion hectares of croplands in the world, which is 15 to 20 percent—or 250 to 350 million hectares (Mha)—higher than former assessments. The change is due to the more detailed understanding of large areas that were never mapped before or were inaccurately mapped as non-croplands.
Earlier studies showed either China or the United States as having the highest net cropland area, but this study shows that India ranks first, with 179.8 Mha (9.6 percent of the global net cropland area). Second is the United States with 167.8 Mha (8.9 percent), China with 165.2 Mha (8.8 percent) and Russia with 155.8 Mha (8.3 percent). Statistics of every country in the world can be viewed on an interactive map.
(This map shows cropland distribution across the world in a nominal 30-meter resolution. This is the baseline product of the GFSAD30 Project. Source: USGS)
South Asia and Europe can be considered agricultural capitals of the world due to the percentage of croplands of the total geographic area. Croplands make up more than 80 percent of Moldova, San Marino and Hungary; between 70 and 80 percent of Denmark, Ukraine, Ireland and Bangladesh; and 60 to 70 percent of the Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czech Republic, Italy and India. For comparison, the United States and China each have 18 percent croplands.
The study was led by the USGS and is part of the Global Food Security-Support Analysis Data @ 30-m (GFSAD30) Project. The map is built primarily from Landsat satellite imagery with 30-meter resolution, which is the highest spatial resolution of any global agricultural dataset.
Importance of Monitoring Croplands in Great Detail
“The map clearly shows individual farm fields, big or small, at any location in the world,” said Prasad Thenkabail, USGS research geographer and Principal Investigator for the GFSAD30 Project Team. “Given the high resolution of 30 meters and 0.09 hectares per pixel, a big advantage is the ability to see croplands in any country and sub-national regions, including states, provinces, districts, counties and villages.”
With the global population nearing the 7.6 billion mark and expected to reach 10 billion by 2050, it is of increasing importance to understand and monitor the state of agriculture across the world in great detail. This new research is useful to international development organizations, farmers, decision-makers, scientists and national security professionals.
“This map is a baseline and starting point for higher level assessments, such as identifying which crops are present and where, when they grow, their productivity, if lands are left fallow and whether the water source is irrigated or rain fed,” said Thenkabail. “Comparisons can be made between the present and past years as well as between one farm and another. It is invaluable to know the precise location of croplands and their dynamics to lead to informed and productive farm management.”
Critical for Water Security
Not only does this map and accompanying data have significant food security implications, but it is also critical as a baseline for assessing water security. Nearly 80 percent of all human water use across the world goes towards producing food, and this research provides insight on “crop per drop,” which is an assessment of the number of crops produced per unit of water.
Download data through the Land Processes Distributed Active Archive Center.