Category Archives: Gps
Gis On The Qinghai – Tibet Railway
Weary pilgrims, attending the Sunning of the Buddha Festival in the Chinese city of Xining (the capital of Qinghai Province), board the Qinghai-Tibet train for the 26-hour trip home to Lhasa, capital of Tibet Province.Whilst asleep, the train will be supervised by the railway’s control centre, which displays real-time information on the train’s location, speed, oxygen levels, and electrical system.The control centre is a partnership between Qinghai-Tibet Railway Bureau and the State Key Laboratory of Rail Traffic Control and Safety, at Beijing Jiaotong University. Together, they have developed a monitoring system for railway operation and safety for the Tibetan line.Close monitoring is critical as the train ascends from Xining (2,275 metre) to cross a remote Himalayan plateau at 4000 metre altitude, before reaching their destination 1900 kilometers away.When the railway section between Golumd (the second largest city of Qinghai province) and Lhasa opened in July, it set numerous worldwide engineering records.Over 960 km of track runs at extreme altitudes, half of it running across permafrost; the world’s highest rail track crosses over Tanggula Pass at 5072 metres. Costing US $4.2 billion, the railway holds the world record for the highest rail tunnel and station and has 675 bridges.To protect against altitude sickness, passenger cars are pressurised and have supplemental oxygen systems; passengers have to sign a health registration agreement before boarding the train.Even the train’s diesel motor locomotives are exclusively designed to function at high altitudes. A year since the Qinghai-Tibet Railway opened, it has carried 11 million tons of freight and 2.02 million passengers, with few incidents.During the railway’s construction, planning engineers were required to design a control centre to receive and display data from various monitoring devices, combining them with data from the railway’s Microsoft and Oracle databases.They also needed
Hyperspectral Data Processing and Applications
The availability of hyperspectral data has overcome the constraints and limitations of low spectral and spatial resolution imagery, and discreet spectral signatures. Hyperspectral images provide high spectral resolution data, with many narrow con¬tiguous spectral bands allowing for detailed applications.1. Target detection, 2. Material mapping, 3. Material identification, 4. Mapping details of surface propertiesIn these cases, the additional information provided by hyperspectral imagery often provides results not possible with multispectral or other types of imagery. In target detection projects, investigators are generally trying to locate known target materials. This can sometimes involve distinguishing targets from very similar backgrounds, or locating examples of targets that are smaller than the nominal pixel size. For example, hyperspectral imagery has been used by military personnel to detect military vehicles under partial vegetation canopy, and to detect small military objects within relatively larger pixels.The spectral characteristics of oil seeps and oil-impacted soils are generally too subtle to be detected by traditional multispectral sensors. In addition, oil seeps are limited in areal extent, and are usually mixed on the surface with other materials. Under these difficult conditions, hyperspectral sensors have sufficient spectral resolution to identify even small amounts of hydrocarbon- based material through their spectral signatures. In a material identification project, investigators do not know which materials are present in the scene. Under this scenario, the analysis is designed to use hyperspectral imagery for identifying the unknown materials. This analysis may also be accompanied by material mapping in which the identified materials are geographically located throughout the image. Material mapping is also performed with hyperspectral imagery when the materials present in the scene are known beforehand. For example, hyperspectral images have been used by geologists for mapping economically interesting minerals (e.g. Clark et al. 1995, 2003). They have also