Benefiting from its climate, geology and geography, Germany affected by natural hazards such as earth- quakes, floods, or tornadoes relatively infrequently – or with a low intensity. Nevertheless, natural events, particularly meteorological events such as storms and floods, continue to cause substantial damage in Ger- many. This is primarily due to Germany's high popu- lation density and concentration of property assets. The Munich Re re-insurance company estimates the econo- mical damages in Germany caused by natural disasters during 1989-98 to some 10 billion Euro (at 1998 prices). Furthermore, Germany is often affected indirectly by its many relations with regions that are more frequently, or more intensely, affected by natural disasters. Germany’s economic co-operation activities with developing coun- tries can thus benefit from an improved co-ordination of Earthobservation information. This is also true in other contexts such as the protection of German citizens abro- ad. One example: More than 500 German citizens peris- hed in the Indian Ocean tsunami on 26 December 2004, making it one of the most devastating "German" natural disasters. This demonstrates that German involvement in fighting the adverse effects of natural and other dis- asters elsewhere in the world is not merely about hel- ping those affected as a token of moral responsibility, but often lies in our own immediate interest.
In this paper, instead, we are interested in studying a platform that offers high temporal resolution, offering a service that is complementing low and medium Earth orbit spacecraft. The reason for this interest originates in a number of potential applications that could be enabled by a platform that can continuously cover the poles. Although high- bandwidth telecommunications and high-resolution imagery are difficult due to the large Earth-spacecraft distance, a number of novel potential applications are enabled, both in the fields of observation and telecommunications. It was shown that spatial resolution in the visible wavelength in the range 10-40 km should be sufficient for real-time, continuous views of dynamic phenomena and large-scale polar weather systems . The creation of atmospheric motion vectors (AMV) would also make use of the stationary location of the platform, avoiding gap problems related to geo-location and inter-calibration that composite images introduce . Glaciology and ice-pack monitoring would also benefit from continuous, but low resolution polar observation . Ultraviolet imagery of the polar night regions at 100 km resolution or better would enable real-time monitoring of rapidly-changing hot spots in the aurora that can affect high frequency communications and radar . The platform could also be used as a continuous data-relay for key Antarctic research activities, in particular for scientific experiments, links to automated weather stations, emergency airfields and telemedicine. Ship tracking was also proposed, to support future high-latitude oil and gas exploration .
Abstract We used coincident Envisat RA2 and AATSR temperature and wind speed data from 2008/2009 to calculate the global net sea-air ﬂux of dimethyl sulﬁde (DMS), which we estimate to be 19.6 Tg S a 21 . Our monthly ﬂux calculations are compared to open ocean eddy correlation measurements of DMS ﬂux from 10 recent cruises, with a root mean square difference of 3.1 lmol m 22 day 21 . In a sensitivity analysis, we varied temperature, salinity, surface wind speed, and aqueous DMS concentration, using ﬁxed global changes as well as CMIP5 model output. The range of DMS ﬂux in future climate scenarios is discussed. The CMIP5 model predicts a reduction in surface wind speed and we estimate that this will decrease the global annual sea-air ﬂux of DMS by 22% over 25 years. Concurrent changes in temperature, salinity, and DMS concentra- tion increase the global ﬂux by much smaller amounts. The net effect of all CMIP5 modelled 25 year predic- tions was a 19% reduction in global DMS ﬂux. 25 year DMS concentration changes had signiﬁcant regional effects, some positive (Southern Ocean, North Atlantic, Northwest Paciﬁc) and some negative (isolated regions along the Equator and in the Indian Ocean). Using satellite-detected coverage of coccolithophore blooms, our estimate of their contribution to North Atlantic DMS emissions suggests that the coccolitho- phores contribute only a small percentage of the North Atlantic annual ﬂux estimate, but may be more important in the summertime and in the northeast Atlantic.
Within 2 years, a mechanism will be established for coordinating user needs across the various societal benefit areas. GEO will facilitate the development and maintenance of a distinct and common user requirements database for GEOSS, building on and linking to existing user requirements databases. These requirements will include specifics such as location, frequency, and accuracy. In the same timescale, a collaboration mechanism to share costs and benefits will be developed for observations and infrastructures for which the defined requirements may not be met by single-party activities alone. GEO will also provide a framework for securing the future continuity of necessary observations and initiating new observations. GEO could act as a forum for discussion on common implementation issues at regional and trans-national levels, such as transportation of in situ observation devices across borders. The implementation of GEOSS will facilitate, within 2 years, the establishment and maintenance of baseline sites for global in situ networks.
5 See http://www.nature.com/news/
mation from which a range of hydrological responses may be inferred or assessed (Jana et al., 2016; Montzka et al., 2017). A recent addition to the COSMOS program has been the use of mobile “rovers”, which offer a way to increase the spatial coverage from the local to mesoscales (Desilets et al., 2010; Chrisman and Zreda, 2013), while also offering a means to merge data from fixed probes to provide a multi- scale real-time soil moisture product (Franz et al., 2015). In addition to supporting hyper-resolution land-surface mod- elling needs, the rover approach provides opportunities not only in research but also commercial activities; most no- tably in precision agriculture, e.g. mounting rovers to ex- isting farm equipment (sprayers, tractors, etc.), autonomous farm vehicles, or to rotating infrastructure (i.e. centre-pivot irrigation systems), offers an interesting opportunistic sens- ing possibility. The capacity to mount probes on delivery trucks, self-driving vehicles, or even national train networks would further expand observational capacity and provide semi-repeatable local and regional mapping opportunities across both natural and urban landscapes. Mobile sensors can easily collect data from either ground vehicles (e.g. snow mobiles, dog sleds) or low-flying aircraft, which offers a po- tentially unprecedented calibration, validation, and evalua- tion dataset for a range of hydrological variables. While rov- ing probes are fairly heavy (50+ kg) and miniaturization op- tions are somewhat limited, the use of drone swarms with several smaller probes functioning as a single unit would further increase mapping possibilities (see Sect. 3.2). With the simultaneous use of several detector energies (bare, cad- mium shielded, and plastic shielded probes) recent research has illustrated the means to collect information on vegetation condition, soil organic properties, and soil moisture simul- taneously, providing a valuable resource to support observa- tion and modelling strategies (Andreasen et al., 2016). Such sensing technology also has the potential to augment ongo- ing global digital soil mapping efforts (Sanchez et al., 2009), as well as aid in the validation of existing high-resolution products (Chaney et al., 2016).
In the last decade, we have seen a massive increase in research on CS and EO (see Figure 1 ) and this trend is expected to continue in the future. Moreover, as we showed in Table 1 , there are already many different ongoing CS projects that have relevance for EO, but most are not embedding their data streams into operational EO applications, while others suffer from insufficient citizen participation. One important element needed to make citizen observations an official data stream is legal recognition by governments and local authorities that citizen-based data are a valid source of information and to recognize that citizens are able to deliver data of sufficient quality that can potentially complement, but not necessarily replace, existing observation networks. For example, in the USA, the Crowdsourcing and CS Act, which came into force in January 2017, gives federal agencies clear authorization to use CS and crowdsourced data. This recognition paves the way for the development of truly integrated environmental monitoring systems involving citizens as a key contributor. No such legislation exists in the EU or other countries around the world.
Abstract: For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely future land-cover. An integration of EO (earthobservation), GIS (geographical information science) and Stochastic Modelling was examined. Post-classification Change Detection employed Landsat TM or ETM+ images in 1986, 2002 and 2008. Subsequently, Markov Chain Analysis projected the land-cover distribution for 2020. Seven broad land-use and land-cover classes were identified and mapped, namely: built-up areas, mine sites tailing ponds barren land forestland farmland and rangeland. The results obtained for the 2008 to 2020 projection revealed a continuous expansion of built-up areas (1.63%), mine sites (0.89%) and farmland (3.4%), and a reduction of forestland (4.17%) and rangeland (2.59%). Despite the advent of very high resolution satellite imagery, this use of EO and GIS technology focussed on low-cost and lower resolution satellite imagery, coupled with Markov Modelling and was found to be beneficial in describing and analysing land-cover change processes in the study area, and was hence potentially useful for strategic planning purposes.
mation from which a range of hydrological responses may be inferred or assessed (Jana et al., 2016; Montzka et al., 2017). A recent addition to the COSMOS program has been the use of mobile “rovers”, which offer a way to increase the spatial coverage from the local to mesoscales (Desilets et al., 2010; Chrisman and Zreda, 2013), while also offering a means to merge data from fixed probes to provide a multi- scale real-time soil moisture product (Franz et al., 2015). In addition to supporting hyper-resolution land-surface mod- elling needs, the rover approach provides opportunities not only in research but also commercial activities; most no- tably in precision agriculture, e.g. mounting rovers to ex- isting farm equipment (sprayers, tractors, etc.), autonomous farm vehicles, or to rotating infrastructure (i.e. centre-pivot irrigation systems), offers an interesting opportunistic sens- ing possibility. The capacity to mount probes on delivery trucks, self-driving vehicles, or even national train networks would further expand observational capacity and provide semi-repeatable local and regional mapping opportunities across both natural and urban landscapes. Mobile sensors can easily collect data from either ground vehicles (e.g. snow mobiles, dog sleds) or low-flying aircraft, which offers a po- tentially unprecedented calibration, validation, and evalua- tion dataset for a range of hydrological variables. While rov- ing probes are fairly heavy (50 + kg) and miniaturization op- tions are somewhat limited, the use of drone swarms with several smaller probes functioning as a single unit would further increase mapping possibilities (see Sect. 3.2). With the simultaneous use of several detector energies (bare, cad- mium shielded, and plastic shielded probes) recent research has illustrated the means to collect information on vegetation condition, soil organic properties, and soil moisture simul- taneously, providing a valuable resource to support observa- tion and modelling strategies (Andreasen et al., 2016). Such sensing technology also has the potential to augment ongo- ing global digital soil mapping efforts (Sanchez et al., 2009), as well as aid in the validation of existing high-resolution products (Chaney et al., 2016).
In this document author proposes systematic outline of membrane computing method in land cover feature extraction. Membrane computing is a novel branch of natural computation that has an immense deal of distribution as well as holds maximal parallelism. The bio-inspired method is utilized for picture classification in addition to these specific pictures are known as remote sensing satellite picture. The terrain landscapes similar to vegetation, water, rocky, barren, in addition to urban are required to be categorized as their data arrange for enormous support throughout climatic behavioral changes, natural disaster, as well as in several further regions of environmental variations. They have well-defined constraints of membrane computing in land cover feature abstraction terms consequently displaying that the particular idiom of membrane computing is valuable for picture classification in addition to then they have projected a procedure of P framework. The recommended procedure is implemented to Alwar region of Rajasthan of 472X576 measurement that also contains 7 Band Indian Resources at Satellite Digital Numbers. The procedure has apprehended nearly all types of the terrain structures of these particular area. It demonstrates practically 99% competence on vegetation as well as water area. The KHAT figures displays that suggested procedure has a complete proficiency of about 0.68812.
contribution. EnMAP will measure in the VNIR and SWIR spectral regions of the solar spectrum with a spectral sampling of 5–10 nm, a ground sampling distance of 30 m and an image swath width of 30 km. EnMAP hyperspectral measurements will enable the monitoring of a wide span of Earth materials and environmental processes. Data products from different pre-processing levels (from calibrated at-sensor radiance to orthorectified surface reflectance data) will be delivered to scientific users under an open data policy. A series of core research topics within the EnMAP science plan, such as agriculture, forestry, water, ecosystem science, soils and geology and urban environments, is currently being addressed through a series of EnMAP-specific research programs. Other on-going preparatory activities include the simulation of EnMAP-like datasets over different environments for testing data pre-processing and application-based algorithms, the consolidation of the calibration-validation plan and the implementation of a software toolbox for the processing and exploitation of EnMAP data.
Abstract: Hyperspectral sensors are a widely used tool in remote sensing of the Earth’s surface. Due to the versatility of the sensors, a multitude of applications profit from or use hyperspectral data, most prominently the assessment of the impact of climate change on the environment. The acceptance of hyperspectral remote sensing stems, in part, from the relatively easy access to data. Besides several existing and planned spaceborne instruments, airborne sensors are by now a commercial, if expensive, commodity. The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) oper- ates an optical laboratory for the characterization and calibration of airborne hyperspectral sensors, the calibration home base (CHB). This laboratory is needed to close the gap between the extensive character- ization efforts performed for spaceborne devices, and the characterization performed by the commercial sensor’s manufacturers. Currently, the latter often does not incorporate the possibility of the traceability of calibration, and may not take into consideration sensor properties that require more complex or time- consuming characterisation methods. As the airborne sensors are effectively used to develop and validate methods for spaceborne hyperspectral remote sensing, the same level of detail in characterization needs to be applied to the airborne devices, resulting in similar measurement uncertainties. One objective of this thesis is therefore to assess the impact of the characterization and calibration possible with the CHB on data from airborne hyperspectral instruments. This is shown on a pair of NEO HySpex hyperspectral instruments operated by DLR for the bathymetry of lake Starnberg as an exemplary hyperspectral remote sensing data product. The requirements for the calibration of future spaceborne hyperspectral instru- ments include the traceability to the système international d’unités (SI). The second part of the thesis therefore deals with different
The capacity of monitoring the Earth’s surface through EarthObservation (EO) is of vital importance to take decisions in a multitude of critical applications, such as disaster response, conservation or understanding the evolution of agrarian and urban spaces. The use of aerial imagery for surveying and assisting in the creation of maps predates modern aviation and was initially based on the manual interpretation of photographs obtained from kites and balloons. Nowadays the availability of Remote Sensing (RS) imagery has increased to an extent that it can not all be manually interpreted: satellites provide a continuous flow of global multi-spectral imagery, often multiple times a month for a given location, at spatial resolutions down to a few meters per pixels; large scale aerial campaigns are organized by many governments every few years, providing sub-meter reso- lution imagery, also known as Very High Resolution (VHR), over whole countries; and the plummeting costs of Unmanned Aerial Vehicles (UAVs) mean they are being increasingly deployed, typically providing each time a few square kilometers of imagery at decimeter resolution. Although advances in photogrammetry and image processing have allowed to assist the photo-interpreters in coping with this data deluge by providing them with orthorectified and radiometrically corrected imagery, the semantic interpretation of the content of the images is still heavily dependent on human expertise to interpret VHR imagery.
This principle of ordered value-adding requires well-established techniques for data management, batch processing and databases, local and distributed (cloud) pro- cessing, understanding of the information flow, experience with learning principles, knowledge extraction from image and library data, and discovery of image seman- tics. At present, typical data sources with easy access are publicly available scientific image data provided by the European Copernicus mission with its Sentinel satellites [5, 6] as well as high-resolution remote sensing images [7, 8]. The European Sentinel satellites comprise among others a constellation of SAR imagers (i.e., Sentinel-1A/ Sentinel-1B providing typically large radar images, with a ground sampling distance of 20 meters and selectable horizontal and vertical polarizations), and a constellation of optical imagers (i.e., Sentinel-2A/Sentinel-2B delivering typically large multi- spectral images with 13 different bands and a ground resolution—depending on the bands—of 10–60 m). This space segment of the Copernicus mission is comple- mented by systematic level-1 and level-2 image data processing on the ground and by support environments that serve as comfortable platforms for further data handling and interpretation covering all aspects of applied data science. These approaches then pave the way for deeper semantic data analysis and understanding as typically required in Earthobservation for crop yield predictions, atmospheric research, etc. The design of Earthobservation (EO) missions as constellations of several satel- lites brings important advantages. However, this is not the case for some of the most popular EO missions. Figure 1 shows typical TerraSAR-X and Copernicus Sentinel overpasses from different orbits and their target areas.
Although our definition of EO is wider than the space sector alone, it is c lear t hat t his sector plays ever prominent role in EO which partly explains the results shown. The leading role of France and Italy in the space industry has its long history. It aly was t he first European country outside the two-party space club, namely Russia and the US, which launched an in-house built satellite using the US launc hing vehic le (1964). One year later France, as a third country in the world, proved operational it s own launc hing system by placing in orbit Astérix satellite. As for Germany, its research on the roc ketry systems before and during the Second World War highly advanced the space exploration. The defeat of Germany (and migration of its scientists to other countries, mainly t o t he US as part of Operation Paperclip) was probably the sole reason why Germany was not a main space power already in the early space race. Nevertheless, even if not at t he fr ont lines, Germany has been developing its own space capabilities well embedded in the wider economic and scientific context. As far as the UK is considered, it s first sat ellit e programme started in 1959, with the Ariel series of British satellites, built in cooperation with the U.S. and launched using American rockets. The first British satellite, Ariel 1, was launched in 1962. The British space programme has always stressed unmanned space research and commercial initiatives. Later, also Spain joined the space-faring st ates. It launched its first satellite in 1974. In 1975 all five countries became founder members of the European Space Agency (ESA), along with the Netherlands, Belgium, Sweden, Denmark and Switzerland.
Dr. Haddawy introduces the Asian Institute of Technology and explains that it is moving very fast from an individual university to partnerships and networks for cross border capacity building. However, the systems for recognition and quality assurance are lagging behind and are currently not equipped to deal with these types of education and have problems handling international (cross-border) qualifications.
Conventional EarthObservation Payload Data Ground Segments (PDGS) continuously receive variable requests for data processing and distribution. However, their architecture was conceived to be on the premises of satellite operators and, for instance, has intrinsic limitations to offer variable services. In the current chapter, we introduce cloud comput- ing technology to be considered as an alternative to offer variable services. For that pur- pose, a cloud infrastructure based on OpenNebula and the PDGS used in the Deimos-2 mission was adapted with the objective of optimizing it using the ENTICE open source middleware. Preliminary results with a realistic satellite recording scenario are presented.
decision-making. Earthobservation from space sharpens our sensitivity to the natural environment and thus stimulates our willingness to learn of its relevance to everyday life conditions. It also make us aware of the need to use science and technology methods to obtain information for environmental monitoring. Private consultancies are developing courses on awareness of the potential of Earthobservation and geo-information in both the public and private sectors and for the general public. The accessibility and knowledge of Earthobservation improves the decision support and provides key information needed to promote economic vitality and environmental stewardship. Earthobservation contributes to solutions that result in socio-economic benefits to society.