Draft:Earth Observation in place and space

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Earth Observation in place and space (EOIPS) is a multidisciplinary field of science that uses various methods and technologies to collect, analyze and present information about the Earth's systems from different perspectives and scales. EOIPS encompasses both space-based and ground-based observations, as well as airborne and underwater platforms, that can measure physical, chemical and biological properties of the Earth and its surroundings. EOIPS can be applied to a wide range of domains, such as environmental monitoring, natural resource management, disaster response, climate change, urban planning, security and defense, education and outreach.

History

The history of EOIPS can be traced back to the early attempts of observing the Earth from high altitudes using balloons, rockets and aircrafts. The first space-based image of the Earth was taken in 1947 by a camera mounted on a V-2 rocket launched from New Mexico. In 1957, the Soviet Union launched Sputnik 1, the first artificial satellite, which initiated the space age and sparked a global interest in space exploration and observation. In 1960, the United States launched TIROS-1, the first weather satellite, which demonstrated the feasibility and usefulness of monitoring the Earth's atmosphere from space. Since then, numerous satellites have been launched for various EOIPS purposes, such as land surface mapping, oceanography, geodesy, meteorology, astronomy and astrophysics.

Methods and technologies

EOIPS relies on various methods and technologies to acquire data about the Earth's systems. These include:

• Remote sensing: The process of obtaining information about an object or phenomenon without making physical contact with it. Remote sensing can use electromagnetic radiation (such as visible light, infrared, microwave or radio waves), sound waves (such as sonar) or gravity fields (such as gravimetry) to detect and measure the properties of the target. Remote sensing can be performed by sensors mounted on satellites, aircrafts, drones, balloons or other platforms.

• In situ sensing: The process of obtaining information about an object or phenomenon by making direct contact with it or by placing sensors within or near it. In situ sensing can use various instruments (such as thermometers, barometers, hygrometers, pH meters or spectrometers) to measure the properties of the target. In situ sensing can be performed by sensors deployed on the ground, in the water or on other platforms.

• Data fusion: The process of combining data from multiple sources (such as remote sensing and in situ sensing) to produce a more comprehensive and accurate representation of the target. Data fusion can use various techniques (such as image processing, statistical analysis or machine learning) to integrate and analyze the data. Data fusion can enhance the spatial, temporal or spectral resolution of the data, reduce noise and uncertainty, fill gaps and detect anomalies.

• Data visualization: The process of presenting data in a graphical or pictorial form that makes it easier to understand and communicate. Data visualization can use various methods (such as maps, charts, graphs or images) to display the data. Data visualization can highlight patterns, trends or relationships in the data, reveal insights or discoveries, support decision making or storytelling.

Applications

EOIPS can be applied to a wide range of domains that require information about the Earth's systems for various purposes. Some examples of EOIPS applications are:

• Environmental monitoring: The systematic observation and assessment of the state and changes of the natural environment (such as land cover, vegetation, water quality, air quality or biodiversity). Environmental monitoring can support environmental protection, conservation and restoration efforts; detect and evaluate environmental impacts; identify environmental risks and hazards; inform environmental policies and regulations; raise environmental awareness and education.

• Natural resource management: The efficient and sustainable use of natural resources (such as water, soil, minerals or energy). Natural resource management can support resource exploration,

This page will be placed in the following categories if it is moved to the article namespace.

Categories:

• AKI Africa environment 2023

References

1. ^ Andries, A.; Morse, S.; Murphy, R.J.; Lynch, J.; Woolliams, E.R. Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators. Sustainability 2019, 11, 5062. https://doi.org/10.3390/
2. ^ Boedecker, G.; Schreiber, U. (eds.) Observation of the Earth System from Space. Springer: Berlin, Heidelberg, 2005. ISBN
3. ^ Dittus, A.; Strobl, P.; Esch, T.; Asamer, H.; Balhar, J.; Boettcher, M.; Boisserée, A.; Mathieu, P.-P.; Metz-Marconcini, A.; Pacini, F. et al. Bringing Earth Observation to Schools with the Geo:spektiv E-Learning Platform. Remote Sens. 2020, 12, 3117. https://doi.org/10.3390/
4. ^ Jensen, J.R. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson Education: Upper Saddle River, NJ, USA, 2007. ISBN
5. ^ Lillesand, T.M.; Kiefer, R.W.; Chipman, J.W. Remote Sensing and Image Interpretation. John Wiley & Sons: Hoboken, NJ, USA, 2015. ISBN
6. ^ Liu, S.C.; Shawe-Taylor J.; Principe J.C.; Giles C.L.; Kasabov N.K. (eds.) Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference. MIT Press: Cambridge MA; London UK; 2008. ISBN
7. ^ Munzner T. Visualization Analysis and Design. CRC Press: Boca Raton FL; London UK; New York NY; 2014. ISBN
8. ^ NRC (National Research Council). Environmental Data Management at NOAA: Archiving Stewardship and Access. The National Academies Press: Washington DC; 2007. ISBN
9. ^ OGC (Open Geospatial Consortium). OGC® Sensor Web Enablement: Overview and High Level Architecture; OGC White Paper OGC 07–165; OGC: Wayland MA;
10. ^ Turner II B.L., Kasperson R.E., Matson P.A., McCarthy J.J., Corell R.W., Christensen L., Eckley N., Kasperson J.X., Luers A., Martello M.L., Polsky C., Pulsipher A., Schiller A.. A framework for vulnerability analysis in sustainability science. Proc Natl Acad Sci U S A. 2003 Jul 8;100(14):8074–8079 https://doi.org/10.1073/pnas.1231335100