Earth Observation in the frame of EO-MINERS - Overview of remote sensing methods, sensors and applications
Remote sensing application
Agriculture - Satellite and airborne images are used as mapping tools to classify crops, examine their health and viability, and monitor farming practices. Agricultural applications of remote sensing include the following (CCRS: Tutorial: Fundamentals of Remote Sensing):
- crop type classification
- crop condition assessment
- crop yield estimation
- mapping of soil characteristics
- mapping of soil management practices
- compliance monitoring (farming practices)
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Salinity mapping in agriculturas fields in Uzbekistan (source: TAU) |
Forestry - Forestry applications of remote sensing include the following:
Reconnaissance mapping: Objectives to be met by national forest/environment agencies include forest cover updating, depletion monitoring, and measuring biophysical properties of
- forest stands.
- forest cover type discrimination
- agroforestry mapping
Commercial forestry: Of importance to commercial forestry companies and to resource management agencies are inventory and mapping applications: collecting harvest information, updating of inventory information for timber supply, broad forest
- type, vegetation density, and biomass measurements.
- clear cut mapping / regeneration assessment
- burn delineation
- infrastructure mapping / operations support
- forest inventory
- biomass estimation
- species inventory
Environmental monitoring: Conservation authorities are concerned with monitoring the quantity, health and diversity of the Earth's forests.
- deforestation (rainforest, mangrove colonies)
- species inventory
- watershed protection (riparian strips)
- coastal protection (mangrove forests)
- forest health and vigor
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LiDAR-derived above ground carbon content for a UK deciduous woodland (source: UK Forestry Comission) |
Geology - Remote sensing is used as a tool to extract information about the land surface structure, composition or subsurface, but is often combined with other data sources providing complementary measurements. Multispectral data can provide information on lithology or rock composition based on spectral reflectance. Radar provides an expression of surface topography and roughness, and thus is extremely valuable, especially when integrated with another data source to provide detailed relief.
Geological applications of remote sensing include the following:
- surficial deposit / bedrock mapping
- lithological mapping
- structural mapping
- sand and gravel (aggregate) exploration/ exploitation
- mineral exploration
- hydrocarbon exploration
- environmental geology
- geobotany
- baseline infrastructure
- sedimentation mapping and monitoring
- event mapping and monitoring
- geo-hazard mapping
- planetary mapping
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ASTER multispectral sensor false colour composite image emphasising geological features inYemen (http://www.satimagingcorp.com/) |
Hydrology - Remote sensing offers a synoptic view of the spatial distribution and dynamics of hydrological phenomena, often unattainable by traditional ground surveys. Radar has brought a new dimension to hydrological studies with its active sensing capabilities, allowing the time window of image acquisition to include inclement weather conditions or seasonal or diurnal darkness.
Examples of hydrological applications include (CCRS: Tutorial: Fundamentals of Remote Sensing):
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Sea ice - Remote sensing data can be used to identify and map different ice types, locate leads (large navigable cracks in the ice), and monitor ice movement. With current technology, this information can be passed to the client in a very short timeframe from acquisition. Users of this type of information include the Coast Guard, port authorities, commercial shipping and fishing industries, ship builders, resource managers (oil and gas / mining), infrastructure construction companies and environmental consultants, marine insurance agents, scientists, and commercial tour operators.
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Antarctic sea ice concentration, ranging from 0 percent (purple) to 100 percent (white) on 07 August 2004. Antarctica is shown in grey, and the unfrozen ocean is shown in dark blue. Sea ice concentration was calculated from data measured by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) sensor aboard NASA's Aqua satellite.
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Examples of sea ice information and applications include (CCRS: Tutorial: Fundamentals of Remote Sensing):
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Land cover and Land use - Resource managers involved in parks, oil, timber, and mining companies, are concerned with both land use and land cover, as are local resource inventory or natural resource agencies. Changes in land cover will be examined by environmental monitoring researchers, conservation authorities, and departments of municipal affairs, with interests varying from tax assessment to reconnaissance vegetation mapping. Governments are also concerned with the general protection of national resources, and become involved in publicly sensitive activities involving land use conflicts.
Land use applications of remote sensing include the following (CCRS: Tutorial: Fundamentals of Remote Sensing):
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Land cover classification of Sope Creek watershed based on the Advanced Terrestrial Land Applications Sensor (ATLAS) airborne remote sensing instrument (source: http://wwwghcc.msfc.nasa.gov/land/ncrst/atlasclass.html) |
Oceans & Coastal Monitoring - Coastlines are environmentally sensitive interfaces between the ocean and land and respond to changes brought about by economic development and changing land-use patterns. Often coastlines are also biologically diverse inter-tidal zones, and can also be highly urbanized. With over 60% of the world's population living close to the ocean, the coastal zone is a region subject to increasing stress from human activity. Government agencies concerned with the impact of human activities in this region need new data sources with which to monitor such diverse changes as coastal erosion, loss of natural habitat, urbanization, effluents and offshore pollution. Many of the dynamics of the open ocean and changes in the coastal region can be mapped and monitored using remote sensing techniques.
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This satellite image depicts a daily snapshot of fall surface water temperature patterns on the Northeast U.S. continental shelf. Cooler temperatures are represented by darker colors shading to blue. Warmer temperatures, such as those associated with the Gulf Stream are represented by the warmer colors shading to red.
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Ocean applications of remote sensing include the following (CCRS: Tutorial: Fundamentals of Remote Sensing):
- Ocean pattern identification:
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- currents, regional circulation patterns, shears frontal zones, internal waves, gravity waves, eddies, upwelling zones, shallow water
- bathymetry
- Storm forecasting
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- wind and wave retrieval
- Fish stock and marine mammal assessment
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- water temperature monitoring
- water quality
- ocean productivity, phytoplankton concentration and drift
- aquaculture inventory and monitoring
- Oil spills
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- mapping and predicting oil spill extent and drift
- strategic support for oil spill emergency response decisions
- identification of natural oil seepage areas for exploration
- Shipping
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- navigation routing
- traffic density studies
- operational fisheries surveillance
- near-shore bathymetry mapping
- Intertidal zone
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- tidal and storm effects
- delineation of the land /water interface
- mapping shoreline features / beach dynamics
- coastal vegetation mapping
- human activity / impact
Atmosphere monitoring - Measurements and observations of the atmosphere (and especially the troposphere) are the most important pre-requisite to our understanding of weather and climate. Numerical models of the atmosphere have revolutionized the preparation of weather forecasts, although rather than reducing the need for observations such models have increased awareness of the importance of data through assimilation schemes. Indeed, the accuracy of forecasts relies crucially upon how well the initial state of the atmosphere can be described and this requires detailed measurements throughout the entire depth of the atmosphere.
Over the last half century, the increasing availability of low cost computers and sensors has enabled a move away from a reliance on the collection of weather data at traditional sites and enclosures. However, perhaps the greatest contribution to improving accuracy in weather prediction and monitoring is the advent of new observing systems based on satellite and airborne platforms. These technologies have completely revolutionized the networking of conventional meteorological instrumentation and have facilitated a colossal advance in both the spatial and temporal scale of weather measurement (Chapman et. al 2011)
Satellite systems provide a unique opportunity to monitor Earth-atmosphere system processes and parameters continuously. In view of the great benefit provided by spaceborne Earth-atmosphere remote sensing, there were strong efforts to construct Earth observing satellite systems in the past. Satellite based observations of the Earth and the atmosphere started with the first meteorological satellite, the Television InfraRed Observation Satellite (TIROS-1), launched in 1960. During the following decades several satellite systems with different sensors provided data for a wide range of atmospheric parameters that enhanced our understanding of Earth-atmosphere processes and dynamics. Nowadays, operational satellite systems provide invaluable measurements of atmospheric parameters at regular intervals on a global scale (Thies and Bendix, 2011).
Meteorological parameters measured by remote sensing
- Radiation: Radiation energy and its spatio-temporal distribution is the driver for atmospheric dynamics. To understand weather and climate, measurements of the radiation that enters and leaves the Earth-atmosphere system are necessary.
- Surface temperature: Retrievals of the sea surface temperature (SST) and land surface temperature (LST) from space provide information for interactions between ocean/land and atmosphere such as evaporation processes and boundary layer dynamics.
- Wind: Wind fields derived from satellites provide continuous area-wide information about atmospheric dynamics in a high spatial and temporal resolution. Such information is of great benefit as an input parameter for numerical weather prediction. Thus, atmospheric motion vectors, derived by tracking atmospheric features (e.g. clouds or water vapour) with satellites were one of the first satellite data products assimilated in global numerical weather prediction.
- Water vapor: Water vapour is the principal greenhouse gas in the atmosphere and a key compound of the global climate. It is important for many atmospheric processes, such as radiative transfer, circulation dynamics, cloud formation, precipitation and the greenhouse effect. Information about the distribution and variability of atmospheric water vapour is critical for understanding these processes controlling the Earth radiative budget and the hydrological cycle.
- Gasses: As a response on the increasing human impact on the evolution of the global climate and on the stratospheric ozone layer much effort has been made to understand the underlying chemical and physical processes and the role of anthropogenic gas emissions. To fulfill this objective there is a clear need for global observation of gas emissions and concentrations in the Earth atmosphere system.
- Aerosols: Aerosols in the troposphere are a major climate forcing parameter, due to the direct and indirect aerosol effect. Despite this importance there are still significant uncertainties concerning the physical and optical properties of tropospheric aerosols and their interaction with global climate. This is mainly due to the inadequate quantitative knowledge of global aerosol characteristics and their temporal variability. To evaluate the aerosol radiative effects together with the magnitude and the potential variability of the aerosol climate forcing it is therefore essential to monitor aerosols on the global scale.
- Clouds – identification and properties: identifying clouds in satellite imagery is an important first step in the retrieval of both surface and atmospheric properties. In the past, various cloud classification techniques have been developed for the different satellite systems and for a variety of purposes. Typical cloud parameters that can be derived from satellite data and that are useful for such investigations comprise cloud-top height, cloud optical thickness, cloud effective particle radius, cloud liquid water path and cloud phase.
- Precipitation: Precipitation is a key factor of the global water cycle and affects all aspects of human life. Because of its great importance and its high spatial and temporal variability, the correct spatio-temporal detection and quantification of precipitation has been one of the main goals of meteorological satellite missions. Precipitation retrieval from satellite data can provide area-wide information in regions for which data from rain gauge or radar networks are sparse or unavailable (Thies and Bendix, 2011).
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Cloud Classification image based on an Infrared geostationary satellite (source: http://www.satmos.meteo.fr/html_en/Archive_CT_MSG.html). |
