eg2234 earth observation applications of remote sensing

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EG2234 Earth Observation Applications of Remote sensing

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EG2234Earth Observation

Applications of

Remote sensing

TOPICS

Quantitative remote sensing Rainfall estimation Land surface temperature Proxy air temperature NDVI, albedo, wind-speed and others Disaster Management Human Health Hydrodynamics

Quantitative remote sensing?

Estimation of a physical quantity Proxy environmental variables Application driven Less science and more operational Makes use of algorithms Interfaces with environmental models

Applications that use quantitative RS

Agriculture– NDVI, temperature, rainfall

Health– NDVI, temperature, rainfall, dust, wind

Hydrology– Rainfall

Climate change– NDVI, temperature, rainfall

Weather forecasting– Winds, rainfall

Rainfall estimation

Cold Cloud Duration (CCD) using Meteosat Tropical Rainfall Measuring Mission using

radar (TRMM) Special Sensor Microwave Imager (SSM/I)

rainfall measurement using microwave instruments

Land Surface Temperature

Thermal infrared images provide an estimate of the magnitude of radiant energy

Radiance (usually expressed as watts per square metre) can be converted to temperature via an instrument-specific algorithm

Energy (and hence temperature) is of the land surface (LST)

LST may be converted to a proxy air temperature by means of a solar correction algorithm

Other quantitative measurements

NDVI Albedo Wind speed Potential Evapotranspiration (PET) Soil moisture Tropospheric humidity

NDVI

•Monitoring•Habitat modelling•Hydrology

VISIBLE

•Albedo•Weather Fcst.

WATERVAPOUR

•Cloud motion•Troposphere

Disaster Management

Uses of RS for Disaster Management

Wildfires Volcanic eruptions Avalanche Tsunami Earthquake Landslides Flooding Extreme weather Drought Disease Refugees Military

Disaster Management

PLANNING MITIGATION

ModellingAssessmentPredictionContingency

Monitoring situationsDeployment of resourcesDecision-makingPublic relations

COST EFFECTIVENESS !!!

QuickBird used extensively throughout Asian Tsunami Disaster

Human Health

Health and disease often has a spatial component

Climatic, environmental and socio-economic variables affect health

Epidemics and outbreaks spread across a region – either as a function of movement of people or environmental factors

Hydrodynamics

stream stream

stream

stream

riverriver river

estuary

THE SEA

Hydrodynamics

From DeMers, 2002

Hydrodynamics

From DeMers, 2002

Further Reading

Cresswell MP, Morse AP, Thomson MC and Connor SJ. (1999). Estimating surface air temperatures from Meteosat land surface temperatures using an empirical solar zenith angle model. International Journal of Remote Sensing, Vol 20 (6), 1125-1132.

Lethbridge M. (1967). Precipitation probability and satellite radiation data. Monthly Weather Review, Vol 95 (7), 487-490

Milford J and Dugdale G. (1990). Estimation of rainfall using geostationary satellite data. In Applications of Remote Sensing in Agriculture. Edited by Steven M and Clark J. Published by Butterworths, London

Dugdale G, Hardy S and Milford J. (1991). Daily catchment rainfall estimated from Meteosat. Hydrological Processes, Vol 5, 261-270

Further Reading

TRMM Website:

http://www.eorc.nasda.go.jp/TRMM/index_e.htm

SSM/I Website:

http://nsidc.org/data/docs/daac/ssmi_instrument.gd.html/

TAMSAT (CCD Rainfall) Website:

http://www.met.reading.ac.uk/tamsat/