1. EG2234: Earth Observation Interactions - Land Dr Mark Cresswell

2. Topics Land surfaces Temperature and radiance Proxy air temperature Albedo NDVI Fires and volcanoes

3. Land Surfaces Remote sensing provides a proxy for the nature of land surfaces We are interested in land cover rather than land use Different materials (land cover classes) absorb and reflect electromagnetic radiation differently We can infer land surface conditions from remotely sensed images

4. Temperature and Radiance Infrared radiation is the key A region of the electromagnetic spectrum sandwiched between the red visible and microwave portions of the spectrum IR radiation is invisible to Humans 3 – 14µm is TIR (0.7 – 1.3µm is NIR, 1.3 – 3µm is MIR, 10.5 - 12.5µm is TIR) Quantity of IR is related to radiance Radiance intensity is related to temperature

5. Temperature and Radiance Detection of infrared energy is performed by the radiometer Measurement is the result of a current effect due to the direct interaction of photons with electrons present in the detector Operation of the detector requires that incident photons liberate charge-carrier electrons

6. Temperature and Radiance Different materials in nature absorb and emit thermal energy differently Differences are due to thermal capacity and ability to conduct as well as environmental parameters Water often has a high thermal capacity compared with bare soil – so both materials are easily distinguished from one another

7. TIR image

8. Raw Meteosat Radiance image

9. Proxy Air Temperature LST (land surface temperature) may be converted to a proxy air temperature by means of a solar correction algorithm Knowing the position of the Sun at the time an image is acquired allows the LST to be transformed into a proxy for air temperature (approx 1m above the ground)

13. Albedo Albedo is simply the ratio of incoming radiation to reflected outgoing radiation expressed as %

14. Albedo Materials such as clouds and fresh snow have a HIGH albedo (they reflect much of the incident solar radiation back to space) Forests and soil have a low albedo (they absorb much of the incident radiation) We can classify materials according to their albedo

16. VISIBLE Albedo Weather Fcst.

17. NDVI Normalised Difference Vegetation Index NDVI provides a good assessment of photosynthesising vegetation – but caution must be exercised with this type of index as other factors can affect the NDVI other than leaf reflectance: Viewing angle, Soil background, Atmospheric degradation and Leaf orientation

18. INFRARED REGION

20. NDVI Often derived from NOAA-AVHRR satellite system (polar orbiter) Uses differential reflectance of visible and IR

21. NDVI

22. One of the problems associated with remote sensing vegetation is mixed pixels – a combination of both soil and vegetation Hutchinson (1982) found that it was difficult to separate soil and vegetation reflectance when vegetation cover is less than 30%. Solution is Perpendicular Vegetation Index (PVI) (Richardson and Wiegand, 1977)

23. PVI (perpendicular vegetation index): B = dark wet soils C = dry soils X = pure vegetation pixel Y = mixed pixel

24. Fires and Volcanoes Because of their thermal anomaly, fires and active volcanoes can be identified from space Acquisition of thermal infrared images allows fires to be detected automatically Time-series of images can show a trend in temperature beneath and surrounding a volcano that might allow a prediction of eruption to be made

25. Volcanoes The moderate resolution imaging spectroradiometer (MODIS) instrument on board the NASA EOS platform, Terra gives global coverage every 1-2 days at 250, 500 and 1000 metre resolutions Spectral data measured at 4µm and 12µm System examines scenes for high temperature volcanic thermal anomalies

26. Volcanoes Once identified, details such as location, emitted spectral radiance and other parameters are transferred via the internet to the Hawaii Institute of Geophysics and Planetology Algorithms appear to be robust at detecting both permanent and sporadically active volcanic systems. See Wright et al. 2002

27. Forest Fires A fire detection and management system should have the following aims: A measure of the geographical limits of the fire-front An estimate of fire intensity Monitoring of burnt area to look for latent fires Mapping of burnt areas to aid restoration (Barducci et al. 2002)

28. Borneo Fires (Sep 18 th 2005) NASA Earth Observatory, 2005