Environmental Remote Sensing GEOG 2021 Lecture 8 Observing platforms & systems and revision

Sensors, systems and applications Polar orbiting v. geostationary High v. low spatial resolution High v. low temporal resolution Choice of spectral region –Optical (SW, NIR), passive microwave (TIR), active microwave (RADAR)

Remember trade-offs in space, time, wavelength etc. Global coverage means broad swaths, moderate-to-low resolution –Accept relatively low spatial detail for global coverage & rapid revisit times –Land cover change, vegetation dynamics, surface reflectance, ocean and atmospheric circulation, global carbon & hydrological cycle –E.g. MODIS (Terra, Aqua) (near-polar orbit) 250m to 1km, 7 bands across visible + NIR and some thermal Swath width ~2400 km So, revisit time of hours – 1 or 2 days, depends on location– coverage much denser at high latitudes than equator

Remember trade-offs in space, time, wavelength etc. Global coverage means broad swaths, moderate-to-low resolution –AVHRR (near-polar orbit) 1.1 to 5km, only 2 bands across visible + NIR, very broad bands (  m,  m) Swath width ~2900km Revisit time 2 daily Widely used in weather forecasting

Remember trade-offs in space, time, wavelength etc. Sea-WIFS –Designed for ocean colour studies –1km resolution, 2800km swath, 16 day repeat (note difference)

Remember trade-offs in space, time, wavelength etc. Can look away from optical in passive microwave (thermal) or active microwave (RADAR) –Passive e.g. ATSR 1 & 2 on ENVISAT Sea surface temperature –RADAR has benefit of all-weather, day or night E.g. SAR on ERS-2, 30m spatial, 100km swath, C-band, various polarisations ASAR on ENVISAT, 30m to 1km res., various swaths, C-band, 5 polarisations Change in Greenland ice sheet thickness over 11 years

Remember trade-offs in space, time, wavelength etc. Global coverage means broad swaths, moderate- to-low resolution –E.g. MERIS (near-polar orbit) ~300m, 15 bands across visible + NIR Swath width ~1100 km So, revisit time of hours – 2 days, depending on where on globe you are – coverage much denser at high latitudes than equator –METEOSAT 2 nd Gen (MSG) (geostationary orbit) 1km (equator) to 3km (worse with latitude) Views of whole Earth disk every 15 mins 30+ years METEOSAT data

Remember trade-offs in space, time, wavelength etc. MERIS image of Californian fires October 2007

Remember trade-offs in space, time, wavelength etc. MSG-2 image of Northern Europe “Mostly cloud free”

Remember trade-offs in space, time, wavelength etc. Local to regional –Requires much higher spatial resolution (< 100m) –So typically, narrower swaths (10s to 100s km) and longer repeat times (weeks to months) –E.g. Landsat (polar orbit) 28m spatial, 7 bands, swath ~185km, repeat time nominally 16 days BUT optical, so clouds can be big problem

Remember trade-offs in space, time, wavelength etc. SPOT 1-4 –Relatively high resolution instrument, like Landsat –20m spatial, 60km swath, 26 day repeat IKONOS, QuickBird –Very high resolution (<1m), narrow swath (10-15km) –Limited bands, on-demand acquisition

A changing world: Earth Palm Jumeirah, UAE Images courtesy GeoEYE/SIME

Summary Instrument characteristics determined by application –How often do we need data, at what spatial and spectral resolution? –Can we combine observations?? –E.g. optical AND microwave? LIDAR? Polar and geostationary orbits? Constellations?

Revision L1: definitions of remote sensing, various platforms and introduction to EM spectrum, atmospheric windows, image formation for optical and RADAR L2: image display and enhancement, histogram manipulation, colour composites (FCC, pseudoclour), image arithmetic (e.g. band ratios, NDVI, differences etc.)

Revision L3: spectral information - optical, vegetation examples, RADAR image characteristics, spectral curves, scatter plots (1 band against another), vegetation indices (perpendicular, parallel) L4: classification - producing thematic information from raster data, supervised (min. distance, max likelihood etc.) & unsupervised (ISODATA), confusion matrix.

Revision L5: spatial operators - convolution filtering, smoothing (low pass), edge detection (high pass, gradient filters). L6: Modelling 1 - types of model, physical v. empirical, deterministic & stochastic, empirical models relating biomass to backscatter, or NDVI L7: Modelling 2 - more types of model, population, regression, hydrological, calibration and validation, forward & inverse

References Global land cover & land cover change B. L. Turner, II*,, Eric F. Lambin, and Anette Reenberg The emergence of land change science for global environmental change and sustainability, PNAS 2007, Deforestation