Image interpretation Aliens create Indian Head with an iPod ? Badlands Guardian (CBC) This feature can be found 180 miles SE of Calgary. 50° 1’ N 110° 7‘ W

Milestones in the History of Remote Sensing 19th century 1839 - Invention of photography use of balloons and kites 1858 first aerial photograph from a captive balloon pigeons oblique photos (mountain peaks) Stereo photography - http://www.londonstereo.com/

20th century 1910s First use of aerial photography from planes (World War I: photo interpretation) 1920s Development of photogrammetry for mapping Military use of radar (World War II) -> Main aerial photo programs in Canada and others 1950s Use of colour photography and infra-red Term 'remote sensing' first appeared (Evelyn Pruit) First reconnaissance satellites: Corona Wikipedia Cool Photos 1960s First weather satellites: Tiros (1960); Nimbus (1964) (and first digital data transfer)

Reconnaissance satellite: Corona 1959-72 ~ 7.5m photos Declassified for environmental studies 2000s Pentagon Mt. Ararat

Multispectral image processing: The Landsat Era Launch of Landsat 1 Nasa USGS satellite and the 80metre MultiSpectral Sensor (MSS) Landsat 4: the 'next generation 30metre sensor': Thematic Mapper (TM) [and Landsat 5 in 1984] SPOT-1 satellite: 10 and 20metre data (France) 1988: India IRS 1A

1990s 1990s Other countries' satellites / sensors: JERS: Japan ERS: Europe RESURS: USSR 1995 Radarsat- Canada's first remote sensing satellite 1999 Landsat 7 Enhanced Thematic Mapper (ETM); - Reduction of data cost removal of data copyright - enabled online data libraries

New millennium events Many new sensors including very high resolution 2000 Terra satellite: ASTER and MODIS data 2000 High resolution private sector satellites: Ikonos (2000) and Quickbird (2001) ………many more -> present Online viewers 2005 Online image viewers- e.g. google earth Free high resolution image data 2009 Landsat image data archive goes online   (free) 2013 Landsat 8

Satellite Image interpretation – manually uses the same factors as Air photos Colour Tone Texture Pattern Shape Shadow Size Association Multispectral signature e.g. water reflects no IR

Shape: the form of an object on an air photo helps to identify the object. Regular uniform shapes often indicate a human involvement; Pattern: similar to shape, the spatial arrangement of objects (e.g. row crops vs. pasture) is also useful to identify an object and its usage;

Shadow: a shadow provides information about height, shape, and orientation Texture: the physical characteristics of an object affects how they appear (e.g. calm water has a smooth texture; a forest canopy has a rough texture); PCI: ‘TEX’

Size: a measure of the object's surface area (e.g. single-lane vs. multi-lane highways); Time: temporal characteristics of a series of photographs can be helpful in determining the historical change of an area (e.g. looking at a series of photos of a city taken in different years can help determine the growth of suburban neighbourhoods;

Association/Site: associating the presence of one object with another, or relating it to its environment, can help identify the object (e.g. industrial buildings often have access to railway sidings; Tone/Colour: the colour characteristics of an object, relative to other objects in the photo (e.g. sand has a bright tone, while water usually has a dark tone; tree species can be determined by the colour of their leaves at certain times of the year);

Differences with satellite images (compared to aerial photographs) Spatial resolution Shape and pattern

Landsat Air photo

Image fusion / pansharpening Goal: Combine higher spatial information in one band with higher spectral information in others to create ‘synthetic’ higher resolution multispectral images

Shadows: usually from the SE (~10am)

Shadows: (almost) always from the SE Deriba Caldera, Sudan: from the Space Station

Great Barrier Reef, Australia

Tasmania

Bowron Lakes Colour: 3,2,1 composite (e.g. google maps)

Colour: 5,4,3 composite

Histograms: Prince George scene Landsat 3-2-1 5-4-3

Air photos: Colour Tone Texture Pattern Shape Shadow ** Size Association ** Digital Images Multispectral signature e.g. water reflects no IR

Reflectance of water based on dissolved solids

Clouds and snow

Reflection in visible / near IR / midIR Water: highest in blue, medium in green/red, very low in IR Snow/ice: high in visible, almost as high in near IR, low in midIR Less green 3,2,1 composite = less green vegetation Less green 5,4,3 composite = lower healthy vegetation (Near IR) Vegetation – coniferous and deciduous are similar in green/red (summer) Deciduous reflects higher in near and mid IR

Reflection in visible / near IR / midIR low-medium-high -> screen colours Dark green: coniferous Bright green: deciduous Pink: arable / agricultural Light purple: residential / built up Black: deep water Blue: shallow or sediment laden water White: Clouds Pink-Red: bare rock Light blue/green: ice/snow

Enhancement based on screen display Based on whole scene Based on zoom into UNBC campus Screen enhancement does NOT affect digital numbers