Remote Sensing

Remote Sensing requires the following: 1. Electromagnetic Energy Source 2. Interaction with a Target 3. Sensor to Record Energy 4. Transmission, Reception and Processing 5. Interpretation 6. Application Electromagnetic Energy Source Illuminates or provides electromagnetic radiation to the target of interest

A - Source of Electromagnetic Radiation B - Radiation comes into Contact with Atmosphere C - Radiation Interacts with Target D - Sensor Collects and Records Electromagnetic Radiation F - Processed Image Interpreted to Extract Target Info E - Recorded Energy Transmitted to Processing Station (for copy) G - Application

Radiation Interacts with Atmosphere Radiation interacts with the atmosphere on the way to the target and as the energy travels from the target to the sensor Interaction with a Target Radiation interacts with target. The nature of this interaction is dependent on the wavelength of the radiation and the nature of the target Sensor A sensor (mounted on satellite/plane/helicopter) collects/records the electromagnetic radiation scattered or emitted by the target Transmission and Processing Recorded energy is transmitted to a processing station to produce an image saved in digital format (or hardcopy)

* Remote sensing is especially important for Interpretation Visual interpretation or digital (GIS) interpretation to extract further information about the target Application Information applied to solve a problem * Remote sensing is especially important for extracting information from harsh environments or difficult terrain

Passive Sensors Measure naturally-available energy (eg. thermal infrared radiation emitted from the Earth 24 hours per day, but solar reflected radiation only during solar day) Active Sensors Sensor emits radiation toward target Reflected radiation in emitted bands are detected and measured (eg. microwaves emitted)

Characteristic spectral responses of different surface types Characteristic spectral responses of different surface types. Bands are those of the SPOT remote sensing satellite.

Images and Photographs Representation in digital format by subdividing image into equally- shaped areas called pixels The ‘brightness’ of each area can be attributed a numeric value or digital number Information from narrow wavelength ranges can be stored in channels, also called bands Often, data from multiple channels can be represented as one of three primary colours which combine according to brightness. We are, thus, no longer blind to these ’s.

Orbits and Swaths Geostationary orbits: Very high altitude satellites (approximately 36 000 km) Focus on the same area of the Earth at all times Continual data collection over a specific area Eg. Weather and communications satellites Near-polar orbits Satellite travels northward on one side of the Earth and then southwards during the second half of its orbit In sun-synchronous orbits, ascending path can be on a shadowed side with the descending path on the sunlit side. Passive sensors would only record data during the descent.

Swath The area imaged on the surface. Swaths vary from very small areas (helicopters and planes) to hundreds of kilometres (spaceborne satellites) Earth rotates: Satellite swath may cover new area with each pass Complete coverage of Earth after one cycle of orbits Areas at high latitude generally covered more frequently Spatial Resolution Size of the smallest possible feature that can be detected Instantaneous Field of View (IFOV) is the angular cone of visibility of the sensor (See A at right) This, along with altitude (C), determines the area visible on the ground (B)

Examples of Remote Sensing Satellites Each has multiple channels for specific purposes 1. Weather GOES (Geostationary Operational Environmental Satellite) NOAA AVHRR (Advanced Very High Resolution Radiometer) 2. Land Surface Observation Landsat (NOAA) SPOT (Système Pour l’Observation de la Terre) IRS (Indian Remote Sensing) MEIS-II and CASI (Airborne Sensors) 3. Marine Observation CZCS (Coastal Zone Colour Scanner) MOS (Marine Observation Satellite) SeaWiFS (Sea-viewing Wide Field of View Sensor)

Applications of Remote Sensing There are many applications of remote sensing, most of which are related to Geography as a discipline Agriculture: Crop type, condition and yield, soil characteristics Forestry: Type, health, biomass, burning, species, deforestation Hydrology: Sea ice, navigation, oil spills, sea surface temperature Land Use: Resource management, habitat protection, urban sprawl, damage assessment, legal boundaries Oceans: Currents, winds, waves, phytoplankton concentration, temperature monitoring, navigation routing, traffic density, bathymetry, land-water interface delineation, coastal vegetation Mapping: Digital Elevation Models (DEM’s), thematic mapping

AVHRR Visible, NIR, Thermal 1.1 km Resolution - local area coverage (LAC) 4 km Resolution - global area coverage (GAC) Used for meteorological studies Vegetation pattern analysis Global modeling Broad spectral bands

LANDSAT Thematic Mapper Sun-synchronous, near-polar orbit, imaging the same 185 km x 0.474 km ground swath every 16 days Global coverage between 81 degrees north latitude and 81 degrees south latitude Particularly useful in determining land use classes Blue/Green, Green, Red, NIR, MIR, Thermal 30 meter resolution 256 brightness values 7 spectral bands

Normalized Difference Vegetative Index (NDVI) NDVI = (NIR - red) / (NIR + red)

RADAR - Radio Detection and Ranging Passive Microwave Sensors: Applications include meteorology (atmosphere profiles, water and ozone content), hydrology (soil moisture) and oceanography (sea ice, currents, oil slicks) Active Microwave Sensors: RADAR - Sensor transmits a microwave (or radio) signal toward a target and detects the backscattered portion of the signal Strength of backscattered signal discriminates between targets Time delay between transmitted and reflected signals determines the distance to the target Non-Imaging (eg. altimeters) or Imaging Sensors Imaging Microwave Sensors include RADARSAT (Canada, 1995) RADARSAT, developed by the Canadian Space Agency, is the world’s first, operationally-oriented radar satellite system capable of rapid delivery of large quantities of data

Image Processing 1. Preprocessing Radiometric and geometric corrections 2. Image Enhancement Improving contrast, and spatial filtering to enhance specific spatial patterns of interest 3. Image Transformations Combined processing of multiple spectral bands for image enhancement 4. Image Classification and Analysis Digital identification and classification of pixels. Classification: Assigns each pixel to a particular class or theme based on desired statistical characteristics (supervised or unsupervised)

Before GIS: Popularity of stack maps Limitation: Restricted to consistent scale, projection and coverage area

Advantage of Digital Overlay: 1. Faster 2. Scale, projection and coverage area less problematic (Most applications consist of sources collected by different methods and at different scales) 3. Time and error associated with manual integration and redrafting eliminated Raster or Vector Implementation

Raster Implementation of Overlay

Overview of Overlay Analysis 1. Three maps represented with a common grid 2. Binary maps converted with Boolean operators such as AND and OR Eg. Suitability Analysis AND = more than one condition must occur simultaneously OR = identifies areas with either condition met

Boolean Logic in Raster Overlay RECLASS OVERLAY It is often useful to RECLASS your data before performing OVERLAY

Task: Given vegetation map and elevation map, isolate a vegetation type within a particular altitude range Map 1: Vegetation Map (VEGMAP) Map 2: Digital Elevation Model (DEM1)

This requires the use of the AND operation STEP 1: Our vegetation of interest is class 6. Reclass to assign a value of 1 for all values from 6 to just less than 7. All other values are assigned a value of zero. CLASS6

STEP 2: Use the Digital Elevation Model to isolate the elevations between 1700 and 1800 m.a.s.l. In the same way, assign a value of 1 to all values from 1700 to just less than 1801 by using a reclass function. DEM1718

Where do the two isolated characteristics coincide ? Use OVERLAY Multiply file CLASS6 by DEM1718 to produce the output map RESULT. Only pixels with a value of 1 survive to be represented in the output file. RESULT

OVERLAY is often used in combination with other operations such as near-neighbour operations Eg. Produce a map of riparian vegetation cover within 100 metres of rivers and streams Locate buffer zone 100m from rivers Overlay with vegetation map Produce resultant map of riparian cover

Vector Implementation of Overlay Produces many new polygons due to overlapping Each new polygon has a unique, new identifier The identifier is linked to an attribute table Result is a single layer coverage linked to all attributes