Spectral Signatures and Image Interpretation

 The Electromagnetic Spectrum:

• The electromagnetic spectrum ranges from the shorter wavelengths (including gamma and x-rays) to the longer wavelengths (including microwaves and broadcast radio waves).

• There are several regions of the electromagnetic spectrum which are useful for remote sensing.

Figure 1: The Electromagnetic Spectrum

Image Interpretation:

•  Analysis of remote sensing imagery involves the identification of various targets in an image.

•  Targets may be defined in terms of the way they reflect or emit radiation.

•  This radiation is measured and recorded by a sensor, and ultimately is depicted as an image product such as an air photo or a satellite image.

•  Act of examining images to identify objects and judge their significance.

•  Information extraction process from the images.

•  An interpreter is a specialist trained in study of photography or imagery, in addition to his own discipline.

•  Involves a considerable amount of subjective judgment.

•  Image is a pictorial representation of an object or a scene.

•  Image can be analog or digital.

•  A digital image is made up of square or rectangular areas called pixels.

•  Each pixel has an associated pixel value which depends on the amount reflected energy from the ground.

Figure 2: Image Structure

Figure 3: Image Pixel Value

Figure 4: Hyperspectral Cube

Figure 5: Band Combination

What makes interpretation of imagery more difficult than the everyday visual interpretation of our surroundings?

• We lose our sense of depth when viewing a two dimensional image, unless we can view it stereoscopically so as to simulate the third dimension of height.

• Viewing objects from directly above also provides a very different perspective than what we are familiar with.

• Combining an unfamiliar perspective with a very different scale and lack of recognizable detail can make even the most familiar object unrecognizable in an image.

• Finally, we are used to seeing only the visible wavelengths, and the imaging of wavelengths outside of this window is more difficult for us to comprehend.

• Spectral resolution = part of the EM spectrum measured.

• Radiometric resolution = smallest differences in energy that can be measured.

• Spatial resolution = smallest unit area measured.

• Revisit time (temporal resolution) = time between two successive image acquisitions over the same area.

Advantages of Using Images over ground observation:

• Synoptic view

• Time freezing ability

• Permanent record

• Spectral resolution

• Spatial resolution

• Cost and time effective

• Stereoscopic view

• Brings out relationship between objects

Spectral Signature:

•  Identity is whatever makes an entity recognizable.

•  A signature is that which gives an object or piece of information its identity.

•  Characteristic feature which forms key to enable an object to be identified.

•  Spectral, Spatial, temporal and polarization variations which facilitate discrimination of features on remotely sensed data.

What is a spectral reflectance curve:

A spectral reflectance curve is a graph of the spectral reflectance of an object as a function of wavelength and is very useful for choosing the wavelength regions for remotely sensed data acquisition for a certain application.

Figure 6: spectral reflectance curve

Significance of spectral signature in remote sensing:

• Spectral responses measured by RS sensors over various features.

• Spectral reflectance & spectral emittance curves.

• Variability of spectral signature: useful for evaluation of condition, not for spectral identification of earth features.

• Temporal and spatial effects on spectral response patterns.

• Change detection depends on temporal effects.

Spectral Signature for Vegetation:

•  A general characteristic of vegetation is its green colour caused by the pigment chlorophyll.

•  Chlorophyll reflects green energy more than red and blue energy, which gives plants green color.

Figure 7: Vegetation Reflection

Figure 8: Spectral Signature for Vegetation

Figure 9: Spot image

Figure 10: IKONOS image

•  The major difference in leaf reflectance between species, are dependent upon leaf thickness.

•  It affects both pigment content and physiological structure.

Figure 11: Vegetation Reflection

Figure 12: Thick leaf

Figure 13: Thin leaf

•  Leaf reflectance is reduced as a result of absorption by three major water absorption bands that occur near wavelengths of 1.4 micrometer, 1.9 m and 2.7 micrometer and two minor water absorption bands that occur near wavelengths of 0.96 micrometer, and 1.1 micrometer

Figure 14: Leaf spectral reflectance signatures in terms of moisture content

Needle-leaf trees canopies reflect significantly less near-infrared radiation compared to broad-leaf vegetation.

Figure 15: Coniferous forest

Figure 16: Deciduous forest

Immature leaves contain less chlorophyll and fewer air voids than older leaves, they reflect more visible light and less infrared radiation.

Figure 17: Mature plant

Figure 18: Immature plant

Figure 19: Leaf Maturity Signature

Reflectance is also affected by health of vegetation

Figure 20: Reflectance is also affected by health of vegetation

Spectral Signature for Soil:

The five characteristics of a soil that determine its

reflectance properties are, in order of importance:

• Moisture content

• Organic content

• Structure

• Iron oxide content

• Texture

Figure 21: Soil Reflection

Soil Moisture:

•  A wet soil generally appears darker

•  Increasing soil moisture content lowers reflectance but did not change shape of the curve

Figure 22: Dry Soil

Figure 23: Wet Soil

Figure 24: Soil Moisture Signature

Organic content:

•  A soil with 5% or more organic matter usually appears black in colour

•  Less decomposed organic materials have higher reflectance in the near ir

•  Very high decomposed organic materials show very low reflectance throughout the reflective region of the solar spectrum

Figure 25: Organic Content of soil

Soil – Iron Content:

•   The presence of iron especially as iron oxide affects the spectral reflectance

•   Reflectance in the green region decreases with increased iron content, but increases in the red region

•   Iron dominated soils have strong absorption in Mir (> 1.3 micrometer)

Figure 26: Spectral Reflectance curve of Soil-Iron

Representative reflectance spectra of surface samples of 5 minerals soils; (a) High organic content, moderately fine texture; (b) Low organic, Low iron content; (c) Low organic, medium iron content; (d) High organic content, moderately coarse texture and (e) High iron content, fine texture.

Soil structure:

•  A clay soil tends to have a strong structure, which leads to a rough surface on ploughing; clay soils also tend to have high moisture content and as a result have a fairly low diffuse reflectance.

•  Sandy soils also tend to have a low moisture content and a result have fairly high and often specular reflectance properties.

Figure 27: Clayey soil

Figure 28: Sandy soil

Spectral Signature for Water:

•  Reflection of Light - Wavelengths

•  Water Depths – Shallow, Deep

•  Suspended material

•  Chlorophyll Content

•  Surface Roughness

•  The majority of radiant flux incident upon water is either not reflected but is either absorbed or transmitted.

•  In visible wavelengths of EMR, little light is absorbed, a small amount, usually below 5% is reflected and the rest is transmitted.

•  Water absorbs NIR and MIR strongly leaving little radiation to be either reflected or transmitted. This results in sharp contrast between any water and land boundaries.

Figure 29: True Color vs False color images

Spectral Reflectance of Snow:

1. GRAIN SIZE (HENCE AGE)

   •  Reflectance falls at all wavelengths as grain size increases

2.  SNOW PACK THICKNESS

   •  Reflectance of snow decreases as it ages

3.  LIQUID WATER CONTENT

   •  Even slightly melting snow reduces reflectance

4.  CONTAMINANT PRESENT

   •  Contaminations (soot, particles, etc.) Reduce snow reflection in the visible region.

•  The lines in the figure represent average reflectance curves compiled by measuring large sample features.

•  Observe how distinctive the curves are for each feature.

•  The configuration of these curves is an indicator of the type and condition of the features to which

they apply.

•  Although the reflectance of individual features will vary considerably above and below the average, these curves demonstrate some fundamental points concerning spectral reflectance.

Figure 30: Spectral Reflectance of Snow

Sensor Resolution in Remote Sensing

Sensor Resolution:

Resolution is defined as the ability of the system to render the information at the smallest discretely separable quantity in terms of distance (spatial), wavelength band of EMR (spectral), time (temporal) and/or radiation quantity (radiometric).

The Four Resolutions of Remote Sensing

• Spatial

• Spectral
• Radiometric
• Temporal

1. Spatial Resolution: 

Spatial resolution is the projection of a detector element or a slit onto the ground. In other words, scanner's spatial resolution is the ground segment sensed at any instant. It is also called ground resolution element (GRE).

Ground Resolution (GRE) = H x IFOV

where, GRE=Ground Resolution Element

D=detector dimension,

F=focal length, and

H=flying height 

Instantaneous Field of View (IFOV)

It is defined the solid angle through which a detector is sensitive to radiation. 

                            IFOV = D/F radian 

The spatial resolution at which data are acquired has two effects –the ability to identify various features and quantify their extent. The former one relates to the classification accuracy and the later to the ability to accurately make mensuration. One important aspect in classification accuracy is the contribution of boundary pixels. As the resolution improves, pure center pixels of a feature increase in comparison to boundary pixels. Thus the boundary error gets reduced with improved resolution.

The accuracy of measurement of an area will depend upon the accuracy of locating the boundary. Since it is not possible to locate with accuracy better than a fraction of a pixel, the larger the pixel size, the more error will be the error in the area estimation.

Images where only large features are visible are said to have coarse or low resolution. In fine resolution images, small objects can be detected.

•  The physical dimension on earth is recorded

•  It refers to the amount of detail that can be detected by a sensor.

•  Detailed mapping of land use practices requires a much greater spatial resolution

10 meter resolution

30 meter resolution

80 meter resolution

Figure 1: IFOV and FOV

Desirable Spatial Resolution:

Meteorology > Cloud patterns, movement > 1-2 Kms
                         Water vapor Analysis  > 8 Kms

Oceanography  > Ocean Color Monitoring (Chlorophyll, Sediment Map, 
                             Yellow Substance, Sea Surface Temp. Mapping)  > 300-1100 m

Land use  > Crop monitoring, Forest Mapping, Hydrology etc.  > 20-30 m
                    Cartography, Urban Planning  > 2-6 m

                    Military Surveillance  > <_ 1 m

2. Spectral Resolution: 

Spectral emissivity curves, which characterize the reflectance and/or emittance of a feature or target, over a variety of wavelengths. Different classes of features and details in an image can be distinguished by comparing their responses over distinct wavelength ranges. Broad classes such as water and vegetation can be separated using broad wavelength ranges (VIS, NIR), whereas specific classes like rock types would require a comparison of fine wavelength ranges to separate them. Hence spectral resolution describes the ability of the sensor to define fine wavelength intervals i.e. sampling the spatially segmented image in different spectral intervals, thereby allowing the spectral irradiance of the image to be determined.
The selection of spectral band location primarily depends on the feature characteristics and atmospheric absorption.

•  Spectral resolution describes the ability of a sensor to define fine wavelength intervals.

•  This refers to the number of bands in the spectrum in which the instrument can take measurements.

•  Higher spectral resolution = better ability to exploit differences in spectral signatures

 

Spectral resolution Sensor Image Example:

•  panchromatic

•  multispectral

•  hyperspectral

3. Radiometric Resolution:

This is a measure of the sensor to differentiate the smallest change in the spectral reflectance/emittance between various targets. It is normally defined as the noise equivalent reflectance change or noise equivalent temperature.

The radiometric resolution depends on the saturation radiance and the number of quantization levels. Thus, a sensor whose saturation is set at 100 percentage reflectance with an 8 bit resolution will have a poor radiometric sensitivity compared to a sensor whose saturation radiance is set at 20 percentage reflectance and 7 bit digitization. 

• It describes the actual information content in an image.

• Sensitivity to the magnitude of the electromagnetic energy determines the radiometric resolution.

• The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy.

• The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy.

2 (number of bits) = number of grey levels

Figure 2: Bit Formation (Source: OpenGeoEdu)

Figure 3: radiometric resolution (Source: NASA Earth Observatory)

4. Temporal Resolution:

Obtaining spatial and spectral data at certain time intervals. Temporal resolution is also called as the repetivity of the satellite; it is the capability of the satellite to image the exact same area at the same viewing angle at different periods of time. The temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the swath overlap and latitude. It is an important aspect in remote sensing when

•  persistent cloud offers limited clear views of the earth’s surface
•  short lived phenomenon need to be imaged (flood, oil slicks etc.)
•  multi temporal comparisons are required (agriculture application)
•  the changing appearance of a feature over time can be used to distinguish it from near similar features (wheat/maize)
•  Represents the frequency with which a satellite can re-visit an area of interest and acquire a new image.
•  Depends on the instrument's field of vision, and the satellite's orbit

Application demand

Meteorological  hourly need to monitor clouds

Oceanographic - 2-3 days of repetivity

Stereo viewing - 0-1 days of repetivity

Vegetation monitoring - 5 days of repetivity

Figure 4: Temporal resolution

Sensor resolution and their applications with reference to IRS and landsat missions:

1. IRS Satellites: History, Characteristics and Applications:

India’s first indigenously designed and developed experimental satellite the Aryabhata (named after the famous ancient astronomer and mathematician) was successfully launched by a Soviet Kosmos-3M rocket on April 19, 1975 from Kapustin Yar. Starting from Bhaskara-I, the First Experimental Earth Observation Remote Sensing Satellite built in India and launched from Vostok, Russia (former USSR), in 1979 to the latest Cartosat 2 Series satellite launched (by indigenous launch vehicle PSLV) in 2018 a variety of sensors are operating in visible, infrared, thermal and microwave spectral regions, including hyper-spectral sensors to acquire digital data at spatial resolutions ranging from 1 km to a meter have been built and launched indigenously along with satellites of developed nations. Indian Space Research Organisation (ISRO) on 15 February 2017 in a single launch successfully fixed 104 satellites in orbits; out of these 3 satellites wer e Indian and rest were of the developed countries mainly the USA. Within a limited time period indigenous PSLV and GSLV have established huge number of IRS and INSAT series satellites in orbits. The facilities to receive and process the remotely sensed data have been established in different parts of India along with various international ground stations. The focus of the present article is the Indian Remote Sensing Satellite (IRS) series along with sensor characteristics and applications. In the early experimental phase, Bhaskara-1(June 7, 1979) and Bhaskara-2 (November 20, 1981) provided data for land applications on the basis of two types of sensor systems – (i) television camera with spatial resolution of 1 km operated in visible and near infrared bands and (ii) Satellite Microwave Radiometer (SAMIR) for oceanic and atmospheric applications. Following the success of this experimental phase, India initiated an indigenous Indian Remote Sensing Satellite (IRS) programme to support national and sub national economies in the areas of agriculture, soils, water resources (surface and ground), forestry and ecology, geology and mineral resources, cartography, rural and urban development, marine fisheries, watershed and coastal management.

The IRS-1A was launched as first indigenous trendsetting operational remote sensing satellite on March 17, 1988 into a Sun-synchronous Polar Orbit (SSPO) by Vostok launch vehicle from Baikonur, former USSR. It was followed by the IRS-1B, an identical satellite, launched by same vehicle and from the same place on August 29, 1991. The IRS-1A/1B satellite sensors Linear Imaging Self-Scanning (LISS-I and LISS-II) operated in visible and near-infrared (NIR) bands with spatial resolutions of 72.5 m and 36.25 m respectively. IRS -P2 satellite was launched (after the failure of IRS-P1 mission on September 20, 1993) by indigenous launch vehicle PSLV-D2 (P series is named after PSLV) on October 15, 1994 with only LISS-II sensor. LISS-I and LISS-II sensors provided useful data for applications in the fields of land use land cover mapping, agriculture, forestry, hydrology, pedology, oceanography, geology, natural resource management, disaster monitoring, and cartography. To further improve the quality of data IRS-1C and 1D, identical satellites, were launched with three sensors – LISS-III, PAN (panchromatic) camera and a Wide Field Sensor (WiFS) with spatial resolutions of 23.5 m, 5.8 m and 188 m, respectively. In addition to fulfilling the general requirements, theme based IRS missions, for applications like natural resource management (RESOURCESAT series and RISAT series), ocean and atmospheric studies (OCEANSAT series, Megha- Tropiques and SARAL) and large scale mapping i.e. detailed mapping applications (CARTOSAT series) have been introduced and well established (Table 1).

Table 1. History of Indian Remote Sensing (IRS) Satellites and Major Applications

Sl. No.	Name	Launch Date	Status	Applications
1	IRS-1A	17 March 1988	Mission Completed in 1992	Land Use Land Cover Mapping, Agriculture, Forestry, Hydrology, Soil Classification, Coastal Wetland Mapping, Natural Resources (especially identification of potential groundwater locations), Disaster Monitoring, Cartography, etc.
2	IRS-1B	29 August 1991	Mission Completed in 2001
3	IRS-P1 (also IE)	20 September 1993	Crashed, due to launch failure of PSLV	Mission Failed
4	IRS-P2	15 October 1994	Mission Completed in 1997	Land, Oceanographic and Atmospheric applications
5	IRS-P3	21 March 1996	Mission Completed in 2004	Technology Evaluation and Scientific Methodology Studies
6	IRS-1C	28 December 1995	Mission Completed in 2007	Land and water resources management. Applications in forestry, agriculture, environment, soil characteristics, wasteland identification, flood and drought monitoring, ocean resource development, mineral exploration, land use and monitoring of underground and surface water resources.
7	IRS 1D	29 September 1997	Mission Completed in 2010
8	IRS-P4 (Oceansat- 1)	27 May 1999	Mission Completed in 2010	Ocean- and atmosphere-related applications
9	Technology Experiment Satellite     (TE S)	22 October 2001	Mission Completed	Experimental satellite to demonstrate and validate the technologies
10	IRS P6 (Resourcesa t- 1)	17 October 2003	In Service	Integrated land and water resources management
11	IRS P5 (Cartosat 1)	5 May 2005	In Service	First Indian Satellite (IRS P5) designed with capability to have stereo images;
12	IRS P7 (Cartosat 2)	10 January 2007	In Service	Digital Elevation Model (DEM); Geo-engineering (mapping) applications
13	Cartosat 2A	28 April 2008	In Service	DO
14	IMS 1	28 April 2008	In Service	To provide remotely sensed data to students and scientists in developing counties,
15	Oceansat-2	23 September	In Service	Ocean- and atmosphere-related applications
16	Cartosat-2B	12 July 2010	In Service	Geo-engineering (mapping) applications
17	Resourcesat -2	20 April 2011	In Service	Integrated land and water resources management
18	Megha- Tropiques	12 October	In Service	To understand the tropical weather and climate and associated energy and moisture budget
19	RISAT-1	26 April 2012	In Service	In agriculture, especially paddy monitoring in kharif season (sensor has cloud
20	SARAL	25 Feb 2013	In Service	Marine meteorology and sea state forecasting; Seasonal forecasting; Climate
21	Resourcesat -2A	07 Dec 2016	In Service	Integrated land and water resources management

22	Cartosat-2D	15 Feb 2017	In Service	Cartographic applications, urban and rural applications, coastal land use and regulation, utility management like road network monitoring, water distribution and creation of land use maps. Change detection to bring out geographical and manmade features and various other Land Information System (LIS) as well as Geographical Information System (GIS) applications.
23	Cartosat-2E	23 June 2017	In Service
24	Cartosat-2 F	Jan 12, 2018	In service

Source: Data compiled by Author from Indian Space Research Organisation, Department of Space.

Table 2. Characteristics of IRS Satellites

	Satellite		Sensor		Spectral Resolution (µm)		Spatial Resolution (m)		Swath width (km)		Temporal Resolution (days)		Orbit Characteristics and Radiometric Resolution or Quantization Level
	IRS-1A/1B		LISS-I, and LISS-II A/B (3 sensors)		0.45-0.52 0.52-0.59 0.62-0.68 0.77-0.86		72.5 m LISS-I 36 m LISS-II		148 74 x 2		22		Orbit Sun- synchronous; Altitude – 904 km; Inclination – 99.50; Equatorial crossing – 10.26 a.m.; Orbit Period – 103.2 minutes. Radiometric Resolution – 7 bit;
	IRS-1C/1D		LISS-III		0.52-0.59 0.62-0.68 0.77-0.86 1.55-1.70		23.5 23.5 23.5 70		142 142 142 148		24		Orbit – Sun synchronous, Altitude – 904 km; Inclination 98.690  Equatorial crossing – 10.30 a.m. Orbit Period = 101.23 min. Radiometric Resolution – 7 bit, Pan-6 bit
			PAN		0.50-0.75		5.8		70		24 (5)
			WiFS		0.62-0.68 0.77-0.86		188		804		5
IRS-P3		WiFS		0.62-0.68 0.77-0.86 1.55-1.70		188		804		5		Orbit: Sun synchronous; Equatorial crossing at 10:30 AM Altitude = 817 km; Inclination = 98.7º; Orbit Period = 101.35 min; Radiometri Resolution – 7 bit
		MOS- A MOS- B MOS- C		0.75-0.77 0.41-1.01 1.595-1.605		1500 520 550		195 200 192		24
IRS-P4 (Oceansat-1)		OCM MSMR		0.4-0.9 6.6, 10.65, 18, 21 GHz (freq.)		360 x 236 105x68, 66x43, 40x26, 34x22		1420 1360		2 2		Orbit: Sun-synchronous; Altitude = 720 km; Inclination = 98.28º; Orbit Period = 99.31 min; Equator crossing at 12:00; Spatial Resolution in km for frequency sequence; Radiometric Resolution – 12 bit.
IRS-P6 ResourceSat- 1		LISS-IV		0.52-0.59 0.62-0.68 0.77-0.86		5.8 5.8 5.8		70		24 (5)		Orbit - Sun synchronous Altitude = 817 km, Inclination = 98.69º, Orbit Period = 101.35 min; Equator crossing at 10:30 a.m. Radiometric Resolution – 10 bit
		LISS-III*		0.52-0.59 0.62-0.68 0.77-0.86 1.55-1.70		23.5 23.5 23.5 23.5		140		24
		AWiFS		0.62-0.68 0.77-0.86 1.55-1.70		56-70 56-70 56-70		740		5
IRS-P5 CartoSat-1		PAN-F PAN-A		0.50-0.75 0.50-0.75		2.5 2.5		30 30				Orbit - Sun synchronous ; Altitude = 618 km; Inclination =97.87º; Orbit Period of 97 min; Equatorial crossing - 10:30 a.m. Radiometric Resolution – 10 bit

2. Applications of IRS satellites:

In 1982 the Planning Commission of India had recognized necessity and significance of establishing a National Natural Resource Management System (NNRMS) to efficiently manage the natural resources by applying remote sensing techniques in conjunction with traditional techniques. Planning Committee of NNRMS (PC-NNRMS) sets guidelines for earth observation based systematic inventory of the country’s natural resources and oversees the progress of remote sensing applications for natural resources management in the country. PC- NNRMS in 1984 constituted six Standing Committees on – (i) Agriculture and Soil; (ii) Bio-resources and Environment; (iii) Geology and Mineral Resources; (iv) Ocean Resources; (v) Remote Sensing Technology and Training and (vi) Water Resources ; and in 1997 three more were constituted on (vii) Rural Development; (viii) Urban and (ix) Cartography. The themes of these Standing Committees themselves represent the major fields of applications of information acquired from earth observation satellite IRS series. The main applications of IRS series satellites are listed in brief in the following section.

 

1. Applications in Agriculture and Soil

 

The agricultural applications of IRS satellite series are following: - (i) Cropping pattern mapping; (ii) Pre- harvest crop area, production and yield estimation; (iii) Condition assessment; (iii) Monitoring command areas; (iv) Compliance monitoring (farming practices) e.g. crop stubble burning; (v) Identification of suitable sites for different agricultural practices; (vi) Mapping of soil characteristics; (vii) Mapping of soil management practices; (viii) Mapping of saline soils and monitoring of land reclamation; (ix) Inventorying and categorization of wastelands; and (x) Identification of fishery prospects.

 

2. Applications in Bio-resources and Environment

 

The applications of IRS satellite series in forestry, biodiversity and ecosystem sustainability are following: - (i) Mapping of forest cover, types, density and species inventory; (ii) Measurement of biophysical conditions of forest strands; (iii) Social forestry and agroforestry mapping; (iv) Biomass estimation; (v) Afforestation and deforestation assessment; (vi) Forest fire surveillance; (vii) Forest health and vigor monitoring; (viii) Detailed survey and inventory of the existing bio-resources; (ix) Environmental impact assessment including pollution (land, water and air); (x) Mapping and monitoring of tiger reserves, elephant corridors, biosphere reserves, mangroves and coral reefs; (xi) Assessment of fuel wood and timber resources; and (xii) Environmental hazard related studies like zonation and damage assessment (floods, drought, cyclone, landslide, volcano, earthquake etc.).

 

3. Applications in Geology and Mineral Resources

 

Geological applications of IRS series satellites include the following: - (i) mapping of surfacial deposits and bedrock; (ii) Lithological and structural mapping; (iii) Mineral prospecting and exploration; and (iv) Geo - hazard mapping, monitoring and zonation.

 

4. Applications in Oceanography

 

The applications of IRS series satellites, especially Oceansat-1 and Oceansat-2, include the following: - (i) Identification of potential fishery zones; (ii) Phytoplankton abundance and habitat assessment; (iii) Observation of marine pollution and sedimentation and its impact; and  (iv) Assessment of sediment dynamics, tidal fluctuations, sea level changes and coastal circulations.

 

5. Applications in Water Resources

 

The applications of IRS series satellite data products in water resource include the following:

- (i) Mapping of surface water bodies; (ii) Identification of potential ground water resources; (iii) Wetland mapping and monitoring; (iv) Snow pack and glacial monitoring; (v) Ice thickness measurements; (vi) Rivers, watersheds and ice lake monitoring and modelling; (vii) Flood mapping and monitoring; (viii) Monitoring reservoir extends over seasons and irrigation scheduling and  flood management; and  (ix) Snowmelt  runoff forecasting.

 

6. Applications in Urban Sector

 

The applications of IRS satellites data products in urban sector are following: - (i) Mapping and Land Use Land Cover classification; (ii) Urban sprawl analysis; (iii) Identification of illegal encroachment, and constructions; (iv) Property tax assessment and estimations; (v) Transport and urban planning; (vi) Mapping of utilities and services; (vii) Population estimation; (viii) Slum detection and monitoring; and (ix) Site suitability analysis

 

7. Applications in Cartography

 

Mapping constitutes an   integral   component of the process   of resource management and mapped information is the common product of analysis of remotely sensed data from IRS series satellites. The Cartosat series is especially oriented towards geo-engineering mapping and DTM (Digital Terrain Modelling) or DEM (Digital Elevation Modelling). Natural as well as manmade features such as transportation networks, settlements and administrative boundaries are represented spatially with respect to geo-referenced data and integrated with attribute information or non-spatial in GIS (Geographical Information System). Baseline, thematic and 2D and 3D topographical maps are essential for planning, evaluation and monitoring, for civilian and military reconnaissance and land use planning.

2. landsat missions: History, Characteristics and Applications:

Since 1972, Landsat satellites have continuously acquired space- based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education.

Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.

In the mid-1960s, stimulated by U.S. successes in planetary exploration using unmanned remote sensing satellites, the Department of the Interior, NASA, and the Department of Agriculture embarked on an ambitious effort to develop and launch the first civilian Earth observation satellite. Their goal was achieved on July 23, 1972, with the launch of the Earth Resources Technology Satellite (ERTS-1), which was later renamed Landsat 1. The launches of Landsat 2, Landsat 3, and Landsat 4 followed in 1975, 1978, and 1982, respectively. When Landsat 5 launched in 1984, no one could have predicted that the satellite would continue to deliver high quality, global data of Earth’s land surfaces for 28 years and 10 months, officially setting a new Guinness World Record for “longest-operating Earth observation satellite.” Landsat 6 failed to achieve orbit in 1993; however, Landsat 7 successfully launched in 1999 and continues to provide global data. Landsat 8, launched in 2013, continues the mission, and Landsat 9 is tentatively planned to launch in 2020 (fig. 1).

1. Satellite Acquisitions:

The Landsat 7 and Landsat 8 satellites both orbit the Earth at an altitude of 705 kilometers (438 miles) in a 185-kilometer (115-mile) swath, moving from north to south over the sunlit side of the Earth in a sun synchronous orbit. Each satellite makes a complete orbit every 99 minutes, completes about 14 full orbits each day, and crosses every point on Earth once every 16 days. Although each satellite has a 16-day full-Earth-coverage cycle, their orbits are offset to allow 8-day repeat coverage of any Landsat scene area on the globe. Between the two satellites, more than 1,000 scenes are added to the USGS archive each day. Landsats 4 and 5 followed the same orbit as Landsats 7 and 8, whereas Landsats 1, 2, and 3 orbited at an altitude of 920 kilometers (572 miles), circling the Earth every 103 minutes, yielding repeat coverage every 18 days. The Landsat Long Term Acquisition Plans (LTAPs) identify Earth imaging priorities that most effectively utilize both Landsat 8 and Landsat 7 data acquisitions. Information about the LTAPs is provided on the Landsat Missions Web site (http://landsat.usgs.gov).

2. Sensors and Band Designations:

The primary sensor onboard Landsats 1, 2, and 3 was the Multispectral Scanner (MSS), which collected data at a resolution of 79 meters in four spectral bands ranging from the visible green to the near-infrared (IR) wavelengths. Delivered Landsat MSS data are resampled to 60 meters (table 1). Return Beam Vidicon (RBV) instruments on Landsats 1, 2, and 3 acquired data at 40-meter resolution, and were recorded to 70-millimeter black and white film. RVB data are archived at the Earth Resources Observation and Science (EROS) Center and are available as film-only products. Landsat 4 and Landsat 5 also carried the MSS, along with the Thematic Mapper (TM) sensor. The TM sensor included additional bands in the shortwave infrared (SWIR) part of the spectrum; improved spatial resolution of 30 meters for the visible, near-IR, and SWIR bands; and the addition of a 120-meter thermal IR band. Delivered Landsat 4 and Landsat 5 TM thermal data are resampled to 30 meters (table 1).

Landsat 7 carries the Enhanced Thematic Mapper Plus (ETM+), with 30-meter visible, near-IR, and SWIR bands; a 60-meter thermal band; and a 15-meter panchromatic band. Delivered Landsat 7 ETM+ thermal data are resampled to 30 meters (table 1). On May 31, 2003, unusual artifacts began to appear within the data collected by the ETM+ instrument. Investigations determined that the Scan Line Corrector (SLC), which compensates for the forward motion of the satellite to align forward and reverse scans necessary to create an image, had failed. Efforts to recover the SLC were unsuccessful, and without an operating SLC, 22 percent of the image data are missing, which results in data gaps forming in alternating wedges that increase in width from the center to the edge of the image. Landsat 7 still acquires geometrically and radiometrically accurate data worldwide, and methods have been established that allow users to fill the data gaps.

Landsat 8, launched as the Landsat Data Continuity Mission on February 11, 2013, contains the push-broom Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI collects data with a spatial resolution of 30 meters in the visible, near-IR, and SWIR wavelength regions, and a 15-meter panchromatic band, which provides data compatible with products from previous missions. OLI also contains a deep blue band for coastal-aerosol studies and a band for cirrus cloud detection (table 1). The TIRS contains two thermal bands, which were designed to allow the use of split-window surface temperature

Table 1. Display and comparison of the bands and wavelengths of each Landsat sensor.

Band designations	Landsat band wavelength comparisons All bands 30-meter resolution unless noted
	L8 OLI/TIRS		L7 ETM+		L4-5 TM		L4-5 MSS*		L1-3 MSS*
Coastal/Aerosol	Band 1	0.43–0.45	--	--	--	--	--	--	--	--
Blue	Band 2	0.45–0.51	Band 1	0.45–0.52	Band 1	0.45–0.52	--	--	--	--
Green	Band 3	0.53–0.59	Band 2	0.52–0.60	Band 2	0.52–0.60	Band 1	0.5–0.6 *	Band 4	0.5–0.6 *
Panchromatic	Band 8**	0.50–0.68	Band 8 **	0.52–0.90	--	--	--	--	--	--
Red	Band 4	0.64–0.67	Band 3	0.63–0.69	Band 3	0.63–0.69	Band 2	0.6–0.7 *	Band 5	0.6–0.7 *
Near-Infrared	Band 5	0.85–0.88	Band 4	0.77–0.90	Band 4	0.76–0.90	Band 3	0.7–0.8 *	Band 6	0.7–0.8 *
Near-Infrared	--	--	--	--	--	--	Band 4	0.8–1.1 *	Band 7	0.8–1.1*
Cirrus	Band 9	1.36–1.38	--	--	--	--	* Acquired at 79 meters, resampled to 60 meters ** 15-meter (panchromatic) T1 = Thermal (acquired at 100 meters, resampled to 30 meters) T2 = Thermal (acquired at 120 meters, resampled to 30 meters)
Shortwave Infrared-1	Band 6	1.57–1.65	Band 5	1.55–1.75	Band 5	1.55–1.75
Shortwave Infrared-2	Band 7	2.11–2.29	Band 7	2.09–2.35	Band 7	2.08–2.35
Thermal	Band 10 T1	10.60–11.19	Band 6 T2	10.40–12.50	Band 6 T2	10.40–12.50
Thermal	Band 11 T1	11.50–12.51	--	--	--	--

Table 2.  The bands of each Landsat satellite and descriptions of how each band is best used

Band name	L8 OLI/TIRS	L7 ETM+	L4-5 TM	L4-5 MSS	L1-3 MSS	Description of use
Coastal/Aerosol	Band 1	--	--	--	--	Coastal areas and shallow water observations; aerosol, dust, smoke detection studies.
Blue (B)	Band 2	Band 1	Band 1	--	--	Bathymetric mapping; soil/vegetation discrimination, forest type mapping, and identifying manmade features.
Green (G)	Band 3	Band 2	Band 2	Band 1	Band 4	Peak vegetation; plant vigor assessments.
Red (R)	Band 4	Band 3	Band 3	Band 2	Band 5	Vegetation type identification; soils and urban features.
Near-Infrared (NIR)	Band 5	Band 4	Band 4	Band 3	Band 6	Vegetation detection and analysis; shoreline mapping and biomass content.
	--	--	--	Band 4	Band 7
Shortwave Infrared-1 (SWIR-1)	Band 6	Band 5	Band 5	--	--	Vegetation moisture content/drought analysis; burned and fire- affected areas; detection of active fires.
Shortwave Infrared-2 (SWIR-2)	Band 7	Band 7	Band 7	--	--	Additional detection of active fires (especially at night); plant moisture/drought analysis.
Panchromatic (PAN)	Band 8	Band 8	--	--	--	Sharpening multispectral imagery to higher resolution.
Cirrus	Band 9	--	--	--	--	Cirrus cloud detection.
Thermal (T)	Band 10	Band 6	Band 6	--	--	Ground temperature mapping and soil moisture estimations.
	Band 11			--	--

 

                                                                                                                                                                                                                                                             

                                                                                                                                                                                                           

 retrieval algorithms; however, due to larger calibration uncertainty associated with band 11, it is recommended that users refrain from using band 11 data.

A Quality Assessment (QA) band is also included in Landsat 8 data products. This file contains information that improves the integrity of science investigations by indicating which pixels could be affected by instrument artifacts or cloud contamination.

3. Applications of Landsat Data:

Landsat data support a vast range of applications in areas such as global change research, agriculture, forestry, geology, land cover mapping, resource management, water, and coastal studies. Specific environmental monitoring activities such as deforestation research, volcanic flow studies, and understanding the effects of natural disasters all benefit from the availability of Landsat data. In recent years, Landsat data have also been used to track oil spills and to monitor mine waste pollution. Table 2 lists Landsat bands and describes the use of each band to help users determine the best bands to use in data analysis. The consistency of Landsat data acquisitions through the years and the richness of the archive, combined with the no-cost data policy, allow users to exploit time series of data over extensive geographic areas to establish long-term trends and monitor the rates and characteristics of land surface change (fig. 2).

4. Landsat Data Products and Processing:

The USGS delivers high quality systematic, geometric, radiometric, and terrain corrected data to users worldwide, and since December 2008, without any cost to users. Millions of Landsat scenes have been downloaded since moving to the open archive model. Landsat Level-1 data products are processed to standard parameters, which include cubic convolution resampling,

 

Figure 2. Landsat images showing expanding archipelagos along the coast of Dubai, United Arab Emirates. A, October 1998 (Landsat 5); B, May 2003 (Landsat 7); C, May 2008 (Landsat 5); and D, May 2015 (Landsat 8).

north-up (map) orientation, Universal Transverse Mercator (UTM) map projection (Polar Stereographic for Antarctic scenes), and World Geodetic System (WGS) 1984 datum. Data are delivered in Georeferenced Tagged Image File Format (GeoTIFF) in compressed files for faster downloads. The number and sizes of data files vary based on the sensor. Full resolution “natural” color composite Joint Photographic Expert Group (.jpg) files of Landsat images (named LandsatLook Images) are also available to download for easy use in presentations and visual interpretation.

Recognizing the need for new climate information products to meet national and international requirements in accordance with the Global Climate Observing System (GCOS), USGS scientists developed higher-level science data products (also known as Level-2). Higher-level data are processed to support time series of observational data with sufficient length, consistency, and continuity to record effects of climate change. Atmospherically corrected Landsat surface reflectance data are the first Level-2 products produced by the USGS. Surface-reflectance-based spectral indices are also available. Landsat surface reflectance and other higher-level data are considered provisional.

Source: 

1. Bhatta , B. (2008) Remote Sensing and GIS, Oxford University Press, New Delhi

2.  www.isro.gov.in

3. www.usgs.gov