Introduction to Optical Remote Sensing: Basic concepts and applications

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Docente: Dr. Juan Miguel Ramírez Cuesta

(ramirezcuesta.jm@gmail.com) 17 Aprile 2020

Progetto di ricerca

INtegrated Computer modeling and monitoring for Irrigation Planning in Italy – PRIN

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• What is Remote Sensing?

• Physical principles

• Satellite & sensor characteristics

• Data processing

• Platform & sensor systems

• Visual interpretation

• Vegetation indices

Base concepts

& Practical exercises

Applications

• Crop type classification

• Crop condition assessment

• Crop yield estimation

• Mapping of soil characteristics

• Farming practices

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DEFINITION:

Remote sensing is the science of acquiring information about the Earth's surface without being in contact with it.

This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information

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Elements (I):

• Energy Source: energy source which illuminates or provides electromagnetic energy to the target of interest

• Radiation and the Atmosphere: energy travelling contacts and interact with the atmosphere it passes through.

• Interaction with the Target: dependent of the properties of both the target and the radiation.

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• Recording of Energy by the Sensor: to collect and record the electromagnetic radiation

• Transmission, Reception, and Processing:

transmission of the energy recorded by the sensor to a receiving and processing station

• Interpretation and Analysis: the processed image is interpreted to extract information about the target

• Application: The extracted information reveals some new information, or assist in solving a particular problem.

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• Reflectivity: ratio between the incident energy flow and the flow reflected

• Absorptivity: ratio between the incident energy flow and the flow absorbed

• Transmissivity: ratio between the incident energy flow and the flow transmitted

• Emissivity: relationship between the emittance of a surface and that offered by a perfect emitter, called black body, at the same temperature

Incident energy flow

Reflectivity

Emissivity Absorptivity

Transmissivity

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Wave theory

Electromagnetic radiation consists of an electrical field (E) which varies in magnitude in a direction perpendicular to the direction in which the radiation is traveling, and a magnetic field (B) oriented at right angles to the electrical field. Both these fields travel at the speed of light.

Magnetic field

Electrical field

Propagation direction

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-Wavelength (λ): length of one wave cycle, which can be measured as the distance between successive wave crests (m)

-Frequency: number of cycles of a wave passing a fixed point per unit of time (s^-1) -Period: the interval of time (s) between successive occurrences of the same state in an oscillatory or cyclic phenomenon

-Amplitude: maximum distance (m) moved by a point on a wave measured from its equilibrium position

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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 applications: Ultraviolet, Visible, Infrared and Microwaves

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Ultraviolet (UV)

Ozone, sulfur dioxide, and trace gases in the troposphere and stratosphere of interest to the atmospheric and volcanic sciences Near UV (400–300 nm)

Middle UV (300–200 nm) Far UV (200–100 nm) Extreme UV (100–10 nm)

Airglow and auroral emissions from the thermosphere of interest in aeronomy and space weather.

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Visible (VIS)

Blue (0.4 - 0.5 μm) Green (0.5 - 0.6 μm) Red (0.6 - 0.7 μm)

Only electromagnetic radiation that our eyes can perceive

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Infrared (IR)

Near infrared (0.7 to 1.5 μm) discriminate vegetation and soil moisture concentrations

Medium infrared (1.5 to 3 μm) estimate vegetation or soils moisture content, and detect high- temperature sources (such as fires or active volcanoes)

Far/Thermal infrared (3 to 1 mm) emission portion of the terrestrial spectrum, where the heat from most of the land covers is detected.

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Microwaves

Wavelengths > 1 mm

Great interest for being a type of energy that is quite transparent to the cloud cover

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Geostationary orbits: view the same portion of the Earth's surface at all times

Near-polar orbits: cover most of the Earth's surface over a certain period of time Orbit: path followed by a satellite

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• Spatial resolution

• Spectral resolution

• Radiometric resolution

• Temporal resolution Resolutions

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Size of the smallest possible feature that can be detected Pixel size

 Factors:

Orbit

Focal length Nº of detectors

 cm  km

• Spatial resolution

IKONOS Quickbird

Geoeye…

Meteosat GOES GMS…

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Number and width of the spectral bands that a sensor is able to determine

• Spectral resolution

Sensor more suitable when a greater number of bands is provided

Bands should be narrow enough to collect the signal over coherent regions of the spectrum

Number, width and location of the bands is related to the satellite objectives

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Sensitivity to the magnitude of the electromagnetic energy.

Ability to discriminate very slight differences in energy grey number

• Radiometric resolution

1 bit

(2¹ grey levels = black and white)

2 bits

(2² grey levels = 4 grey levels)

4 bits

(2⁴ grey levels = 16 grey levels)

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Revisit period, i.e. length of time it takes for a satellite to complete one entire orbit cycle

• Temporal resolution

Orbital characteristics of the platform:

Design of the sensor:

Altitude Velocity Inclination

Observation angle Aperture

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Preprocessing tasks needed before environmental information can be accurately extracted from remotely sensed data

 Radiometric calibration

 Geometric processing

 Image quality enhancement

 Atmospheric correction

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 Preflight calibration: measures a sensor’s radiometric properties before that sensor is sent into space

 In-flight calibration: routine basis with on-board calibration systems

 Post-launch calibration: make use of selected natural or artificial sites on the surface of the Earth

• Radiometric calibration

Digital number  Reflectance

Intended for correcting the degradation of the satellite performance over time

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 Altitude

 Attitude

 Velocity

 Earth rotation and curvature

 Surface relief displacement

 Perspective projection

• Geometric processing

Satellite no perfectly represent Earth surface

Systematic  Sensor characteristics Platform ephemeris data Random Ground control points

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 Instrument’s electronics

 Dead or dying detectors

 Downlink errors

• Image quality enhancement Imperfections or image artifacts:

Classification:

 Spatial domain: manipulating pixel values to achieve the desired enhancement

 Frequency domain: the image is first transferred to the frequency domain avoiding the direct use of pixel values.

Before

After

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 Known atmospheric properties Aerosol and water vapour (other sensors or models)

 Satellite imagery itself

• Atmospheric correction

Signal = Atmosphere + surface information

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Government Space Agencies

China National Space Administration (CNSA) European Space Agency (ESA)

Indian Space Research Organization (ISRO) Japan Aerospace Exploration Agency (JAXA)

National Aeronautics and Space Administration (NASA) Russian Federal Space Agency (RFSA)

Wost worldwide used satellites

TERRA AQUA LANDSAT 7 LANDSAT 8 SENTINEL 2

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Satellite Sensor Bands number

Band Spatial

resolution (m)

Spectral resolution (µm)

Radiometric resolution (bits)

Temporal resolution (days)

TERRA MODIS 18

Band 1 250 0.620-0.670

12 1

Band 2 250 0.841-0.876

Band 3 500 0.459-0.479

Band 4 500 0.545-0.565

Band 5 500 1.230-1.250

Band 6 500 1.628-1.652

Band 7 500 2.105-2.155

Band 8 1000 0.405-0.420

Band 9 1000 0.438-0.448

Band 10 1000 0.483-0.493

Band 11 1000 0.526-0.536

Band 12 1000 0.546-0.556

Band 13 1000 0.662-0.672

Band 14 1000 0.673-0.683

Band 15 1000 0.743-0.753

Band 16 1000 0.862-0.877

Band 17 1000 0.890-0.920

Band 18 1000 0.931-0.941

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Satellite Sensor Bands number

Band Spatial

resolution (m)

Spectral resolution (µm)

TERRA MODIS 18

Band 1 250 0.620-0.670

12 1

Band 2 250 0.841-0.876

Band 3 500 0.459-0.479

Band 4 500 0.545-0.565

Band 5 500 1.230-1.250

Band 6 500 1.628-1.652

Band 7 500 2.105-2.155

Band 8 1000 0.405-0.420

Band 9 1000 0.438-0.448

Band 10 1000 0.483-0.493

Band 11 1000 0.526-0.536

Band 12 1000 0.546-0.556

Band 13 1000 0.662-0.672

Band 14 1000 0.673-0.683

Band 15 1000 0.743-0.753

Band 16 1000 0.862-0.877

Band 17 1000 0.890-0.920

Band 18 1000 0.931-0.941

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Satellite Sensor Bands

number Band Spatial

resolution (m)

Spectral resolution (µm)

Landsat 7 ETM+ 8

Band 1 30 0.450-0.520

8 16

Band 2 30 0.520-0.600

Band 3 30 0.630-0.690

Band 4 30 0.770-0.900

Band 5 30 1.550-1.750

Band 6 60 10.400-12.500

Band 7 30 2.080-2.350

Band 8 15 0.520-0.900

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resolution (m)

Landsat 8

OLI 9

Band 1 30 0.430-0.450

12 16

Band 2 30 0.450-0.510

Band 3 30 0.530-0.590

Band 4 30 0.640-0.670

Band 5 30 0.850-0.880

Band 6 30 1.570-1.650

Band 7 30 2.110-2.290

Band 8 15 0.500-0.680

Band 9 30 1.360-1.380

TIRS 2 Band 10 100 10.600-11.190

Band 11 100 11.500-12.510

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resolution (m)

Sentinel 2 MSI 13

Band 1 60 0.443

12 5

Band 2 10 0.490

Band 3 10 0.560

Band 4 10 0.665

Band 5 20 0.705

Band 6 20 0.740

Band 7 20 0.783

Band 8 10 0.842

Band 8b 20 0.865

Band 9 60 0.945

Band 10 60 1.375

Band 11 20 1.610

Band 12 20 2.190

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Downloading satellite images Webpages:

•https://scihub.copernicus.eu/dhus/#/home

•https://glovis.usgs.gov/app?fullscreen=0

•https://earthexplorer.usgs.gov/

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Based on the analysis of some image characteristics:

Allow discriminating categories with a similar spectral behavior, but with a very different thematic meaning

Tone Colour Texture Pattern Shape Size

Association

Acquisition period

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• Tone

Intensity of energy received by the sensor for a certain spectrum band - Dark tones  low reflectivity

- Light tones  high reflectivity

Visible Near Infrared

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• Colour

Selective reflectivity of objects at different wavelengths

Wavelengths between 0.4 and 0.7 μm  Blue, Green and Red Bands combination:

Natural color

False colour

RED colour

GREEN colour

BLUE colour

NIR band Red band Green band Red band Green band Blue band

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Colour compositions

Visualizing the previously downloaded image in natural colour and in false colour

 Natural color: RGB  4-3-2

 False colour: RGB  8-4-3

 Red: Band 4

 Green: Band 3

 Blue: Band 2

 Near Infrared: Band 8

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• Texture

Apparent roughness or smoothness of a region of the image, i.e. spatial contrast between the elements that compose it

Reflects the relationship between the size of the objects and the resolution of the sensor Depending on the size of the objects at the image scale:

-Coarse texture: 0.25 - 1 mm² -Medium texture: 0.04 - 0.25 mm² -Fine texture: < 0.04 mm²

Example: Fruit versus deciduous

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• Pattern

Spatial arrangement of visibly discernible objects

Orderly repetition of similar tones and textures  recognizable pattern Examples:

-Orchards with evenly spaced trees

-Urban streets with regularly spaced houses

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• Shape

General form, structure, or outline of individual objects A very distinctive clue for interpretation

Urban or agricultural targets:

straight edge shapes Natural features:

more irregular in shape

Crop land irrigated by rotating sprinkler systems:

circular shapes.

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• Size

Function of scale

Assess the size of a target relative to other objects in a scene and the absolute size, to aid in the interpretation of that target.

Target size  appropriate interpretation

Example in urban land use:

-Large buildings  commercial use -Small buildings  residential use

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• Association

Relationship between other recognizable objects or features in proximity to the target of interest

The identification of features that one would expect to associate with other features may provide information to facilitate identification

Example: Urban garden versus natural forest

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• Acquisition period

Periodic acquisition of images  temporal dimension -Change detection between two reference dates

-Information about the seasonal cycle of vegetation cover

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Combinations between the original bands  discrimination of thematic aspects Better evaluate vegetation vigour and health status

-High RED-NIR contrast  High vegetation vigour/ better health status

-Low RED-NIR contrast  unhealthy/senescent vegetation or non-vegetated surfaces VI principle:

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Quantitative measures to determine biomass or plant vigor.

Obtained by the mathematical combination of the bands of a satellite image

Main Vegetation Indices

-Simple Ratio (SR)

-Normalized Difference Vegetation Index (NDVI) -Normalized Difference Water Index (NDWI) -Soil Adjusted Vegetation Index (SAVI)

-Enhanced Vegetation Index (EVI)

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• Simple Ratio (SR)

Ratio of near-infrared wavelength reflectivity to red-wavelength reflectivity Varies from 0 to more than 30, where normally healthy vegetation includes values between 2 and 8

𝑺𝑹 = 𝝆

_𝑵𝑰𝑹

𝝆

_𝑹

10

0

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NIR and R bands  Normalized

Varies between -1 and 1:

 NDVI < 0  Water

 NDVI ≈ 0  Bare soil areas

 NDVI ≈ 1  Healthy/vigorous vegetation

𝐍𝐃𝐕𝐈 = 𝝆

_𝑵𝑰𝑹

− 𝝆

_𝑹

𝝆

_𝑵𝑰𝑹

+ 𝝆

_𝑹

• Normalized Difference Vegetation Index (NDVI)

+1

-1

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Identification of water bodies, and crop water stress

The related bands are the green and NIR.

Water bodies  low reflectivity in VIS and NIR Water bodies  NDWI > 0.5

𝐍𝐃𝐖𝐈 = 𝝆

_𝑮

− 𝝆

_𝑵𝑰𝑹

𝝆

_𝑮

+ 𝝆

_𝑵𝑰𝑹

• Normalized Difference Water Index (NDWI)

vegetation soil

+1

-1

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Areas with small % of vegetation cover

Useful for different types of soil  Bare soil correction (L parameter)

Ranges between -1 and 1, values closer to 1 indicating healthy and vigorous vegetation.

𝑺𝑨𝑽𝑰 = 𝝆_𝑵𝑰𝑹 − 𝝆_𝑹

𝝆_𝑵𝑰𝑹 + 𝝆_𝑹 + 𝑳 (𝟏 + 𝑳)

• Soil Adjusted Vegetation Index (SAVI)

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𝐄𝐕𝐈 = 𝑮 𝝆_𝑵𝑰𝑹 − 𝝆_𝑹

𝝆_𝑵𝑰𝑹 + 𝑪𝟏 ∗ 𝝆_𝑹 + 𝑪𝟐 ∗ 𝝆_𝑩 + 𝑳

• Enhanced Vegetation Index (EVI)

Areas where the leaf area index of the plant canopy is very high

Includes the blue wavelength band

Corrects the effect of the bare soil and the atmospheric influence

Varies between -1 and 1, with the same meaning than NDVI and SAVI

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Calculating Vegetation Indices

𝑵𝑫𝑽𝑰 = 𝝆

_𝑵𝑰𝑹

− 𝝆

_𝑹

𝝆

_𝑵𝑰𝑹

+ 𝝆

_𝑹

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• Agriculture:

1) Crop type classification

2) Crop evapotranspiration estimation 3) Crop yield estimation

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1. Crop type classification

 12 Crop types

 Germany

 Landsat 5 and 7

 Spectral temporal profiles

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1. Crop type classification

 Increasing availability of optical imagery:

high temporal coverage and a spatial and radiometric resolutions suitable for crop type mapping

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2. Crop evapotranspiration estimation

 ETc estimations based on Dual Kc approach (FAO)

ETc =

 Tool for computing crop evapotranspiration (ETc)

ETo: reference evapotranspiration ETc: crop evapotranspiration

Kcb: Basal crop coefficient Kcb: Evaporation coefficient

Kcb + Ke ETo

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2. Crop evapotranspiration estimation

 Model Inputs:

Satellite images (L7/8, S2) Crop characteristics

Weather data Soil type

Irrigation system

 Spatially mapping ETc allows quantifying the water demand and its variability within an agricultural field.

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 Vineyard orchard

 Landsat 7 and 8

 NDVI

 Ground measurements 3. Crop yield estimation

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 Alternative to traditional grape yield prediction methods.

 Remote sensing provide spatial and temporal information for crop management

NDVI Predicted yield

 Yield predicted from NDVI versus ground relationships

3. Crop yield estimation

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Docente: Dr. Juan Miguel Ramírez Cuesta

(ramirezcuesta.jm@gmail.com) 17 Aprile 2020

Progetto di ricerca

INtegrated Computer modeling and monitoring for Irrigation Planning in Italy – PRIN