Remote Sensing in Environmental Research

Georgios Aim. Skianis

University of Athens, Faculty of Geology and Geo-Environment, Department of Geography and Climatology, Remote Sensing Laboratory.

1. Physical principles

2. Platforms and Sensors

3. Images at the visible and infrared spectrum

4. Images at the thermal infrared spectrum

5. Radar images

6. Image analysis

7. Some environmental applications

1. Physical Principles

Red, Green, Blue additive colors Blue channel 1

Green channel 2 Red channel 3

RGB 321 (color composite)

Spectral Signature

2. Platforms, Scanners and Sensors

3. Images at the visible and infrared spectrum

Landsat ETM channel 1 (blue). City of Pyrgos (Western Peloponnesos)

Brightness value (tonality) of each pixel

Channel 2 (green) Channel 3 (red)

Channel 4 (NIR, 0.76-0.9 μm) Channel 5 (middle infarred, 1.55-1.75 μm)

RGB 321 (Red, Green, Blue) RGB 432 (NIR, Red, Green)

RGB 542 (Middle Infrared, NIR, Red)

Landsat natural colors (RGB 321) RGB 432 (NIR, Red, Green)

RGB 421 (NIR, Green, Blue) RGB 742 (middle infrared, NIR, Green)

4. Images at the thermal infrared spectrum

Τrad = ε1/4. Τkin T radiant, emissivity, T kinetik

P thermal inertia (how easy does the temperature change)

As long as thermal inertia increases, temperature variation decreases

RGB 321 (natural colors) Landsat image over Mesologi-Evinos river

Thermal infrared image of the same region

Landsat nocturnal image of the lakes Ontario and Erie, USA

L = c.DN 2731/ln 1

2

LK

KT

Landsat image, thermal infrared channel

A map of temperatures

5. Radar Images

Active Passive remote sensing

Radar image, ERS-1, Udine, Italy

Landsat image, Udine, Italy

Zone L, Polarization HH, Endeavour SIR-CX-SAR

Zone L, Polarization HV, Endeavour SIR-CX-SAR

RGB L-HH (red), L-HV (green) και C-HH (blue). Endeavour SIR-CX-SAR

Detection of an oil spill

RGB L-VV (red), mean value L-VV and C-VV (green) and C-VV (blue). Οι εικόνες ελήφθησαν από το σύστημα Endeavour, SIR-CX-SAR. Mumbai, India

Radar image L-HH, SIR-A, over Sahara Desert. The Landsat image is represented by yellow-orange colors.

Radar may penetrate certain meters below ground surface

6. Image Analysis

•Preprocessing (georeferencing, atmospheric correction, destriping,…)

•Image enhancement (contrast enhancement, image sharpening, edge detection,…)

•Information extraction (vegetation indices, classification, principal component analysis,…)

Atmospheric correction

)cos(

)BV(

00

min

01 d

p

EtEt

LLK

Landsat RGB 321 image Atmospherically corrected image

Destriping

Initial image Filtered (destriped) image

Contrast enhancement

Initial Linear stretch

Equalization

Edge detection

22 ff|grad| yx

–1 0 1

fx = –2 0 2

–1 0 1

1 2 1

fy = 0 0 0

–1 –2 –1

Initial image

Filtered image

Sobel filter

Classification

Spectral domain

Training fields

Classified image

7. Some environmental applications

Thermal channel

Contrast enhanced temperature map of the Argolic Bay

Detection of submarine carstic springs

The drainage network of a region of Southern Yemen, as it appears in a Landsat image

Mapping of the drainage network

Satellite images of Elvas river (Germany) before and after the floods of 2000

Mapping of floods

Land cover mapping using vegetation indices

Satellite image of Nile river, Egypt, in natural colors

The NDVI vegetation index of the region.

NDVI = (NIR-Red)/(NIR +Red)

Mapping burnt areas

NDVI image produced by an ALOS multispectral image

Terra Modis satellite image over the Gulf of Mexico. The meandric structure with the bright tones is the Gulf stream.

Oceanography

Archaeology

Detection of the ancient city of Ubar (Arabic Peninsula) by a Landsat image