Global Forest Watch Using High Resolution Synthetic Aperture Radar

Forests always have been an important for all human beings and animals. For some forests provide shelter and some forests are source of livelihood.


Monitoring global forest footprint is of particular interest and importance for scientific community. Remote sensing technique has played a vital role in in determination and estimation of global forest cover.


Since 1972, US Landsat satellite program (optical imaging) is well known for measuring forest cover. There are certain regions in the world such as tropical forest, which are under cloud cover most of the time in a year. Optical remote sensing senor couldn’t see beyond the clouds collect and hence put the limitation on global forest cover.





Global mosaics show phased-array type L-band synthetic aperture radar-2 color composites in (top) horizontal transmission/ reception (HH), horizontal transmission/vertical reception (HV), and HH/HV modes in red, green and blue, respectively, and (bottom) the corresponding map of forest areas (green)/non-forest areas (yellow)

Credit: SPIE


With advancement in technology, a new technology known as synthetic aperture radar (SAR) has revolutionized the remote sensing technology. SAR can see through the clouds and even in nights.


In a study by Masanobu Shimada et. al., titled “High-resolution satellite radar for mapping changes in global forest cover” has produced the global forest cover map for 2007–2010.


A high-resolution synthetic aperture radar (SAR) uses low-frequency microwaves, which enable forest and non-forest/other land use to be easily differentiated under all weather conditions. As of now only three SAR frequency bands are available for use in space. Of these three bands, the L-band of 23 cm wavelength has a higher ability to distinguish forest areas than the C-band of 5.6cm and X-band of 3 cm wavelength.


Scientist have used the phased-array type L-band SAR (PALSAR) was a high resolution multi-polarization low-noise radar, which was installed on the Advanced Land Observing Satellite (ALOS). ALOS has collected 2.1 million SAR images of 70*70 km in dimension of entire globe. They have also applied PALSAR data in HV mode to estimate forest cover and its annual variation.








Color composite image showing four-year variation of forest cover in western Indonesia and Malaysia.Credit: SPIE



Scientists have generated four global mosaics of PALSAR horizontal transmission/horizontal reception (HH) and HV polarization data at 25 m spatial resolution using data acquired annually from 2007 to 2010.


The first estimated global forest area was 38,542,500 sq. km from the ALOS/PALSAR data set, following the Landsat-based estimation of forest area as 40,656,570 sq. km . The difference can probably be attributed to a difference in the sensitivity of optical and L-band SAR techniques in measuring forest cover.


In summary, L-band SAR backscatter provides the basis of information regarding forest and non-forest areas on a global scale. The enhanced maps and classification of land use provide a new global resource for documenting the changing extent of forests and also offer opportunities to quantify historical and future changes through comparison with data from the Japanese Earth Resources Satellite-1 SAR (1992–1998) and ALOS-2/PALSAR-2 (from 2014).

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ICAR-NIVEDI is looking for Project Assistant

NIVEDI – National Institute Of Veterinary Epidemiology And Disease Informatics, formerly known as Project Directorate of Animal Disease Monitoring and Surveillance (PD_ADMAS), has a long successful history of delivering predicted informatics and solutions for various animal diseases. The institute has been setup under the regulations of Indian Council of Agricultural Research (ICAR).

A walk in interview for one position of Project Assistant purely on contract basis under various projects at ICAR-National Institute of Veterinary Epidemiology  & Disease Informatics(ICAR Campus) Ramagondanahalli, Yelahanka, Bengaluru.

Name of Position: Project Assistant

Name of the Project: DBT twinning on BVD

No. of Position: 01

Education Qualification Required: Bachelor’s Degree in Microbiology/ Bio-technology/Remote Sensing and GIS Geo-informatics

Experience: Working experience in Microbiology Laboratory with computer knowledge will be preferred.

Remuneration: Rs.8000/- per month (Consolidated)

Age: 18-30 years (Age relaxation upto 35 years for SC/ST and 33 years for OBC as on the date of advertisement).

HOW TO APPLY

Walk-in-Interview: At 11.00 AM on 07 July 2015

The positions are co-terminus with the respective projects.Candidates are required to bring testimonials in original and one set of attested copies of testimonials & one passport size colour photo along with completed application form (which may be downloaded from the website), at the time of interview.

Candidates should be present for verification of testimonials at 10.30 AM on 7-7-2015.

DOWNLOAD OFFICIAL NOTICE

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Geo-matching.com Adds Handheld Scanners Category

Lemmer, 30 June 2015–Geo-matching.com has recently added Handheld Scanners to its broad spectrum of product categories. FARO Europe,Dot Product and Artec 3D Scanners are the first suppliers in this category.In addition to general specifications, detailed information is given abouts canning characteristics, connectivity and sensors and functionality.To see the Handheld Scanners category, visit http://geo-matching.com/category/id84-handheld-scanners.html.

Geo-matching.com (www.geo-matching.com) is the independent geomatic and hydrographic product comparison website featuring detailed spec-based comparisons and user reviews for more than 920 products in 35 product categories. The website guides users through the maze of specifications and gives them the opportunity to compare products from different suppliers. They can also read other professionals’ reviews in order to reach a balanced judgment before buying.

Just some of the categories on offer include Digital Aerial Cameras, GNSS Receivers, Mobile GIS Systems – Hardware and Software, Mobile Mappers, Photogrammetric Software, Imagery Processing Software, Point Cloud Processing Software, Remote Sensing Image Processing Software, Terrestrial Laser Scanners, Total Stations, and UAS for Mapping and 3D Modelling.

Visit www.geo-matching.com to browse through the products, upload a productorleave a review. Feel free to contact sybout.wijma@geomares.nl for more information.

Geo-matching.com is an initiative of Geomares Publishing, an international publisher of magazines, websites, newsletters, books and more relating to geomatics, hydrography and adjacent industries suchas electronic navigation. Geomares Publishing, Nieuwedijk 43, 8531 HK Lemmer, The Netherlands, Tel.+31 (0)514 561854, email: info@geomares.nl.

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Fast, accurate and highly automated Atmospheric Correction

Video: 

Learn the importance of working with calibrated imagery for applications such as land use and land cover mapping and change detection. Using Geomatica, response values can be normalized across repeat pass images, using interactive spectral plots with expected values.


Follow the link for the video:




https://www.youtube.com/watch?v=5ATyM-bbyc8&feature=autoshare




Via PCI Geomatics, link here.

Why SWIR Band in Remote Sensing?

Each object has its own spectral signature, which is also the basic principle of remote sensing. Today we have satellites with various sensors collecting data in Visible light, Near Infrared (NIR), Thermal Infrared (TIR), Panchromatic and Shortwave Infrared (SWIR ).

If we have these many sensors, why we need SWIR sensor?

SWIR  is immediate adjacent to NIR in electromagnetic spectrum  and refers to non-visible light falling roughly between 1400 and 3000 nanometers (nm) in wavelength.

SWIR Sensor vs Visible Light Sensor:

SWIR light is reflective in nature and bounce of objects much like visible light. But SWIR light is not visible to human eyes. As a result of reflective nature, SWIR light has shadow and contrast in its imagery (contrast depends upon the radiometric resolution of sensor). Unlike visible light imagery SWIR light image is not in color. This makes objects easily recognizable and yields one of the tactical advantages of the SWIR, namely, object or individual identification.

Visible blue, green, and red of Landsat 8 satellite. Credit: USGS

SWIR Sensor vs Thermal Sensor:

Thermal sensors are another important type of sensor as they can see heat and so use for  thermal mapping. Instead of measuring the temperature of the air (as weather station do), they capture the ground heat. Thermal sensor capture imagery of warm object against a cool background and they do not provide good resolution  (100 m in Landsat 8) imagery. Whereas SWIR sensors can have high resolution (30 m in Landsat 8, 3.7/7.5 meter in WorldView 3) and can actually identify what object is. They also pinpoint sites of active burning, detect hot spots and estimate of where the fire is burning the hottest, so that response efforts can be directed most efficiently.

Thermal infrared, or TIR bands of Landsat 8 satellite. Credit: USGS

Like SWIR light imagery they are also not in color. SWIR Sensors are smaller in size than Thermal sensor, hence a lighter payload for satellite.

SWIR Sensor vs Panchromatic Sensor:

Panchromatic sensor capture imagery in single band (black and white film) instead of collecting visible color (red, green, blue) separately, it combines them into one channel or band. Panchromatic sensor can see more light at once and can have very high spatial resolutions  (31 cm in WorldView 3) than any other sensor types. But panchromatic sensor can not see in night and through smoke.

Panchromatic band of Landsat 8 satellite . Credi: USGS

SWIR sensor may not have similar spatial resolution but they have a high spatial resolution (3.7/7.5 meter in WorldView 3, 30 m in Landsat 8). SWIR sensor can see in night and through smoke.

SWIR Sensor vs NIR Sensor:

Near Infrared sensor are extremely important for ecology because healthy plants reflect it – the water in their leaves scatters the wavelengths back into the sky. They can be used for vegetation monitoring, crop stress etc. By comparing it with other bands, we get indexes like NDVI, which let us measure plant health more precisely than if we only looked at visible greenness. But NIR sensor do not tell us about the geology, rocks etc.

Near infrared, or NIR of Landsat 8 satellite. Credit: USGS

False-color image by using SWIR as red, NIR as green, and deep blue as blue of Landsat 8 satellite. Credit: USGS

SWIR sensors are particularly useful for telling wet earth from dry earth, and for geology: rocks and soils that look similar in other bands often have strong contrasts in SWIR. SWIR sensors discriminates moisture content of soil and vegetation and penetrates thin clouds.

I. The SWIR image is a “false color” composite made from three of the eight SWIR bands (bands 6, 3, and 1) that coincidentally give an orange color to the fire. Credit: DigitalGlobe

II. The SWIR bands penetrate smoke to differing degrees. SWIR band 8 has the best smoke penetration. Credit: DigitalGlobe

III. Further zoom around the burn area in which no smoke is visible. Credit: Digitalglobe

IV. SWIR band 8 has the best smoke penetration; here is a zoomed in shot of the fire line in which no smoke is visible.    Credit: DigitalGlobe

Possible Applications of SWIR Sensors:

• Mineral exploration
• Wildfire response (can penetrate through smoke)
• Food security
• Mining/Geology
• Urban feature identification (such as roofing and construction materials)
• Vegetation
• Petroleum (e.g. an oil spill)
• Snow and Ice discrimination
• Soil moisture estimation

Advantages of SWIR

• High sensitivity
• High resolution
• Provide imagery in day and night
• Can see penetrates thin clouds
• Covert illumination
• SWIR sensors are small in size, hence lighter payload and can be mounted on UAV
• Can see through the smoke
• Atmospheric aerosols have lesser effect on SWIR bands.

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