This document provides information about the Environmental Remote Sensing course GEOG 2021. It introduces the structure and content of the course, including lectures, practical sessions, assessment, and reading materials. The course is split into two halves, with the first introducing remote sensing concepts and the second focusing on a practical example. Lectures are on Mondays and practical sessions on Thursdays. Assessment consists of an exam and a coursework write-up. Relevant reading materials and online resources are also listed.
Basic Concepts, Explanation, and Application. Fundamental Remote Sensing; Advantage/ disadvantages, Imaging/non Imaging sensors, RAR and SAR, SAR Geometry, Resolutions in the microwave, Geometric Distortions in SAR, Polarization in SAR, Target Interaction, SAR Interferometry
Basic Concepts, Explanation, and Application. Fundamental Remote Sensing; Advantage/ disadvantages, Imaging/non Imaging sensors, RAR and SAR, SAR Geometry, Resolutions in the microwave, Geometric Distortions in SAR, Polarization in SAR, Target Interaction, SAR Interferometry
Remote sensing and aerial photography study notes. Including concept and history of RS, visual image interpretation, digital image interpretation, application of RS, digital imaging, application of remote sensing etc.
Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic radiation (EM) at varying wavelengths which can uniquely identify an object. Here, i have focused on the spectral signature of water and the various micro-process that are responsible for it.
Remote sensing and aerial photography study notes. Including concept and history of RS, visual image interpretation, digital image interpretation, application of RS, digital imaging, application of remote sensing etc.
Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic radiation (EM) at varying wavelengths which can uniquely identify an object. Here, i have focused on the spectral signature of water and the various micro-process that are responsible for it.
Modification and Climate Change Analysis of surrounding Environment using Rem...iosrjce
This review is presented in three parts. The first part explains such terms as climate, climate change,
climate change adaptation, remote sensing (RS) and geographical information systems (GIS). The second part
highlights some areas where RS and GIS are applicable in climate change analysis and adaptation. Issues
considered are snow/glacier monitoring, land cover monitoring, carbon trace/accounting, atmospheric
dynamics, terrestrial temperature monitoring, biodiversity conservation, ocean and coast monitoring, erosion
monitoring and control, agriculture, flood monitoring, health and disease, drought and desertification. The
third part concludes from all illustrated instances that climate change problems will be less understood and
managed without the application of RS and GIS. While humanity is still being plagued by climate change effects,
RS and GIS play a crucial role in its management for continued human survival. Key words: Climate, Climate
Change, Climate Change Adaptation, Geographical Information System and Remote Sensing.
In India, agriculture is one of the major application areas of the remote sensing technology. Various national level agricultural applications have been developed which showcases the use of remote sensing data provided by the sensors/satellites launched by the country’s space agency, Indian Space Research Organisation (ISRO)
Iirs overview -Remote sensing and GIS application in Water Resources ManagementTushar Dholakia
Remote sensing and GIS application in Water Resources Management- By S.P. Aggarval spa@iirs.gov.in Indian Institute of Remote sensing ISRO, Department of space, Dehradun
From pixels to point clouds - Using drones,game engines and virtual reality t...ARDC
Presentation by Dr Tim Brown
Full webinar: https://www.youtube.com/watch?v=bl_7ClXhQlA&list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435&index=11
Individual snippet:https://youtu.be/PVf4zYNJlmM?list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435
Extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather in the observed record (satellite, reanalysis products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Thus far, extreme weather events have been typically specified by the community through hand-coded, multi-variate threshold conditions. Such criteria are usually subjective, and often there is little agreement in the community on the specific algorithm that should be used. We propose the use of a different approach: machine learning (and in particular deep learning) for solving this important problem. If human experts can provide spatio-temporal patches of a climate dataset, and associated labels, we can turn to a machine learning system to learn the underlying feature representation. The trained Machine Learning (ML) system can then be applied to novel datasets, thereby automating the pattern detection step. Summary statistics, such as location, intensity and frequency of such events can be easily computed as a post-process.
We will report compelling results from our investigations of Deep Learning for the tasks of classifying tropical cyclones, atmospheric rivers and weather front events. For all of these events, we observe 90-99% classification accuracy. We will also report on progress in localizing such events: namely drawing a bounding box (of the correct size and scale) around the weather pattern of interest. Both tasks currently utilize multi-layer convolutional networks in conjunction with hyper-parameter optimization. We utilize HPC systems at NERSC to perform the optimization across multiple nodes, and utilize highly-tuned libraries to utilize multiple cores on a single node. We will conclude with thoughts on the frontier of Deep Learning and the role of humans (vis-a-vis AI) in the scientific discovery process.
NASA Advanced Computing Environment for Science & Engineeringinside-BigData.com
In this deck from the 2017 Argonne Training Program on Extreme-Scale Computing, Rupak Biswas from NASA presents: NASA Advanced Computing Environment for Science & Engineering.
""High performance computing is now integral to NASA’s portfolio of missions to pioneer the future of space exploration, accelerate scientific discovery, and enable aeronautics research. Anchored by the Pleiades supercomputer at NASA Ames Research Center, the High End Computing Capability (HECC) Project provides a fully integrated environment to satisfy NASA’s diverse modeling, simulation, and analysis needs. In addition, HECC serves as the agency’s expert source for evaluating emerging HPC technologies and maturing the most appropriate ones into the production environment. This includes investigating advanced IT technologies such as accelerators, cloud computing, collaborative environments, big data analytics, and adiabatic quantum computing. The overall goal is to provide a consolidated bleeding-edge environment to support NASA's computational and analysis requirements for science and engineering applications."
Dr. Rupak Biswas is currently the Director of Exploration Technology at NASA Ames Research Center, Moffett Field, Calif., and has held this Senior Executive Service (SES) position since January 2016. In this role, he in charge of planning, directing, and coordinating the technology development and operational activities of the organization that comprises of advanced supercomputing, human systems integration, intelligent systems, and entry systems technology. The directorate consists of approximately 700 employees with an annual budget of $160 million, and includes two of NASA’s critical and consolidated infrastructures: arc jet testing facility and supercomputing facility. He is also the Manager of the NASA-wide High End Computing Capability Project that provides a full range of advanced computational resources and services to numerous programs across the agency. In addition, he leads the emerging quantum computing effort for NASA.
Watch the video: https://wp.me/p3RLHQ-hua
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Richard Reinhart, NASA Glenn Research Center: "Space Communications and Navigation (SCaN) Testbed." Presented at the 2013 International Space Station Research and Development Conference, http://www.astronautical.org/issrdc/2013.
VISION / AMBITION
-Australia the first drone-sensed nation (cm-scale)
-Pre-competitive data release for industry, environmental management, education & research
-Conventional survey & remote sensing techniques at ultra-high resolution and flexibility (time-series, rapid response etc)
-Next gen “UNDERCOVER” techniques (minerals and water resources)
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...
Environmental Remote Sensing
1. Environmental Remote Sensing
GEOG 2021
Dr. P. Lewis
Pearson Building, room 114, x 30585
plewis@geog.ucl.ac.uk
Dr. M. Disney
Pearson Building, room 113, x 30592
mdisney@geog.ucl.ac.uk
2. 2
Structure of Course
First half of course introduces remote sensing
Second half focuses on a practical example using remote
sensing data
8 lectures
Mondays 10-11am, G07 Pearson Building
7 practicals
Thursdays 11-1pm, in PB UNIX computer lab (room 110a)
help sessions (PB UNIX lab 110a)
- extended practical project - all of the above times
approximately from reading week onwards
3. 3
Structure of Course
Assessment
exam (60%) and coursework (40%)
coursework write-up on the extended practical
submission date – Weds 24th
March (12:00??)
Course webpage
http://www.geog.ucl.ac.uk/~plewis/geog2021
4. 4
Lecture Plan
Intro to RS
Radiation Characteristics
Spectral Information & intro to classification
Spatial Information
Classification
Modelling I
reading week
Modelling II
5. 5
Purpose of 2021
Enable practical use of remote sensing data through
background theory & typical operations
enchancement (spectral / spatial)
classification
practical example in environmental science
Use ENVI on Sun UNIX workstations
widely-used
good range of functionality
relatively easy to use (GUI)
6. 6
Reading and browsing
Campbell, J. B. (1996) Introduction to Remote Sensing (2nd Ed), London:Taylor and
Francis.
R. Harris, 1987. "Satellite Remote Sensing, An Introduction", Routledge & Kegan
Paul.
Jensen, J. R. (2000) Remote Sensing of the Environment: An Earth Resource
Perspective, 2000, Prentice Hall, New Jersey. (Excellent on RS but no image
processing).
Jensen, J. R. (2005, 3rd ed.) Introductory Digital Image Processing, Prentice Hall,
New Jersey. (Companion to above) BUT mostly available online at
http://www.cla.sc.edu/geog/rslab/751/index.html
Lillesand, T. M., Kiefer, R. W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing
and Image Interpretation, John Wiley, New York.
Mather, P. M. (1999) Computer Processing of Remotely sensed‑ Images, 2nd
Edition. John Wiley and Sons, Chichester.
W.G. Rees, 1996. "Physical Principles of Remote Sensing", Cambridge Univ. Press
7. 7
• Links (on the course webpage)...
– CEOS Remote Sensing notes
– CEOS disaster page
– NASA Remote Sensing Tutorial - Remote
Sensing and Image Interpretation Analysis
– ASPRS remote sensing core curriculum
– Manchester Information Datasets and
Associated Services (MIDAS)
– Remote Sensing Glossary (CCRS)
(comprehensive links)
Reading and browsing
8. 8
• Web
• Tutorials
• http://rst.gsfc.nasa.gov/
• http://earth.esa.int/applications/data_util/SARDOCS/spaceborne/Radar_Courses/
• http://www.crisp.nus.edu.sg/~research/tutorial/image.htm
• http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php
• http://octopus.gma.org/surfing/satellites/index.html
• Glossary of alphabet soup acronyms!
http://www.ccrs.nrcan.gc.ca/glossary/index_e.php
• Other resources
• NASA www.nasa.gov
• NASAs Visible Earth (source of data): http://visibleearth.nasa.gov/
• European Space Agency earth.esa.int
• NOAA www.noaa.gov
• Remote sensing and Photogrammetry Society UK www.rspsoc.org
• IKONOS: http://www.spaceimaging.com/
• QuickBird: http://www.digitalglobe.com/
Reading and browsing
9. 9
• GLOVIS (USGS Global Visualisation Viewer)
– http://glovis.usgs.gov/
– All global Landsat data now available – hugely useful resource
– Plus ASTER, MODIS (moderate/coarse resolution but global coverage)
• NASA Distributed Active Archive Centres – huge range of free NASA data:
– http://nasadaacs.eos.nasa.gov/about.html (overview)
– https://lpdaac.usgs.gov/ (land)
– http://podaac.jpl.nasa.gov/ (oceans)
– http://www.nsidc.org/daac/ (snow and ice)
• UK/NERC
– NERC National Centre for Earth Observation (NCEO)
– http://www.nceo.ac.uk
– Earth Observation Data Centre
– http://www.neodc.rl.ac.uk/ (UK/European focused, with ESA data, airborne, various
campaign surveys etc. – may require registration)
Free data sources on the web
10. 10
Fundamentals
• Remote sensing is the acquisition of data, "remotely"
• Earth Observation / Remote Sensing (EO/RS)
• For EO, "remotely" means using instruments (sensors) carried by platforms
• Usually we will think in terms of satellites, but this doesn't have to be the case
– aircraft, helicopters, ...
12. 12
Remote Sensing: examples
•Platform depends on application
•What information do we want?
•How much detail?
•What type of detail?
upscale
http://www-imk.fzk.de:8080/imk2/mipas-b/mipas-b.htm
upscale upscale
13. 13
Why use satellite RS ?
• Source of spatial and temporal information
– land surface, oceans, atmosphere, ice
• monitor and develop understanding of environment
• information can be accurate, timely, consistent and large (spatial)
scale
• some historical data (60s/70s+)
• move to quantitative applications
– data for climate (temperature, atmospheric gases, land surface,
aerosols….)
• some 'commercial' applications
– Weather, agricultural monitoring, resource management
14. 14
But….
• Remote sensing has various issues
– Can be expensive
– Can be technically difficult
– NOT direct
• measure surrogate variables
• e.g. reflectance (%), brightness temperature (Wm-2
⇒ o
K),
backscatter (dB)
• RELATE to other, more direct properties.
15. 15
Basic Concepts: EM Spectrum
Sometime use frequency, f=c/λ,
where c=3x108
m/s (speed of light)
λ 1 nm, 1mm, 1m
f 3x1017
Hz, 3x1011
Hz, 3x108
Hz,
19. 19
A Remote Sensing System
• Energy source
• platform
• sensor
• data recording / transmission
• ground receiving station
• data processing
• expert interpretation / data users
20. 20
Physical Basis
• measurement of EM radiation
– scattered, reflected
• energy sources
– Sun, Earth
– artificial
• source properties
– vary in intensity AND across wavelengths
21. 21
EM radiation
• emitted, scattered or absorbed
• intrinsic properties (emission, scattering,
absorption)
– vary with wavelength
– vary with physical / chemical properties
– can vary with viewing angle
22. 22
Data Acquisition
• RS instrument measures energy
received
– 3 useful areas of the spectrum:-
1) Visible / near / mid infrared
– passive
• solar energy reflected by
the surface
• determine surface (spectral)
reflectance
– active
• LIDAR - active laser pulse
• time delay (height)
• induce florescence
(chlorophyll)
2) Thermal infrared
– energy measured - temperature
of surface and emissivity
3) Microwave
– active
• microwave pulse
transmitted
• measure amount scattered
back
• infer scattering
– passive
• emitted energy at shorter
end of microwave spectrum
23. 23
Image Formation
• Photographic (visible / NIR, recorded on film, (near) instantaneous)
• whiskbroom scanner
– visible / NIR / MIR / TIR
– point sensor using rotating mirror, build up image as mirror scans
– Landsat MSS, TM
• Pushbroom scanner
– mainly visible / NIR
– array of sensing elements (line) simultaneously, build up line by line
– SPOT
24. 24
• real aperture radar
– microwave
– energy emitted across-track
– return time measured (slant range)
– amount of energy (scattering)
• synthetic aperture radar
– microwave
– higher resolution - extended antenna
simulated by forward motion of platform
– ERS-1, -2 SAR (AMI), Radarsat SAR, JERS
SAR
Image Formation: RADAR
25. 25
Quantization: digital data
– received energy is a continuous signal (analogue)
– quantise (split) into discrete levels (digital)
– Recorded levels called digital number (DN)
– downloaded to receiving station when in view
– 'bits'...
• 0-1 (1 bit), 0-255 (8 bits), 0-1023 (10 bits), 0-4095 (12 bit)
– quantization between upper and lower limits (dynamic range)
• not necessarily linear
– DN in image converted back to meaningful energy measure through calibration
• account for atmosphere, geometry, ...
– relate energy measure to intrinsic property (reflectance)
26. 26
Image characteristics
• pixel - DN
• pixels - 2D grid (array)
• rows / columns (or lines / samples)
• 3D (cube) if we have more than 1 channel
• dynamic range
– difference between lowest / highest DN
27. 27
Example Applications
• visible / NIR / MIR - day only, no cloud cover
– vegetation amount/dynamics
– geological mapping (structure, mineral / petroleum
exploration)
– urban and land use (agric., forestry etc.)
– Ocean temperature, phytoplankton blooms
– meteorology (clouds, atmospheric scattering)
– Ice sheet dynamics
30. 30
• Thermal infrared - day / night, rate of heating /
cooling
– heat loss (urban)
– thermal plumes (pollution)
– mapping temperature
– geology
– forest fires
– meteorology (cloud temp, height)
Example Applications
31. 31
• Active microwave - little affected by atmospheric
conditions, day / night
– surface roughness (erosion)
– water content (hydrology) - top few cms
– vegetation - structure (leaf, branch, trunk properties)
– Digital Elevation Models, deformation, volcanoes,
earthquakes etc. (SAR interferometry)
Example Applications