:: Tutorial Remote Sensing & GIS ::

GIS Tutorials and Exercises

(Source : http://hcl.harvard.edu/libraries/maps/gis/tutorials.cfm)

Introduction to Geographic Information Systems (GIS) Tutorial

The Introduction to GIS Tutorial consists of a three-part movie accompanied by hands-on exercises.

These tutorials require the Flash Player browser plugin.

Web resources mentioned during the movie are available at GIS Links and Resources.

Exercises
The exercises accompanying the movie are compressed into a ZIP file. To work with the exercises:

  1. Make sure that you have the program ArcGIS, which is required to do the exercises. ArcGIS is available from the Harvard GSD Web site’s Harvard ArcGIS/ArcView Download Page.
  2. Download the exercises from IntroToGIS.zip
  3. Unzip the files into your local c:\temp directory, where two two folders will be created. The “workshop” folder contains the exercise data, and the “instructions” folder contains PDF instructions to lead you through the exercises.
  4. Complete Exercise 1 after Part 1 of the movie.
  5. Complete Exercise 2 after Part 2 of the movie.
  6. Complete Exercises 3 and 4 midway through Part 3 of the movie when prompted to do so.


Introduction to GIS

By Kardi Teknomo, PhD.

(Source : http://people.revoledu.com/kardi/tutorial/GIS/index.html)

What is GIS, GIS data model, Vector Data, Point, Line, Polygon, Vector Representation, Raster Data, TIN model, Scale of Map, Introduction to ArcGIS, Exploring ArcMap, Basic GIS technique, Extract part of the map, Query by Attribute, Create layer from selection, Convert Layer to Shape File, Create a New Map from Extracted Layers, Create Choropleth map, Showing only selected layers, Create Dot Density map, Change Symbol of Points, Buffer, Geoprocessing, Add Field and Calculate Value of Table, Statistic, Sort and Summary, GIS Resources

This tutorial introduces GIS with feature to use Arc GIS (Arc View, Arc Editor or Arc Info). I assumed you know nothing about GIS.

What is GIS, GIS data model,Vector Data, Point, Line, Polygon, What things do we represent as Point, Line or Polygon?, Raster Data, TIN model, Scale of Map, Introduction to ArcGIS, Exploring ArcMap, Basic GIS technique, Extract part of the map, Query by Attribute, Create layer from selection, Convert Layer to Shape File, Create a New Map from Extracted Layers, Create Choropleth map, Showing only selected layers, Create Dot Density map, Change Symbol of Points , Buffer, Geoprocessing, Add Field and Calculate Value of Table, Statistic, Sort and Summary

Remote Sensing

(source : http//www.gisdevelopment.net/tutorials/tuman008.htm)

An Introduction

Remote Sensing is the science and art of acquiring information (spectral, spatial, temporal) about material objects, area, or phenomenon, without coming into physical contact with the objects, or area, or phenomenon under investigation. Without direct contact, some means of transferring information through space must be utilised. In remote sensing, information transfer is accomplished by use of electromagnetic radiation (EMR). EMR is a form of energy that reveals its presence by the observable effects it produces when it strikes the matter. EMR is considered to span the spectrum of wavelengths from 10-10 mm to cosmic rays up to 1010 mm, the broadcast wavelengths, which extend from 0.30-15 mm.

Types

  1. In respect to the type of Energy Resources:
    Passive Remote Sensing: Makes use of sensors that detect the reflected or emitted electro-magnetic radiation from natural sources.

    Active remote Sensing: Makes use of sensors that detect reflected responses from objects that are irradiated from artificially-generated energy sources, such as radar.

  2. In respect to Wavelength Regions:Remote Sensing is classified into three types in respect to the wavelength regions
    • Visible and Reflective Infrared Remote Sensing.
    • Thermal Infrared Remote Sensing.
    • Microwave Remote Sensing.

Some Interesting Links :

Bands Used in Remote Sensing
Emission of EMR (Electo-Magnetic Radiation) from gases is due to atoms and molecules in the gas. Atoms consist of a positively charged nucleus surrounded by orbiting electrons, which have discrete energy states. Transition of electrons from one energy state to the other leads to emission of radiation at discrete wavelengths. The resulting spectrum is called line spectrum. Molecules possess rotational and vibrational energy states. Transition between which leads to emission of radiation in a band spectrum. The wavelengths, which are emitted by atoms/molecules, are also the ones, which are absorbed by them. Emission from solids and liquids occurs when they are heated and results in a continuous spectrum. This is called thermal emission and it is an important source of EMR from the viewpoint of remote sensing.

The Electro-Magnetic Radiation (EMR), which is reflected or emitted from an object, is the usual source of Remote Sensing data. However, any medium, such as gravity or magnetic fields, can be used in remote sensing.

Remote Sensing Technology makes use of the wide range Electro-Magnetic Spectrum (EMS) from a very short wave “Gamma Ray” to a very long ‘Radio Wave’.

Wavelength regions of electro-magnetic radiation have different names ranging from Gamma ray, X-ray, Ultraviolet (UV), Visible light, Infrared (IR) to Radio Wave, in order from the shorter wavelengths.

The optical wavelength region, an important region for remote sensing applications, is further subdivided as follows:

Name Wavelength (mm)
Optical wavelength 0.30-15.0
Reflective
1.    Portion Visible
2.    Near IR
3.    Middle IR
0.38-3.00
0.38-0.72
0.72-1.30
1.30-3.00
Far IR (Thermal, Emissive) 7.00-15.0

Microwave region (1mm to 1m) is another portion of EM spectrum that is frequently used to gather valuable remote sensing information.

Spectral Characteristics vis-à-vis different systems.
The sunlight transmission through the atmosphere is effected by absorption and scattering of atmospheric molecules and aerosols. This reduction of the sunlight’s intensity s called extinction.

The interrelationship between energy sources and atmospheric absorption characteristics is shown in Figure 3

  • Figure 3(a) shows the spectral distribution of the energy emitted by the sun (black body at 58000 K and by earth features black body at 3000 K). These two curve represent the most common sources of energy used in remote sensing.
  • Figure 3(b) shows the spectral regions in which the atmosphere blocks the energy are shaded. Remote-sensing data acquisition is limited to the unblocked spectral regions called atmospheric windows.
  • Figure 3(c) shows that the spectral sensitivity range of the eye (the ‘visible’ range) coincides with an ‘atmospheric window’ and the peak level of energy from the sun.
  • Figure3 (d) shows the example of atmospheric transmission characteristics and notes some of the important ‘atmospheric windows’. An ‘atmospheric window’ is a portion of Electro-magnetic spectrum in which the radiation passing through the atmosphere is not significantly altered by reflection, or absorption, or scattered by atmospheric constituents. Some useful atmospheric windows are given in the table.

The important point to note from the figures is the interaction and the interdependence between the primary sources of Electro-magnetic energy, the atmospheric windows through which source energy may be transmitted to and from the earth’s surface features, and the spectral sensitivity of the sensors available to detect and record the energy. One cannot select the sensors to be used in any given remote-sensing task arbitrarily; one must instead consider

  1. the available spectral sensitivity of the sensors,
  2. the presence or absence of atmospheric windows in the spectral range(s) in which one wishes to sense, and
  3. the source, magnitude, and spectral composition of the energy availabe in these ranges.

Ultimately, however, the choice of spectral range of the sensor must be based on the manner in which the energy interacts with the features under investigation.

Energy Interactions, Spectral Reflectance and Colour Readability in Satellite Imagery
All matter is composed of atoms and molecules with particular compositions. Therefore, matter will emit or absorb electro-magnetic radiation on a particular wavelength with respect to the inner state. All matter reflects, absorbs, penetrates and emits Electro-magnetic radiation in a unique way. Electro-magnetic radiation through the atmosphere to and from matters on the earth’s surface are reflected, scattered, diffracted, refracted, absorbed, transmitted and dispersed. For example, the reason why a leaf looks green is that the chlorophyll absorbs blue and red spectra and reflects the green. The unique characteristics of matter are called spectral characteristics.

Energy Interactions
When electro-magnetic energy is incident on any given earth surface feature, three fundamental energy interactions with the feature are possible. See Figure 4

Spectral Reflectance & Colour Readability
Two points about the above given relationship (expressed in the form of equation) should be noted.

  1. The proportions of energy reflected, absorbed, and transmitted will vary for different earth features, depending upon their material type and conditions. These differences permit us to distinguish different features on an image.
  2. The wavelength dependency means that, even within a given feature type, the proportion of reflected, absorbed, and transmitted energy will vary at different wavelengths.

Thus, two features may be distinguishable in one spectral range and be very different on another wavelength brand. Within the visible portion of the spectrum, these spectral variations result in the visual effect called COLOUR. For example we call blue objects ‘blue’ when they reflect highly in the ‘green’ spectral region, and so on. Thus the eye uses spectral variations in the magnitude of reflected energy to discriminate between various objects.

A graph of the spectral reflectance of an object as a function of wavelength is called a spectral reflectance curve. The configuration of spectral reflectance curves provides insight characteristics of an object and has a strong influence on the choice of wavelength region(s) in which remote sensing data are acquired for a particular application. This is illustrated in figure 5, which shows highly generalized spectral reflectance curves of deciduous and coniferous trees. (In the discussion, we use the terms deciduous and coniferous somewhat loosely, referring to broad-leaved trees, such as Oak and Maple, as deciduous and to needle-bearing trees, such as pine and spruce, as coniferous.). It should be noted that the curve for each of these object types is plotted as a ‘ribbon’ (or ‘envelope’) of values, not as a single line. This is because spectral reflectances vary somewhat within a given material class. That is, the spectral reflectance of one deciduous tree species and another will never be identical. Nor will the spectral reflectance of trees of the same species ever be exactly equal.

Figure 6 shows the typical spectral reflectance curves for three basic types of earth feature:

  • Green vegetation
  • Soil
  • Water.

The lines in this figure represent average reflectance curves compiled by measuring large sample features. It should be noted how distinctive the curves are for each feature. In general, 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.

Colour Discrimination based on Wavelengths of Spectral Reflectances.
(IRS-IA/IB LISS I and LISSII*)

Band

wavelength (µm)

Principal

1

0.45-0.52

Sensitive to sedimentation, deciduous/coniferous forest cover discrimination, soil vegetation differentiation

2

0.52-0.59

Green reflectance by healthy vegetation, vegetation vigour, rock-soil discrimination, turbidity and bathymetry in shallow waters

3

0.62-0.68

Sensitive to chlorophyll absorption: plant species discrimination, differentiation of soil and geological boundary

4

0.77-0.86

Sensitive to green biomass and moisture in vegetation, land and water contrast, landform/geomorphic studies.

*Spatial Resolution of Linear imaging self scanning (LISS): LISS-I (72.5 m) and LISS-II (36.25m)

Electro-Magnetic Remote Sensing of Earth’s Resources — Process & Elements

Major Components of Remote Sensing Technology:
The following are major components of Remote sensing System:

  1. Energy Source
  1. Passive System: sun, irradiance from earth’s materials;
  2. Active System: irradiance from artificially generated energy sources such as radar.
  1. Platforms:(Vehicle to carry the sensor) (truck, aircraft, space shuttle, satellite, etc.)
  2. Sensors:(Device to detect electro-magnetic radiation) (camera, scanner, etc.)
  3. Detectors: (Handling signal data) (photographic, digital, etc.)
  4. Processing:(Handling Signal data) (photographic, digital etc.)
  5. Institutionalisation: (Organisation for execution at all stages of remote-sensing technology: international and national orrganisations, centres, universities, etc.).

Platforms
The vehicles or carriers for remote sensors are called the platforms. Typical platforms are satellites and aircraft, but they can also include radio-controlled aeroplanes, balloons kits for low altitude remote sensing, as well as ladder trucks or ‘cherry pickers’ for ground investigations. The key factor for the selection of a platform is the altitude that determines the ground resolution and which is also dependent on the instantaneous field of view (IFOV) of the sensor on board the platform.

Salient feature of some important satellite platforms.

Features

Landsat1,2,3

Landsat 4,5

SPOT

IRS-IA

IRS-IC

Natre Sun Sys Sun Sys Sun Sys Sun Sys Sun Sys
Altitude (km) 919 705 832 904 817
Orbital period (minutes) 103.3 99 101 103.2 101.35
inclination (degrees 99 98.2 98.7 99 98.69
Temporal resolution (days) 18 16 26 22 24
Revolutions 251 233 369 307 341
Equatorial crossing (AM) 09.30 09.30 10.30 10.00 10.30
Sensors RBV,MSS MSS,TM HRV LISS-I,LISS-II LISS-III,PAN,WIFS

SENSORS

ACTIVE SENSORS
(Detect the reflected or emitted electromagnetic radiation from natural sources.)

PASSIVE SENSORS
(Detect reflected responses from objects that are irradiated from artificially-generated energy sources such as radar.)

Passive

Non-Scanning

    • Non-Imaging. (They are a type of profile recorder, ex. Microwave Radiometer. Magnetic sensor.Gravimeter.Fourier Spectrometer.
    • Imaging. (Example of this are the cameras which can be: Monochrome, Natural Colour, Infrared etc.)

Scanning

    • Imaging. Image Plane scanning.Ex. TV CameraSolid scanner.Object Plane scanning.Ex. Optical Mechanical ScannerMicrowave radiometer.
Active

Non-Scanning

  • Non-Imaging. (They are a type of profile recorder, ex. Microwave Radiometer.Microwave Altimeter.Laser Water Depth Meter.Laser Distance Meter. Scanning
  • Imaging. (It is a radar ex. Object Plane scanning:
  1. Real Aperture Radar.
  2. Synthetic Aperture Radar.

Image Plane Scanning:

  1. Passive Phased Array Radar.

Resolution
In general resolution is defined as the ability of an entire remote-sensing system, including lens antennae, display, exposure, processing, and other factors, to render a sharply defined image. Resolution of a remote-sensing is of different types.

  1. Spectral Resolution: of a remote sensing instrument (sensor) is determined by the band-widths of the Electro-magnetic radiation of the channels used. High spectral resolution, thus, is achieved by narrow bandwidths width, collectively, are likely to provide a more accurate spectral signature for discrete objects than broad bandwidth.
  2. Radiometric Resolution: is determined by the number of discrete levels into which signals may be divided.
  3. Spatial Resolution: in terms of the geometric properties of the imaging system, is usually described as the instantaneous field of view (IFOV). The IFOV is defined as the maximum angle of view in which a sensor can effectively detect electro-magnetic energy.
  4. Temporal Resolution: is related ot the repetitive coverage of the ground by the remote-sensing system. The temporal resolution of Landsat 4/5 is sixteen days.

An Ideal Remote Sensing System
Having introduced some basic concepts, we now have the necessary elements to conceptualize an ideal remote sensing system. In doing so, we can then appreciate some of the problems encountered in the design and application of the various real remote-sensing systems examined in subsequent chapters.

The basic components of an ideal remote-sensing system are shown in figure 8. These include the following components.

  • A uniform energy source. This source will provide energy over all wavelengths, at a constant, known, high level of output, irrespective of time and place.
  • A non-interfering atmosphere. This will be an atmosphere that will not modify the energy from the source in any manner, whether that energy is on its way to earth’s surface or coming from it. Again, ideally this will hold irrespective of wavelength, time, place, and sensing altitude involved.
  • A series of unique energy/matter interaction at the earth’s surface. These interactions will generate reflected and/or emitted signals that are not only selective in respect to wavelengths, but also are known, invariant, and unique to each and every earth surface feature type and subtype of interest.
  • A super sensor. This will be a sensor, highly sensitive to all wavelengths, yielding spatially detailed data on the absolute brightness (or radiance) from a scene (a function of wavelength), throughout the spectrum. This super sensor will be simple and reliable, require, virtually no power or space, and be accurate and economical to operate.
  • A real-time data handling system. In this system, the instant the radiance versus wavelength response over a terrain element is generated, it will be processed into an interpretable format and recognized as being unique to the particular terrain element from which it comes. This processing will be performed nearly instantaneously (real time), providing timely information. Because of the consistent nature of the energy/matter interactions, there will be no need for reference data in the analytical procedure. The derived data will provide insight into the physical-chemical-biological state of each feature of interest.
  • Multiple data users. These people will have comprehensive knowledge of both their respective disciplines and of remote-sensing data acquisition and analysis techniques. The same set of data will become various forms of information for different users, because of their vast knowledge about the particular earth resources being used.

Unfortunately, an ideal remote-sensing system, as described above, does not exist. Real remote-sensing systems fall short of the ideal at virtually every point in the sequence outlined.

Remote Sensing Satellites
A satellite with remote sensors to observe the earth is called a remote-sensing satellite, or earth observation satellite. Remote-Sensing Satellites are characterised by their altitude, orbit and sensor.

TRIOS Series (1960-1965)
The Television and Infrared Observation Satelites.

NOAA It is the first generation of National Oceanic and Atmospheric Administration satellites and was as the first operation operational remote sensing satellite system.

The third generation NOAA satellites are also successfully used for vegetation monitoring, apart from meteorological monitoring. It is equipped with Advanced Very High Resolution Radiometer (AVHRR) sensors, and is established at an altitude of 850 km. In polar orbit.

GMS Geo-synchronous meteorological satellite. It is established at an altitude of 36,000 km, and its main purpose is meteorological observations

Landsat is established at an altitude of 700 Kms is a polar orbit and is used mainly for land area observation.

Other remote sensing satellite series in operations are: SPOT, MOS, JERS, ESR, RADARSAT, IRS etc.

Some Interesting Links :

Remote Sensing: An Overview

(source : http://www.csc.noaa.gov/products/sccoasts/html/remote.htm)


Different Types of   Remotely Sensed Images
Examples of Remotely Sensed Data Sets

What is Remote Sensing?

Remote sensing is a technique used to collect data about the earth without taking a physical sample of the earth�s surface. A sensor is used to measure the energy reflected from the earth. This information can be displayed as a digital image or as a photograph. Sensors can be mounted on a satellite orbiting the earth, or on a plane or other airborne structure.

There are two basic types of sensors: passive and active sensors. Passive sensors record radiation reflected from the earth’s surface. The source of this radiation must come from outside the sensor; in most cases, this is solar energy. Because of this energy requirement, passive solar sensors can only capture data during daylight hours. The Thematic Mapper (TM) sensor system on the Landsat satellite is a passive sensor. The land cover and change analysis data provided on this CD-ROM were classified using Landsat TM imagery. For more information about the land cover and change analysis data, click here. Example of a Passive Sensor
Example of a Passive Sensor
Example of an Active Sensor
Example of an Active Sensor
Active sensors are different from passive sensors. Unlike passive sensors, active sensors require the energy source to come from within the sensor. For example, a laser-beam remote sensing system is an active sensor that sends out a beam of light with a known wavelength and frequency. This beam of light hits the earth and is reflected back to the sensor, which records the time it took for the beam of light to return. Topographic LIDAR laser beach mapping data included on this CD-ROM were collected with an active sensor. For more information about LIDAR data, click here.

For more detailed information about remote sensing, click here.

What Can You Do with Remotely Sensed Data?

Example of the Digital Shoreline for South Carolina
South Carolina Shoreline
was Delineated from
Aerial Photography

Coastal Applications
Remote sensing data can be an asset to coastal resource managers by providing a pictorial representation of coastal processes. For example, remote sensing data can be used to monitor and evaluate shoreline changes both pre- and post-beach renourishment used to study shoreline and bluff erosion. Other coastal applications of remote sensing data include mapping intertidal zones and their features, delineating the shoreline, mapping coastal features (including vegetation), studying sediment transport, developing bathymetric models, extracting building outlines for use in a geographic information system (GIS), and evaluating the effects of human impact.

Example of Sea Surface Temperature for the Carolina Coast
Sea Surface Temperatures
for the Carolina Coast

Oceanic Applications
Large scale events such as ocean circulation, current systems, upwelling and eddy formation can be better understood by using satellite imagery. Learning how these events work could provide managers insight into how to better manage ocean resources. Other oceanic properties satellite imagery can measure include chlorophyll concentrations, water temperature, wave heights, sea surface winds, and sea ice.

Example of How LIDAR Can Be Used to Map Beach Front Property
LIDAR Data Used to Map
Beach Front Property

Hazard Assessment
As more people migrate toward the coast, it is important for coastal resource managers and other planners to understand how hazards could impact coastal communities. A hazard event could include large storms, earthquakes, erosion, and flooding. Remote sensing can be used both to aid in identifying resources prior to an event and also to assess the damage following an event.

North Inlet National Estuarine Research Reserve Boundary Overlaid on Satellite Imagery
North Inlet National Estuarine
Research Reserve Boundary
Overlaid on Satellite Imagery

Natural Resource Management
With the increase in urban sprawl and recreational use of natural areas, habitat assessment and natural resource management are becoming important topics for coastal resource managers. Remote sensing data sets can be used to monitor urban sprawl, map and inventory wetlands, and delineate wildlife habitat. Once the land cover has been mapped, repeated collection of remote sensing data can be used to monitor and study the various types of habitat and vegetation.

Incorporating Remote Sensing Data into a GIS

Remote sensing and GIS technologies were initially developed for different purposes. However, both of these resources can provide information about the earth’s resources. Advancements in computer hardware and software technology now make it possible for data from these sources to be easily integrated.

Most GIS software packages allow remotely sensed data to be imported, or at least viewed, within the software application. This ability allows the analyst to overlay remote sensing data layers with other spatial data layers. Analysts use remotely sensed imagery with GIS data sets for a variety of reasons, including providing a continuous regional view of the areas and extracting GIS data layers, such as contours or building footprints.

South Carolina Digital Ortho Quarter Quad (DOQQ) Data Overlaid with a Road Coverage
South Carolina Digital Ortho Quarter Quads (DOQQ) Data Overlaid with a
Road Coverage. DOQQ Data Are Available on the South Carolina
Department of Natural Resources Web Page.

(source : http://rst.gsfc.nasa.gov/Front/tofc.html)

TABLE OF CONTENTS

(source : http://rst.gsfc.nasa.gov/Front/tofc.html)

Dedication and Foreword

Dedication and Foreword

Overview

HOW TO USE THIS TUTORIAL AND A SYNOPSIS OF THE PRINCIPLES AND APPLICATIONS OF REMOTE SENSING.

Introduction: Theoretical and Technical Perspectives of Remote Sensing; Special Applications

The Concept of Remote Sensing

Principles of Remote Sensing: The Photon and Radiometric Parameters / Optional Reading: The Quantum Physics underlying Remote Sensing; Use of Spectroscopy in Determining Quantum Levels / Transmittance, Absorptance, and Reflectance / The Electromagnetic Spectrum: Distribution of Radiant Energies / Spectral Signatures

Sensor Technology; Types of Resolution

Processing and Classification of Remotely Sensed Data; Pattern Recognition; Approaches to Data/Image Interpretation

History of Remote Sensing; Remote Sensing Systems:

In the Beginning; Launch Vehicles / Table: History of Remote Sensing into the 1970s/ Multispectral Images / Film as a Recording Medium / Color & False Color Composites / Apollo 9 Multispectral Images / Earth Resources Technology Satellite (ERTS-1) / Multi-Spectral Scanner (MSS) / A Landsat Image / MSS Histograms / Table: Best MSS Bands for Identifying Surface Features / Thematic Mapper (TM) / Examples of TM Imagery / Other Remote Sensing Systems: MOMS and SPOT / Envisat, IRS-1, JERS, RESURS, OKEAN, CBERS, and MicroSat (AlSat-1) Series / Hyperspectral Imaging / Radar and Thermal Systems / Meteorological, Oceanographic, and Earth Systems Satellites / The Systems (Multisource) Approach to Remote Sensing / Humans in Space: Long Term Mobile and Fixed Stations (Salut; Soyuz; Skylab; Apollo-Soyuz; Space Shuttle (STS); MIR; International Space Station / Military Intelligence Satellites / The Commercialization of Space / Geophysical Remote Sensing: External Fields; Magnetics / Geophysical Remote Sensing: Gravity / Geophysical Remote Sensing: Crustal Dynamics; Seismology / Medical Applications of Remote Sensing

Concluding Remarks

Section 1: Image Processing and Interpretation – Morro Bay, California

Section 2: Geologic Applications I – Stratigraphy & Structure

Section 3: Vegetation Applications – Agriculture, Forestry, and Ecology

Section 4: Urban and Land Use Applications

Section 5: Geologic Applications II – Mineral & Petroleum Exploration

Section 6: Space Flight Across the U.S – Boston to Denver to San Francisco; Landsat Tours the World

Section 7: Regional Studies – Use of Mosaics from Landsat

Section 8: Radar and Microwave Remote Sensing

Section 9: The Warm Earth – Thermal Remote Sensing

Section 10: Aerial Photography as Primary & Ancillary Data Sources

Section 11: The Earth’s Surface in 3D – Stereo Systems and Topographic Mapping

Section 12: The Human Remote Senser in Space – Astronaut Photography
Guest Writer: Dr. Paul D. Lowman Jr.

Section 13: Collecting Data at the Surface – Ground Truth; The “Multi” Concept; Hyperspectral Imaging Spectroscopy

Section 14: The Water Planet – Meteorological, Oceanographic and Hydrologic Applications of Remote Sensing

Section 15: Geographic Information Systems – The GIS Approach to Decision Making

Maps and Attributes / Data Elements and Models / GIS Defined / A GIS Case Study in Africa / Decision Making; Suitability Determination / Conducting a GIS Analysis / The PP&L Siting Problem / A GIS/Remote Sensing Case Study in Archaeology: Burgundy, France / Using GIS to Study a Rare Flower Habitat on Block Island, Rhode Island

Section 16: Earth Systems Science – Earth Science Enterprise and the EOS Program
Guest Writer: Dr. Mitchell K. Hobish

Section 17: Use of Remote Sensing in Basic Science Studies I – Mega-Geomorphology

Section 18: Basic Science II: Impact Cratering

Distribution of Craters / Cratering Mechanics / Crater Morphology; Major Impact Structures / Shock Metamorphism / Petrography of the Manson Crater / Remote Sensing of Craters

Section 19: The Solar System and Planetary Exploration

Section 20: Astronomy and Cosmology: The Description, Origin, and Development of the Universe

Preface (including a review of Relativity and Quantum Physics) / Origin and Early Development of the Universe; Big Bang Eras; Expansion of Space / The Hubble Space Telescope; Birth, Life, and Death of Stars / The Genesis Mission / The Nature and Evolution of Galaxies / Images of Galaxies and Stars outside the Visible Light Range. / Special Features of Galaxies: Colliding Galaxies; Galactic Gases; Starbursts and Active Galactic Nucleus / Novae and Supernovae; Pulsars, Quasars, and Black Holes; Gamma Ray Bursts; and Colliding Stars / Spectral Analysis of Star Composition; Element Synthesis in Stars / Space-Time and Expansion / Evidence for the Big Bang; the Redshift; Galactic Distances; Age of the Universe; Cosmic Background Radiation; Expansion Models; Dark Matter and Energy / Recent Innovations about the Concept of “Universe”: Dark Energy and an Accelerating Universe?; Varieties of Universes (Multiverses) / Origin of Planetary Systems / Origin and Nature of Life on Planetary Bodies / Some Philisophical Implications concerning the Cause and Purpose of the Universe

Section 21: Remote Sensing into the 21st Century
Guest Writer: William E. Stoney




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