English  |  Español  |  Français

Appendices

Compiler:
Ned Horning, Center for Biodiversity and Conservation of the American Museum of Natural History

A.1 Abbreviations and Acronyms

This list includes acronyms common in remote sensing, and others included in this sourcebook.
A more complete remote sensing acronym reference can be found in “Glossary of the remote sensing terms” section of the Canada Centre for Remote Sensing web site.

Acronym Full name
ADEOS Advanced Earth Observation System
AIRS Atmospheric Infrared Sounder
ADEOS Advanced Earth Observation System
AIRS Atmospheric Infrared Sounder
AIRSAR Airborne Synthetic Aperture Radar
ALI Advanced Land Imager (on EO-1 satellite)
ALOS Advanced Land Observing Satellite
ASAR Envisat Advanced Synthetic Aperture Radar
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer
AVHRR Advanced Very High-Resolution Radiometer
BRDF Bidirectional Reflectance Distribution Function
CASI Compact Airborne Spectrographic Imager
CEOS Committee on Earth Observation Satellites
CIR Colour Infrared
DEM Digital Elevation Model
DN Digital Number (pixel value)
DTM Digital Terrain Model
EMR Electromagnetic Radiation
EMS Electromagnetic Spectrum
ENVISAT Environmental Satellite
EOS Earth Observing System
EOSP Earth Observing Scanning Polarimeter
ERS-1 Earth Remote Sensing Satellite
ERTS Earth Resources Technology Satellite
ETM Enhanced Thematic Mapper
FFT Fast Fourier Transform
FIR Far Infrared
FOV Field of View
GCP Ground Control Point
GIS Geographic Information System
GMS Geostationary Meteorological Satellite
GOES Geostationary Operational Environmental Satellite
GPS Global Positioning System
HIRS High-Resolution Infrared Sounder
HIS Hue, Intensity, Saturation
IKONOS A commercial satellite operating at one (panchromatic) / four (multispectral) metre resolution
IRS Indian Remote Sensing Satellite
IUCN World Conservation Union
JAXA Japan Aerospace Exploration Agency
JERS Japanese Earth Resources Satellite
LAI Leaf Area Index
LIDAR Light Detection and Ranging
LUT Look-up Table
LWIR Long-Wave Infrared
MISR Multi-Angle Imaging SpectroRadiometer
MLS Microwave Limb Scanner
MODIS Moderate Resolution Imaging Spectroradiometer
MSS Multispectral Scanner
NASA National Aeronautics and Space Administration
NDVI Normalized Difference Vegetation Index
NIR Near Infrared
NOAA National Oceanographic and Atmospheric Administration
PCA Principal Component Analysis
RADAR Radio Detection and Ranging
Radarsat Radar Satellite
RGB Red, Green, Blue
SAGE III Stratospheric Aerosol and Gas Experiment III
SAR Synthetic Aperture Radar
SIR-C Shuttle Imaging Radar-C
SPOT Système Pour l’Observation de la Terre
SRTM Shuttle Radar Topography Mission
SWIR Short-Wave Infrared
TES Tropospheric Emission Spectrometer
TIMS Thermal Infrared Multispectral Scanner
TIR Thermal Infrared
TM Thematic Mapper
TOMS Total Ozone Mapping Spectrometer
TOPEX Ocean Topography Experiment
TOPSAR Topographic Synthetic Aperture Radar
TRMM Tropical Rainfall Measurement Mission
UNEP-WCMC UNEP World Conservation Monitoring Centre
UV Ultraviolet
VIS Visible Spectrum
WFOV Wide Field Of View
WCPA World Consortium on Protected Areas
WDPA World Database of Protected Areas
WWF World Wide Fund for Nature
X-SAR X-Band Synthetic Aperture Radar

A.2 Glossary

The definitions in this glossary are relevant to remote sensing applications. Some of these terms have broader definitions than the one given. An extensive glossary for remote sensing terms can be found on the Canada Centre for Remote Sensing website.
  • Active sensor - Remote sensing instrument that emits its own energy and then measures that energy after it is reflected from features on the Earth’s surface.
  • Band - A single layer of an image created using a specific range of wavelengths. A colour digital image is composed of three bands that record red, green, and blue wavelengths of light.
  • Channel - This is typically synonymous with “band”.
  • Classification - The process of identifying and labelling features in an image. Pixels are grouped into categories using manual or automated methods.
  • Covariance - The extent to which two random variables vary together. A positive covariance indicates that a higher value of one variable tends to be linked to a higher value of another. Negative covariance indicates that a higher value of one variable tends to be linked to a lower value in another. This value is used when comparing two different bands of the same image to identify areas of consistent land cover or habitat.
  • Electromagnetic Spectrum - The range of wavelengths of electromagnetic radiation. Remote sensing applications typically use wavelengths that include the visible wavelengths (blue through red), the infrared, and microwave regions of the electromagnetic spectrum. The shorter wavelength ultraviolet, x-ray, and gamma rays are not typically used. *The long wavelength radio waves are also not typically used.
  • Feature recognition - The ability to identify a feature on a digital image. In remote sensing this can refer to identifying manmade features such as buildings or airplanes but it can also refer to natural features such as land cover or topographic features such as ridges and valleys.
  • Hyperspectral - Many bands (often more than 100). Some hyperspectral sensors are capable of recording images with more than 200 bands and each band represents a specific (usually very narrow) portion of the electromagnetic spectrum.
  • Infrared - The portion of the electromagnetic spectrum that lies between the visible and microwave wavelengths (0.7 nanometres – 100 micrometres).
  • Lidar - Lidar is an acronym for Light Detection and Ranging (LIDAR – although the letters are usually not capitalized). It is a remote sensing instrument that emits a laser pulse and measures the time for the pulse to return to the detector as well as the intensity of the returned signal. Interpreting the returned signal can provide digital elevation models (DEMs), and height and structure information about vegetation and other features.
  • Mosaicking - The process of combining several neighbouring images together. This can be undertaken for display or analysis purposes although can introduce errors when classifying as each individual image has been acquired under slightly different environmental conditions.
  • Multispectral - Multiple bands, with each band recording a different portion of the electromagnetic spectrum.
  • Open source software - Software that has the source code freely available and is licensed so that it can be freely distributed and modified as long as appropriate credit is provided to the developers. There are several licensing options for open source software but all of them follow these basic rules. More information about open source software is available at the Open Source Initiative web page. More information about open source geospatial software can be found at the * Open Source Geospatial (OSGeo).
  • Optical sensor - Sensor that is sensitive to visible and infrared wavelengths of light.
  • Panchromatic Band - A band available on some sensors that records information across a wide range of the electromagnetic spectrum. This band is often recorded at a higher spatial resolution and can be used to sharpen data across the other bands.
  • Passive sensor - Remote sensing instrument that measures energy that originated from the sun and was reflected by the Earth’s surface or was emitted from features on the Earth’s surface.
  • Pixel - An individual “picture element” from an image. When an image is magnified the individual pixels can be seen as a square or rectangular block in the image.
  • Radar - Radar is an acronym for Radio Detection and Ranging (RADAR – although the letters are usually not capitalized). It is a remote sensing instrument that emits a microwave signal and measures the time for the signal to return to the detector as well as the intensity of the returned signal. Interpreting the returned signal can provide digital elevation models (DEMs), changes in water level and information about land cover.
  • Radiance - Measure of radiation energy. Radiance is usually measured in watts per unit solid angle area.
  • Radiation - Energy transferred as particles or waves through space or other media. In remote sensing radiation often comes from the sun although it can also come from the sensor as is the case with LIDAR and RADAR sensors.
  • Radiometer - An instrument that measures the intensity of electromagnetic energy in different wavelengths.
  • Reflectance - Ratio of the intensity of reflected radiation to that of incident radiation on a surface. Reflectance is expressed in percent and usually refers to a specific wavelength.
  • Resolution - The smallest detail visible in an image. Usually resolution refers to spatial resolution. The spatial resolution of an image is an indication of the size of a single pixel in ground dimensions. It is usually presented as a single value that represents the length of one side of a square. For example, a spatial resolution of 30 metres means that one pixel represents an area 30 metres by 30 metres on the ground. If the pixel is rectangular, it will be recorded as a height and width dimension (i.e., 56m x 79m).
  • Sensor - A device that is capable of recording the intensity of electromagnetic radiation. In remote sensing these devices typically record this information in images, rather than from a single point.
  • Spectral reflectance curve - A curve describing the reflectance values for a particular feature over a range of wavelengths. The x-axis is for wavelength and the y-axis is for reflectance. Different features have unique spectral reflectance curves.
  • Visible spectrum - The portion of the electromagnetic spectrum between the ultraviolet and infrared wavelengths. This is the range of wavelengths (including the colours in the spectrum from blue through red) that can be detected by the human eye.
  • Wavelength - Distance between two crests of a wave. In remote sensing electromagnetic waves are typically measured in nanometres, millimetres, and centimetres.

    A.3 Satellites, sensors and data

    A.3.1 Sensor characteristics and image selection
    Selecting the right imagery for a particular task can seem very complex. However, with a little background information and practice it is possible to narrow the choice to just a few of the dozens of image types. It can also be very helpful to discuss the question with other users who have addressed similar issues. Remote sensing resources on the Internet such as e-mail list servers or contacts with a university or other organization working with satellite imagery, can provide valuable advice for selecting appropriate imagery.

    Often the most limiting factor is the money available to purchase imagery. The price for satellite imagery can range from nothing to over $50/square kilometre. The pricing schemes used by the various vendors change and they can be a little difficult to understand. It is always a good idea to look at the vendor’s web site or to contact the vendor to find out how their products are priced. There are some great archives offering free satellite imagery, but most of that imagery is either from the Landsat series of satellites or it is coarse (less the 250m resolution) resolution.

    Spatial resolution

    This refers to the size of a pixel in terms of ground dimensions. It is usually presented as a single value that represents the length of one side of a square. For example, a spatial resolution of 30 metres means that one pixel represents an area of 30 metres by 30 metres on the ground. If the pixel is rectangular it will be represented by a height and width dimension (i.e., 56m x 79m). So, how does one select an appropriate spatial resolution? Although there are guidelines for selecting an appropriate spatial resolution most people rely on experience, and trial and error. If you can’t tap into someone with sufficient experience try to select a resolution that is a factor of 10 times smaller than the size of the features you are identifying. For example, if you want to visually delineate features with a minimum size (minimum mapping unit) of 1 hectare (100m x 100m), a 30m spatial resolution is probably sufficient but if you want to identify tree crowns that are roughly 3m x 3m you would probably want to select a 1m or finer resolution. In the remainder of this section other variables associated with different satellite image products will be described to assist in deciphering information provided by vendors.

    Spectral bands (channels)

    When evaluating the spectral quality of a particular image product there are three variables that are usually considered:
    1. bandwidth,
    2. band placement, and
    3. the number of bands.

    The spectral bandwidth refers to the range of wavelengths that are detected by a particular sensor. This characteristic is particularly important when using hyperspectral imagery.

    Band placement defines the portion of the electromagnetic spectrum that is used for a particular image band. For example, one channel might detect blue wavelengths and another channel might detect thermal wavelengths. The particular properties of the features you are interested in dictate which bands are important.

    The last spectral variable is the number of bands. This is generally less important for visual interpretation, which tends to use only three bands at a time, but can become very important when using automated classification approaches. Hyperspectral images are those with many bands (usually over 100).

    Program history

    It is important to know the background of a satellite sensor (or its program history) if you want to be able to obtain imagery that was acquired several years ago. Some satellite image programs, such as Landsat, were started over 30 years ago whereas others, such as Quickbird, started in 2001.

    Image surface area

    The ground area covered by an image product defines the footprint of the image. Usually, high spatial resolution images cover less ground per image than the lower resolution images but this is not always the case. Having images that cover large areas increases your chances of covering your area of interest in the fewest number of scenes possible. Stitching together adjacent images can be problematic, especially if the adjacent images were acquired during different seasons. Having your entire study area on a single image saves a lot of work.

    Multi-angle options

    Some satellite sensors can be pointed over a particular target area to acquire images. This has a few benefits. One is that a user can request that a particular feature be targeted thereby removing the problem of having to stitch adjacent images together since your study area can be placed in the middle of the image. Another advantage of pointable sensors is that they can be used to acquire stereo imagery which can be viewed in 3-D and can be used to create Digital Elevation Models (DEMs). Other sensors that are not pointable (they always point straight down) usually use a systematic, predefined acquisition program that always acquires imagery over the same area. An example of this is the Landsat World Reference System (WRS) index that breaks up the globe into overlapping “tiles.” These tiles each have unique reference numbers known as the “path” and “row.” Knowing the path and row numbers makes it easy to search for all of the images available for your area of interest.

    Repeat interval

    The repeat interval is the minimum time a particular feature can be recorded twice. For example, with Landsat the same image area can be recorded every 16 days. Some sensors with a very wide field of view can acquire multiple images of the same area in the same day. Another advantage of pointable sensors is that they can reduce the repeat time for which a feature can be recorded because they are not limited to viewing directly under the satellite. It should also be noted that most remote sensing satellites have a near-polar orbit and are not able to acquire imagery at the poles since their orbit does not go over these areas.

    Scheduling options and price

    In many cases you can find appropriate imagery in an archive. However, if it is necessary to request new imagery for a particular area it is important to know what scheduling options exist and their associated costs. Most of the commercial providers have multiple scheduling options depending on the priority. High priority scheduling can cost several thousand dollars per image in addition to the image costs. Always check with the vendor to find out how their scheduling works and how much it costs.

    Selecting imagery over an area of interest

    Each of the image vendors and most of the image archives have some sort of browse facility that allows you to select the area you are interested in with links to browse images that show you what the image looks like before you purchase it. Some of the browse facilities use an interactive map that you can use to zoom in on and outline your area. Others let you specify the area using latitude and longitude coordinates. Many of the sites give you both map and coordinate options. Much effort has gone into improving the user interface for these sites and these days they are generally pretty straight forward to use with many providing short tutorials on how to use the browse facility.

    A.3.2 Sensors commonly used to assess biodiversity issues
    Below is a list of some of the common satellite sensors used in biodiversity conservation. The list is ordered from fine to coarse resolution with optical sensor and radar sensors at the end. The satellite name is indicated the left/sensor on the right.

    IKONOS-2 Spatial resolution: Panchromatic: 1m, Multispectral; 4m Image coverage: 11.3 km swath width Spectral bands: 1 Panchromatic: 525.8-928.5nm; 4 Multispectral: 450-520, 520-600, 630-690, 760-900nm Repeat frequency: 1 – 3 days Launch date: 1999

    QUICKBIRD Spatial resolution: Pan: 61 cm; MS: 2.44 m Image coverage: 16.5 km Spectral bands: Pan: 725, 479.5, 546.5, 654, 814.5 nm Repeat frequency: 1-3 Launch date: 2001

    SPOT 5/HRG Spatial resolution: Panchromatic: 2.5m, Multispectral: 10m, SWIR: 20m Image coverage: 60km x 60km to 80km Spectral bands: 1 Panchromatic: 480-710nm; 4 Multispectral: 500-590, 610-680, 780-890, 1580-1750nm Repeat frequency: 2-3 days Launch date: 2002

    RESOURCESAT 1 IRS/P6 (three instruments LISS-3, LISS-4, and AWiFS) Spatial resolution: LISS-3: 23.5m, LISS-4: 5.8m, AWiFS 56m. Image coverage: LISS-3: 141km swath, LISS-4: 23.9 km (MX mode) 70.3m (Pan mode), AWiFS 740km. Spectral bands: LISS-3 and AWiFS: 520-590, 620-680, 770-860, 1550-1700 LISS-4: 520-590, 620-680, 770-860 Repeat frequency: LISS-4: 5 days, LISS-3 and AWiFS: 24 days Launch date: 1996, 2003

    SPOT 4/HRVIR Spatial resolution: Panchromatic 10m, Multispectral 20m, SWIR 20 m Image coverage: 60km x 60km to 80km Spectral bands: 1 Panchromatic: 610-680nm, 4 Multispectral: 500-590, 610-680,780-890, 1580-1750nm Repeat frequency: 2-3 days Launch date: 1998

    IR-MSS/CBERS2, 2b Spatial resolution: 20m Image coverage: 113km Spectral bands: VIS: 0.45-0.52µm, 0.52-0.59µm, 0.63-0.69µm, NIR: 0.77-0.89µm, PAN: 0.51-0.71µm Repeat frequency: 26 days Launch date: 2003, 2006

    Terra/ASTER Spatial resolution:Visible and near-infrared (VNIR): 15m, Shortwave infrared (SWIR): 30m, and Thermal infrared (TIR): 90m. Image coverage: 60mk x 60km Spectral bands: 4 VNIR: 520-600, 630-690, 780-860, 780-860 nm (last band is pointed aft) 6 SWIR: 1600-1700, 2145-2185, 2185-2225, 2235-2285, 2295-2365, 2360-2430nm 5 TIR: 8125-8475, 8475-8825, 8925-9275, 10250-10950, 10850-11650 nm Repeat frequency: 16 days; acquisitions are scheduled Launch date: 2000

    Landsat/TM and ETM+ Spatial resolution: Panchromatic: 15m, Multispectral: 30m Thermal: 60 Image coverage: 185km x 170km Spectral bands: 1 Panchromatic (only on ETM+): 520-730nm 7 Multispectral: 450-520, 520-600, 630-690, 760-900, 1550-1750, 10400-12500, 2080- 2350nm Repeat frequency: 16 days Launch date: Landsat TM 4 and 5 1982 and 1984, Landsat ETM+ 1999

    Landsat/MSS Spatial resolution: Landsat 1-3: 56m x 79m, Landsat 4-5: 68m x 82m Image coverage: 185km x 185km Spectral bands: 4-5 Multispectral: 500-600, 600-700, 700-800, 800-1100, 10400-12600nm (only on Landsat 1-3) Repeat frequency: Landsat 1-3: 18 days,

    Landsat 4-5: 16 days Launch date: 1972, 1975, 1978, 1982, 1984 ENVISAT-1/MERIS Spatial resolution: Ocean: 1040m x 1200 m, Land & coast: 260m x 300m Image coverage: 1150km Spectral bands: VIS-NIR: 15 bands selectable across range: 0.4-1.05µm (bandwidth programmable between 0.0025 and 0.03µm) Repeat frequency: 3 days Launch date: 2002

    Terra/MODIS Spatial resolution: Bands 1 and 2: 250m, Bands 3-7: 500m, and Bands 8-36: 1km Image coverage: 2330 km swath width Spectral bands: Bands 1 and 2: 620-670, 841-876 Bands 3-7: 459-479, 545-565, 1230-1250, 1628-1652, 2105-2155nm Bands 8-36: 12 bands ranging from 405-965nm and 17 bands ranging from 1360-14385nm Repeat frequency: near daily Launch date: 2000

    NOAA KLM/AVHRR Spatial resolution: 1.1 km Image coverage: 3000 km wide Spectral bands: Multispectral: 580-680, 725-1000, 1580-1640, 3550-3930, 10300-11300, 11500-12500nm Repeat frequency: 1 day Launch date: 1978

    SPOT VEGETATION Spatial resolution: 1.15km at nadir Image coverage: 2200 km wide, variable length Spectral bands: VIS: 0.61-0.68µm, NIR: 0.78-0.89µm, SWIR: 1.58-1.75µm Repeat frequency: 1 day Launch date: 1986

    SeaWIFS Spatial resolution: 1.1 km Image coverage: 2,801 km Spectral bands: 8 bands at 402-422, 433-453, 480-500, 500-520, 545-565, 660-680, 745-785, 845-885 nm Repeat frequency: 1 day Launch date: 1997

    RADAR

    ENVISAT/ASAR C-band Spatial resolution: 150m x 150m Image coverage: Image and alternating polarisation modes: up to 100km, Wave mode: 5km, Wide swath and global monitoring modes: 400km or more Spectral bands: Microwave: C-band, with choice of 5 polarisation modes (VV, HH, VV/HH, HV/HH, or VH/VV) Repeat frequency: 35 days; acquisitions are scheduled Launch date: 2002

    Radarsat-1/ SAR (Synthetic Aperture Radar) C-band (HH polarization) Spatial resolution: Standard: 100km Wide: 150km, Fine: 45km, ScanSAR Narrow: 300km, ScanSAR Wide: 500km, Extended (H): 75km, Extended (L): 170km Image coverage: : Standard: 25 x28 m (4 looks), Wide beam (1/2):48-30 x 28m/ 32-25 x 28m (4 looks), Fine resolution: 11-9 x 9m (1 look), ScanSAR (N/W): 50 x 50m/ 100 x 100m (2-4/4-8 looks), Extended (H/L): 22-19x28m/ 63-28 x 28m (4 looks) Spectral bands: Microwave: C band: 5.3GHz, HH polarisation Repeat frequency: 24 days Launch date: 1995

    ALOS (Advanced Land Observing Satellite)/ Phased Array type L-band Synthetic Aperture Radar (PALSAR) Spatial resolution: Hi-res: 7-44m or 14-88m (depends on polarisation and looks), ScanSAR mode: <100m, Polarimetry 24-88m Image coverage: High resolution mode: 70km, Scan SAR mode: 250-360km, Polarimetry: 30km Spectral bands: Microwave: L-Band 1270MHz Repeat frequency: 3 days Launch date: 2006

    For further information and comparison tables:

    CEOS
    A catalog of missions and satellite instruments from 2005 Edition of the CEOS Earth Observation Handbook, prepared by the European Space Agency (ESA).

    ASPRS
    Guide to land Imaging Satellites.This PDF file is a complete overview of civil land imaging satellites with resolutions equal to or better than 36 metres in orbit or currently planned to be in orbit by 2010.

    ITC’s
    database of satellites and sensors A database with a broad range of information about most satellite remote sensing systems in use today. The database can be browsed and searched for information.

    Earth observation satellites and sensors for risk management
    A good resource for a broad range of information about different sensor systems and satellites. The site includes a table listing past and future launch dates as well.

    A.3.3 Continuity of present systems and useful new platforms
    Operational monitoring for biodiversity indicators requires data for large areas, ideally available for little or no cost. While the U.S. Government continues to make available (through NASA and USGS) low- or no-cost data that are free of redistribution restrictions, the ability of national governments to sustain Earth-focused remote sensing research and related applications may be subject to change. So far, the Landsat-TM class of data has proven to be very useful because of its spatial and spectral resolutions and its availability. Other sensors collecting data of this type include IRS, SPOT IMAGE and CBERS and their importance for biodiversity information is heightened while near-term access to Landsat data remains an issue. Corona and other declassified sensors, which collected high resolution imagery prior to the launch of Landsat, with these data now being publicly available are important sources of data for extending the environmental record farther into the past.

    The U.S. Government has committed to provide an operational Landsat continuity mission, but a launch date is not expected until mid-2011 at the earliest. Satellites or sensors that might potentially be available to fill the gap between Landsat 7 and its successor are IRS, CBERS, ASTER, and SPOT. Terra ASTER and the EO-1 ALI research sensors collect data available through well maintained archive and ordering systems at a low cost to the user. Terra MODIS collects data at coarser spatial resolutions (250 m, 500m, and 1000m) but with higher temporal availability due to possible repeat acquisitions every 1-2 days.

    High-resolution imagery will continue to be important for local applications and as a surrogate for ground sampling. Occasionally these data come into the public domain through data buys or in response to humanitarian relief efforts. Aerial photography will always be an alternative and frequently a preferred data source for high resolution data.

    In addition to these long-running programs several new programs began around the turn of the millennium and others are scheduled for launch in the near future. The ASPRS Guide to Land Imaging Satellites illustrates the timing and spatial resolution for several future missions. In the near future, 8 countries plan to operate satellites with 1 metre or better spatial resolution and roughly 20 countries will have operational optical satellites (K. Jacobsen, University of Hannover, Germany: ISPRS Hannover workshop 2005).

    A.4 Three examples of how to obtain imagery

    Below are guidelines for navigating three satellite image web archives.

    A.4.1 GLCF Map Search

    On the map search page, you will see a world map, you can choose the type of imagery you are interested in finding on the left, and click the Update Map button to see the areas that are available in red.

    Step 1: Zoom In Zoom to your area of interest by clicking on the map. Above and to the left of the map are the zoom and pan buttons. The zoom bar is found centered above the map. These can be used in the following ways:

    ZOOM BUTTON: When this button is selected, clicking on the map will zoom in one level and re-center the map This button is selected, by default, when starting a new map session.

    PAN BUTTON: Select this button if you want the map to pan to the location when clicking on the map. The map will re-center to the location clicked without changing the zoom level.

    ZOOM BAR: Use this bar to zoom in or out to any level without re-centering the map. Clicking the plus button zooms in one level, the minus button zooms out one level. To zoom in or out to a specific level, click any of the bars between the plus and minus buttons. Click on the closest bar to the minus button to quickly zoom back out to the global level.

    Step 2: Select Datasets

    Datasets are listed on the left hand side of the screen. Select the datasets that you are interested in and then click on

    Areas covered by available data are shown on the map in a light red/orange color.

    Step 3: Make a Selection

    Make a selection by using any of the following methods:
    • Clicking on the map using the selection buttons
    • Selecting all items shown on the map
    • Querying by WRS Path/Row
    • Querying by Latitude/Longitude
    • Querying by Place Name
    • Drawing a rectangle, line, or polygon on the map

      Details on each of these methods are available. Selections are shown on the map with a thick yellow line. All scenes that match the current selection are shown in a darker color. Refine searches using parameters found under the “Date/Type” tab. Additional layers can be added to the map under the “Map Layers” tab. A selection cannot match more than 600 images; if your selection has more than this, refine your search until you have less than 600 images.

      Selection Buttons

      SELECT BUTTON: When this button is selected, clicking on the map creates a selection around all data found at that point. Add more data to your selection by continuing to click on different points on the map.

      UNSELECT BUTTON: When this button is selected, clicking on the map removes all selections found at that point. Selections created by other methods (not using the select button) can also be unselected with this button.

      Overlapping selections will have dissolved borders on the map. Using the unselect button requires selecting on both selections unless you click on the area where they overlap.

      Step 4: Preview and Download

      Once you have data in your selection, the “Preview and Download” button will become active. You can then save your search in your workspace or begin downloading directly.

      A.4.2 Global Visualization Viewer
      GLOVIS
      You will see a similar map interface on the GLOVIS web site. There is a Select Sensor drop down menu where you will find the available data sets. Click the About Browse Images button to read more details about the available imagery.

      Choose a latitude/longitude location, or simply click on the map to view the available imagery. Once you are in the viewer, you can change the Sensor, Resolution and Map Layers using the menu bar above the map.

      For Landsat images, you should see nine images in an interactive mosaic in the viewer. You can click on any image to bring it to the top. To scan the different dates for this image, click the Previous Scene and Next Scene buttons on the left. You can also choose to exclude images based on the amount of cloud cover with the Max Cloud option. Right clicking on an image brings up additional options such as viewing metadata, a preview image. Choose add scene to list for ordering. Clicking Order will bring you to the appropriate USGS web site for purchasing the image. You can either have disks mailed to you, or download via FTP.

      A.4.3 Earth Explorer
      EarthExplorer Choose Guest to enter the Data Set Selection menu, or register as a user for additional options such as saving search results.
      In the Data Set Selection area you will find a list of the available data sources, as well as Related Links to additional information. Choose the data set that you would like to search. In the Spatial Coverage box you can define your area of interest on a map, using coordinates, or a place name. Click the Continue button to advance.

      You can choose the (Additional Search Criteria…) link to limit the search by Path/Row, cloud cover, entity ID, day or night, or data classification for Landsat. You can also limit the search by choosing a date range in the Acquisition Date box. Choose a large number for records returned in order to view the complete range of available data. Click Search and wait for the results.

      Once the results are returned, click the data set link to view the available images. Click the Show link to view preview images, metadata, and footprints. Not all datasets have preview images. Images can be ordered through the “Shopping Basket” feature.

      A.5 Online tutorials and software resources

      A.5.1 Tutorials

      Studying Earth’s Environment from Space

      This is an educational site, sponsored by Old Dominion University, for high school and college instructors and students. Free data, image processing software, and tutorials are provided for the following themes: stratospheric ozone, global land vegetation, oceanography, and polar sea ice processes.

      Remote Sensing Advanced Technology (RSAT) Tutorials

      This site has a few simple examples of how remote sensing imagery can be used in a variety of applications. It illustrates the practical aspects of concepts such as pixel size, spectral band combinations, and 3-D perspective views.

      NASA Remote Sensing Tutorial

      This is a fairly complete, traditional-style remote sensing tutorial available on CD and the Web. The contents could be used for a college-level introductory remote sensing course. It was developed and is currently supported by NASA. It is sometimes referred to as the “Short Tutorial” after the author, Dr. Nick Short.

      Ohio U.-view Remote Sensing On-Line Tutorial

      This is a Power Point style presentation of remote sensing. The tutorial covers a broad range of topics but does not provide significant detail on individual topics.

      The Remote Sensing Core Curriculum

      The remote sensing core curriculum is an assemblage of content from various authors using various presentation styles. Some of the volumes are reasonably complete, some only provide exercises, and some are not completed. In general this site may provide some useful material for college-level remote sensing educators, but in general the site is not designed for access by the general public.

      University of Colorado, Department of Geography – Aerial Photography and Remote Sensing

      This presents a brief overview of aerial photography and remote sensing concepts and applications.

      Canada Centre for Remote Sensing – Learning Resources

      The CCRS web site has a broad range of quality remote sensing education resources for remote sensing novices and experts. All of the material is available in English, French, and sometimes other languages. This is likely the most complete set of remote sensing tutorials available on the web.

      Downloading and Formatting Earth Images from Terraserver

      This tutorial details how one can download free aerial photography on the Internet and format it for use in remote sensing and GIS software.

      International Institute for Geo-Information Science and Earth Observation (ITC) Remote Sensing Education

      This site provides links to a dozen or so different Internet sites that have remote sensing education resources.

      Institute for Advanced Education in Geospatial Sciences (IAEGS)

      The IAEGS provides distance learning opportunities for topics in the field of Geospatial Information Technology. The courses are meant to complement conventional classroom learning with the goal being to develop a highly skilled workforce. These courses must be purchased.

      A.5.2 Software resources
      A.5.2.1 Commercial remote sensing packages
      Commercial off the shelf software is designed to visualize and process remotely sensed imagery. These packages all provide a broad range of features necessary when working with remotely sensed imagery. Pricing can be quite variable depending on the type of institution requesting the software and where that institution is located. Price information is not given here. Functionality of the different packages is also not given. Software reviews such as the one on the American Society for Photogrammetry and Remote Sensing web site: (http://www.asprs.org/resources/software/index.html) are available but these software packages constantly undergo improvements so reviews quickly become out of date.

      ERDAS IMAGINE
      ERDAS IMAGINE, a suite of software products for working with remotely sensed imagery, is the flagship product of Leica Geosystems Geospatial Imaging.

      ENVI
      ENVI is developed by Research Systems Incorporated (RSI) a subsidiary of ITT Industries. It is developed on the IDL programming language, also developed by RSI.

      PCI Geomatica
      PCI Geomatica is produced by the company PCI Geomatics in Canada. A number of add-on modules are available for advanced processing.

      IDRISI Kilimanjaro
      IDRISI Kilimanjaro is developed by Clark Labs at Clark University. It runs on low-end Windows computers which makes it appealing to many universities and remote sensing facilities.

      ER Mapper
      ER Mapper is the name of the company and the software. The company was recently acquired by Leica Geosystems (the makers of ERDAS), however, it will continue to be sold around the world.

      TNTmips
      TNTmips is produced by MicroImages. They offer a free version of the software called TNTlite that is fully functional, however, it is limited to working with small images.

      Image Analyst

      Image analyst is Intergraph’s desktop image processing and analysis package. It is compatible with their suite of other geospatial offerings.

      A.5.2.2 Data translation – Tools for translating data formats:
      GDAL
      GDAL is a translator library for raster geospatial data formats. A nice implementation of this library can be experienced by using the OpenEV software.

      WILBER (focus on terrain data)
      This software program can import and export many of the popular digital terrain data formats. It does not appear to be actively supported.

      FME (Feature Manipulation Engine)
      FME is a commercial software package that is very popular among GIS and remote sensing practitioners. The software provides capabilities for translating dozens of file formats.

      Geosage
      Geosage is an inexpensive Windows program that provides two useful functions: 1) combine image bands into a multi-band image and enhance them and 2) pan-sharpening images by combining a high resolution panchromatic image with a lower resolution multi-spectral image.

      A.5.2.3 Free data viewers
      Free software for viewing remote sensing data: Data viewers are distributed by commercial companies as a free tool to visualize imagery and in some cases vector data. The capabilities are often a subset of their commercial products. They are often distributed with data products to provide the user with the necessary tools for viewing geo-spatial data. These tools do not qualify as GIS or image processing software.

      ArcExplorer
      ArcExplorer is distributed by ESRI.

      ER Viewer
      ER Viewer is distributed by ER Mapper.

      PCI FreeView
      PCI FreeView is distributed by PCI.

      ENVI Freelook
      ENVI Freelook is distributed by RSI.

      A.5.2.4 No cost image processing software
      OpenEV
      OpenEV is an Open Source project to develop a software program that displays and analyses vector and raster geospatial data. It runs on Windows, Linux, and some other Unix platforms, however, a Macintosh port is being discussed. The software is built on various Open Source tools and libraries. The development activity is quite active and many new capabilities are in the works. This is one of the best freely available remote sensing image visualization packages available.

      NASA Image2000
      This is a Java-based image-processing package that was developed by NASA. Currently development has stopped but the program is available for download. The program provides a broad range of functions but is limiting in that it does not handle large datasets well. With additional funding this program has potential.

      ImageJ
      ImageJ is a Java-based Open Source program that has a good following. The program is being developed by an employee of the National Institutes of Health and the user community. It is a powerful image-processing package geared for the biological and medical sciences. It does not have capabilities for dealing with geospatial data. It claims to be the fastest pure Java image-processing program.

      WebWinds
      WebWinds is a Java-based data visualization program originally developed at the Jet Propulsion Laboratory and is now being developed by a private organization. Although the interface is not intuitive, it is a powerful visualization tool. It is difficult to say how long this program will be available for free. It has the ability to allow Internet-based distributed processing.

      MultiSpec
      MultiSpec is being developed at Purdue University. It is available for the Windows and Macintosh platforms. This software has been embraced by the GLOBE project for a number of their exercises. It was originally designed as a teaching tool but is now used by many remote sensing practitioners. If offers some sophisticated image classification tools.

      GRASS
      GRASS is an Open Source project originally developed by the United States Corps of Engineers in the early 1980’s. GRASS is a powerful raster-based GIS with many image-processing capabilities. It is primarily a command line program designed to run on Windows, Mac OSX, and Linux platforms. GRASS is a bit cumbersome for the first-time user. GRASS is also being integrated into the open source desktop GIS software package Quantum GIS (QGIS).

      OSSIM
      OSSIM stands for the Open Source Software Image Map project. The project leverages existing Open Source algorithms, tools, and packages to construct an integrated tool for remotely sensed image-processing and GIS analysis. The development team recently created a graphical user interface for OSSIM called ImageLinker that runs on all major operating systems. This software has a lot of potential.

      IDV
      IDV stands for Integrated Data Viewer and it is an Open Source Java-based program for visualizing and analysing geoscience data. The program appears to have a focus on weather and atmospheric research, with great 3-D capabilities. Unidata is developing this software.

      SPRING
      A state-of-the-art GIS and remote sensing image processing system with an object-oriented data model which provides for the integration of raster and vector data representations in a single environment. SPRING is a product of Brazil’s National Institute for Space Research.

      WinDisp (FAO)
      WinDisp is focused on early warning and food security issues. It has very basic functionality and limited file importing capabilities. It is being developed by the FAO.

      WinChips
      WinChips is a good general purpose Windows-based image processing tool with extensive tools for AVHRR processing. They provide a free “Standard license” but charge for the “Extended license” that provides Orthophoto creation and advanced AVHRR related capabilities. Documentation is good.

      ScanMagic Lite
      ScanMagicLite provides basic image processing capabilities. The lite version has similar functionallity to the licensed version, however, it does not allow printing and exporting of data.

      A.5.2.5 DEM and Terrain (flyby) Visualization tools

      Software for use in visualizing Digital Elevation Model (DEM) and terrain data:

      MicroDEM
      MicroDEM is a Windows program that displays and merges digital elevation models, satellite imagery, and vector data. Professor Peter Guth of the Oceanography Department of the U.S. Naval Academy is currently developing the program.

      The Virtual Terrain Project The Virtual Terrain Project is an Open Source effort with the goal of creating tools to easily construct any part of the real world in an interactive 3D digital form. The program is designed to run on Windows and Linux computers, with additional development going into a Mac port.

      3DEM This free Windows program will produce 3-D terrain scenes and flyby animations from a wide variety of freely available sources. This is easy to use and it is capable of handling several different data formats. It can also save animations as “*.avi” or “*.mpg” movies.

      DG Terrain Viewer
      This free Windows program was originally designed to view the SRTM data that NASA distributes for free. A nice GPS data overlay feature was recently added.

      A.5.3 Other Internet resources

      General remote sensing information

      International Institute for Geo-Information Science and Earth Observation (ITC) Remote Sensing Information
      This site offers links to hundreds of sites related to remote sensing. It is a great resource when looking for remote sensing information.

      Earth Observatory
      This is an excellent NASA-sponsored site that publishes easy to read examples of how satellite data are used for a broad variety of applications. There is also a great image archive illustrating several Earth Science concepts. They have an “image of the day” that often depicts some significant natural or anthropogenic event happening around the world.

      GISUser.com
      A GIS-focused site that includes a lot of remote sensing resources including software, data, and articles.

      SlashGISRS
      This is a nice user-driven web site to read about and participate in discussions about GIS and remote sensing happenings.

      A.5.4. Remote sensing support on the Web
      Online resources can be very useful when trying to understand a concept or figure out how to use a particular technique. Simply reading the posted message can help advance one’s proficiency in image processing and image interpretation. In addition to the following sites, most software vendors have online technical support options geared toward their specific software packages.

      E-mail list servers

      IMAGRS-L
      This is an active discussion list that has been around for several years.

      GIS List
      Although this is a GIS focused list there are occasional remote sensing related questions posted.

      Applied GIS and Remote Sensing List
      This list is fairly new but it has become quite popular and has a distinct international flair. It is hosted by a group at the University of Laval in Canada.

      The Society for Conversation GIS List
      This conservation focused list deals with GIS and remote sensing topics. The participants are friendly and usually quick to respond to questions. To subscribe to this list, visit the Society for Conservation GIS web site.

      Newsletters

      Geo Community
      There are three different newsletters available at this site. They all have a GIS focus but occasionally discuss remote sensing issues.

      GIS Monitor
      This weekly newsletter provides a good summary of GIS and remote sensing related happenings. It is easy to read and is a good way to keep up with industry developments.

      Newsgroups — (these titles are not linked to a URL)
      comp.infosystems.gis This newsgroup is focused on GIS but occasionally discusses remote sensing issues.

      sci.image.processing
      This is focused on general image processing although there are occasional postings geared toward the remote sensing image processing community.

      A.6 Opportunities for operational support

      A number of interesting opportunities to share remote sensing data and information currently exist, others are still evolving. Some of these are aimed at facilitating openness within the conservation and Earth science communities. Others are focused on interoperability technologies and data standards. The following section identifies a few such opportunities.

      A.6.1 Intergovernmental
      The Committee of Earth Observation Satellites (CEOS) is the major international forum for the coordination of Earth observation satellite programs and for the interaction of these programs with users of satellite data worldwide. Any country with Earth observation capabilities is eligible for membership. In addition to its internal activities, CEOS has a substantial outreach activity to developing nations which provides a unique opportunity to interface with the CEOS members. The goals of this outreach effort include:
      • assessment of space capabilities versus user requirements;
      • data access, ground structures, information services;
      • assessment of data use, analysis of lessons from the past;
      • promotion of well-designed pilot projects, including user involvement;
      • increased education and training;
      • growth of local talent;
      • provision of infrastructures suited to local operational conditions; and
      • improved use of existing user interfaces, with augmentation if necessary.

      The intergovernmental Group on Earth Observations (GEO) is leading a worldwide effort to build a Global Earth Observation System of Systems (GEOSS). GEOSS will work with and build upon existing national, regional, and international systems to provide comprehensive, coordinated Earth observations from thousands of instruments worldwide; transforming the data they collect into vital information for society.

      The Food and Agriculture Organization of the United Nations (FAO) supports a number of programs related to monitoring changes in land cover over time. A number of these are listed below:

      Land Degradation Assessment in Drylands (LADA)

      Global Forest Resources Assessment (FRA)

      Global Land Cover Network (GLCN)

      Africover land cover mapping program

      The Global Biodiversity Information Facility (GBIF) members include countries and international organizations who have signed a Memorandum of Understanding that they will share biodiversity data and contribute to the development of increasingly effective mechanisms for making those data available via the Internet. GBIF allows members to share data openly, freely and electronically, so the resource will be dynamic, interactive, and ever-evolving. Within five years, GBIF aims to be the most-used gateway to biodiversity and other biological data on the Internet.

      The Conservation Commons is a cooperative effort amongst like-minded conservation organizations and research institutions which breaks down barriers to access, more effectively connecting practitioners to data and information assets. It contributes to developing and adopting standards for integrating these assets to support the generation of knowledge and best practice. The purpose of the Conservation Commons is to ensure open access and fair use of data, information, expertise, and knowledge for the conservation of biodiversity for the benefit of the global conservation community and beyond.

      The Inter-American Biodiversity Information Network (IABIN) will provide the networking information infrastructure (such as standards and protocols) and biodiversity information content required by the countries of the Americas to improve decision-making, particularly for issues at the interface of human development and biodiversity conservation. It is developing an Internet-based platform to give access to scientifically credible biodiversity information currently scattered throughout the world in different institutions, such as government organizations, museums, botanical gardens, universities, and NGOs.

      The International Organization for Standardization (ISO) is the world’s largest developer of standards. Although ISO’s principal activity is the development of technical standards, ISO standards also have important economic and social repercussions. ISO standards make a positive difference, not just to engineers and manufacturers for whom they solve basic problems in production and distribution, but to society as a whole. ISO standards exist and continue to evolve for geospatial data and information.

      The Terrestrial Ecosystem Monitoring Sites (TEMS) provide information and access to long-term terrestrial monitoring sites. Over 2000 sites are registered in the TEMS, Terrestrial Ecosystem Monitoring Sites international directory. A substantial amount of information exists at these sites. The goals are outlined as:
      • To develop modelling, assessment and research programmes;
      • Assess the gaps in geographic coverage of key variables;
      • Link ground and satellite observations;
      • Evaluate the quality of data and measurement methods;
      • Identifying “T.Sites” that need upgrading.

      Registration of a site can be done online at: http://www.fao.org/gtos/tems/tsite_edit.jsp

      The Global Observation of Forest Cover and Land Dynamics (GOFC-GOLD) objective is to improve the quality and availability of observations of forests at regional and global scales and to produce useful, timely and validated information products from these data for a wide variety of users. GOFC-GOLD includes a regional network based in Africa, Asia and Eurasia (see http://www.fao.org/gtos/gofc-gold/networks.html for more details).

      A.6.2 Non-governmental/non-profit
      The Earth Science Information Partnership (ESIP) Federation is a collaborative between government agencies, universities, non-profit organizations, and businesses in an effort to make Earth Science information available to a broader community. The Federation has been a substantial force for technology and education innovation and for furthering openness, interoperability and the exchange of ideas. Over 3500 data sets are available through the Federation. Note that the Federation is currently composed largely of US interests although many of the participants are actively engaged in and open to international collaborations.

      The Open Geospatial Consortium (OGC) is a member-driven standards organization which continues to substantively influence the way geographic information systems (GIS) share data. OGC is an international organization and, for those concerned with influencing geospatial standards, membership is worth considering. It should be noted that currently the upper membership levels are largely composed of US government and commercial organizations. The Open Source Geospatial Foundation (OSGEO) has been created to support and build the highest-quality open source geospatial software. The foundation’s goal is to encourage the use and collaborative development of community-led projects.

      A.6.3 Other (research, technological initiatives)
      Open-source Project for a Network Data Access Protocol (OPeNDAP) is a technological framework that simplifies all aspects of scientific data networking. It enables users of their software to seamlessly share their data, regardless of original data format. The software may be downloaded from their web site for free. OPeNDAP is an evolving technology but shows great promise for sharing heterogeneous data sources.

      The Global Land Cover Facility (GLCF) is a research and applications project dedicated to the free and open distribution of remotely sensed Earth science information. It is associated with the University of Maryland Institute for Advanced Computing Studies. GLCF collaborates in terms of its research, product development and data provision activities. Some collaborators have donated data collections in an effort to further the availability of free data to the science and applications community. GLCF is able to assist CBD members with identifying and meeting remote sensing data requirements (glcf@umiacs.umd.edu).

Rate this page - 1 person has rated this page 
  • United Nations
  • United Nations Environment Programme