Learning From Paper: Using Printed Satellite Images as a Conservation and Ecosystem Management Tool A class delivered at the 3rd IUCN World Conservation Congress 17-25 November 2004 Bangkok, Thailand

Prepared and presented by: Karen C. Seto, Ph.D. (kseto@stanford.edu) Assistant Professor Department of Geological and Environmental Sciences, and Center for Environmental Science and Policy Stanford University, and Ecosystem Management Tools Thematic Leader Commission for Ecosystem Management, IUCN Gary N. Geller, Ph.D. (gary.n.geller@jpl.nasa.gov) ASTER Conservation Liaison Jet Propulsion Laboratory California Institute of Technology   Ned Horning (horning@amnh.org) Remote Sensing/GIS Program Manager Center for Biodiversity and Conservation American Museum of Natural History

Overview Remote sensing overview Value and limitations of working with paper images Visual interpretation methods Hands-on image exploration and interpretation Discussion of applications where printed images can be used effectively Wrap-up

How can satellite images help conservation practitioners? Observe: land cover, boundaries, threats, damage, topography… Monitor: change in forest cover, range condition, land use… Classify: into vegetation and land use categories, habitats… Measure: areas, distances, height/elevation… Detect: fires, resource use violations…

Highlights of Earth Remote Sensing Before 1972 - primarily aerial and satellite photographs 1972 - First Landsat satellite launched 1978 - SPOT satellite launched 1988 - Indian Remote Sensing Satellite launched 1995 - Radarsat launched 1999 - IKONOS satellite launched and NASA launched Terra satellite Today many new satellite and airborne instruments are being developed and launched

An image is made up of individual elements called pixels that are arranged in a grid of rows and columns.

The sensor acquires several images (bands) at once, each recording a specific color or range of colors. When viewed, each individual band looks like a black and white photograph                                                              Landsat band 2 - (wavelength range = 0.52-0.60 µm = blue light)

For each band the intensity of energy for a specific range of wavelengths (colors) is measured

Spectral signatures

RGB Band Composite

Pixel color and brightness is determined by the pixel value

Certain bands or band combinations are better than others for identifying specific land cover features. Landsat TM Red= band 3, Green = band 2, Blue = band 1 Landsat TM Red= band 4, Green = band 5, Blue = band 4

Landsat ETM+ band 1 (0.45-0.52 µm, blue-green) Penetrates water better than the other bands so it is often the band of choice for aquatic ecosystems Used to monitor sediment in water, mapping coral reefs, and water depth The “noisiest”  of the Landsat bands since short wavelength blue light is scattered more than the other bands Rarely used for "pretty picture" type images

Landsat ETM+ band 2 (0.52-0.60 µm, green) Similar qualities to band 1 but not as noisy. Matches the wavelength for the color green.

Landsat ETM+ band 3 (0.63-0.69 µm, red) Since vegetation absorbs nearly all red light (it is sometimes called the chlorophyll absorption band) this band can be useful for distinguishing between vegetation and soil and in monitoring vegetation health

Landsat ETM+ band 4 (0.76-0.90 µm, near infrared) Since water absorbs nearly all light at this wavelength water bodies appear very dark. This contrasts with bright reflectance for soil and vegetation so it is a good band for defining the water/land interface Sensitive to vegetation cover Less affected by atmospheric contamination

Landsat ETM+ band 5 (1.55-1.75 µm, mid-infrared) Very sensitive to moisture and is therefore used to monitor vegetation water stress and soil moisture. Useful to differentiate between clouds and snow

Landsat ETM+ band 6 (10.40-12.50 µm, thermal infrared) Measures surface temperature. Geological applications Differentiate clouds from bright soils since clouds tend to be very cold The resolution is twice as course as the other bands (60 m instead of 30 m)

Landsat ETM+ band 7 (2.08-2.35 µm mid-infrared) Can detect high surface temperatures Also used for vegetation moisture although generally band 5 is generally preferred for that application Commonly used in geology

Landsat ETM+ bands 4,3,2 – Peak chlorophyll, land/water boundary, urban areas Landsat ETM+ bands 3,2,1 – Penetrates shallow water and shows submerged shelf, water turbidity

Landsat ETM+ bands 4,5,3 – Land/water boundary, Vegetation type and condition, soil moisture Landsat ETM+ bands 7,4,2 – Moisture content in vegetation and soils, geological mapping, vegetation mapping

MODIS (500m) – Composited using imagery acquired from June – September 2001

Landsat ETM+ (30m) - 2 April 2002

ASTER (15m) - 8 November 2003

CORONA (5m) – 4 March 1967

IKONOS (1m) – 29 April 2002

IKONOS zoomed

Advantages of using paper imagery No need for a computer or fancy equipment Inexpensive to create Very portable and easy to carry in the field Easy to show other people and often a more effective communication tool Looks nice on the wall

Limitations of using paper imagery Ancillary data obscures image data Not possible to zoom into the image Not possible to change the image enhancement Can not easily overlay other data layers Generally more difficult to locate oneself on the image since GPS tracking is not possible

Visual Interpretation Skills How to read a satellite image

What do you need to interpret remotely sensed imagery? Familiarity with the specific area or similar areas Basic interpretation skills Image prints that are of sufficient quality Projection grid marks on the image are helpful to locate oneself on the image using a GPS Equipment to protect the imagery if working in the field Tools to transcribe information onto the image

Basic Elements of Visual Interpretation Tone (color) Size and shape Texture and pattern Relative and absolute location Shadows

Tone and Color Variations in tone and color results in all of the other visual elements When looking at a image photo we associate specific tones to particular features Tones change when we enhance an image or when we change the band combination of a color image

Size and Shape Rectangular features often indicate human influence such as agriculture Size and shape information greatly influenced by image resolution Knowing the scale of the image helps to convert feature dimensions on the image to actual dimensions

Texture and Pattern Varies with image resolution Often noted by roughness or smoothness Influenced by shadows

Relative and Absolute Location The location of a feature narrows the list of possible cover types Relative location particularly useful to determine land use

Shadows Often considered a contaminant but can be very useful to identify features on an image Helpful to accentuate relief Shadow effects change throughout the day and throughout the year Shadows can give an indication to the size of a particular feature

Exercises Purpose: Give you a change to do some of what we just talked about After the break we will: Explore different satellite images Identify features on printed satellite images Relate features in the image with features in a photograph Draw lines around features we can see in the satellite images

Supplies that will be used in class Imagery (Landsat ETM+ ASTER, Corona, IKONOS, ground photo) Topographic map Transparencies Masking tape Black marker Cotton Alcohol

Exercises

Discussion Questions, additions, and clarifications…

Wrap Up What have we learned? After this class Where satellite images come from and what they are made of How to interpret images Methods for using images as an aid to conservation management After this class Find some images for your area (use handout) Play with them using some of the techniques we discussed