Landsat color composite image draped on a Digital Elevation Model

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1 SFR406 Spring 2015 Formation and Interpretation of Color Composite Images Introduction One sees color images collected by earth orbiting satellites in popular magazines, in movies and television shows; however, few viewers have any understanding of what they are looking at. Many of these images were recorded by Landsat or other multispectral scanners. These images are usually color composite images (as described in the following sections). Color composite images are often interpreted visually, sometimes to compliment data analysis in remote sensing studies. For example, one can learn how to interpret general vegetation types from a summer color composite image to determine where to go to visit or sample cover type attributes in the field. Color composite images combined with a digital elevation model (DEM) can depict some interesting perspectives of the landscape (Figure). Visual interpretation of time-series Landsat color composites has been used as a method to support accuracy assessment of forest or land cover change images (Cohen et al., 1998; Sader et al., 2003). In regions where vegetation or land cover maps do not exist or are out of date, visual interpretation of Landsat color composites has been employed as the method to develop the vegetation maps for large mapping areas (FAO, 1993). Landsat color composite image draped on a Digital Elevation Model Digital and color composite images can be interpreted visually as in aerial photo interpretation or digitally as in digital image processing performed with computer

2 programs. Color composite images can be interpreted visually using many of the same PI principles used in aerial photo interpretation. Students well- grounded in PI principles and related concepts will be able to adapt their interpretation skills to digital images, particularly the interpretation of satellite color composite images formed on a computer monitor or printed/plotted on paper. Fundamentals Concepts The art of interpreting a 3 band color composite image is aided by the following: 1) Field knowledge and distribution of cover types throughout the study area (e.g., forest types and plant ecology, topography, and terrain analysis. 2) Familiarity with the multispectral reflectance characteristics and DN levels for different forest and land cover type. 3) Familiarity with additive color theory. 4) Knowledge of PI principles and PI experience. Mechanics of a Color Composite Image If three different wavebands are displayed simultaneously on the computer monitor, a color composite image will result. The color write functions or color guns in the monitor are represented by primary colors - red, green and blue (RGB). Two colors in combination form complementary colors (R + G = yellow; R + B = magenta; G + B = cyan, all 3 primaries combined are white and lack of all three form no color (black). Each image represents 8 bits and three 8 bit images in the composite represents over 16 million possible colors. The additive primaries: red, green, and blue Characteristics: Equal proportions of the three additive primaries combine to form white light. The strength of the composite colors formed are controlled by the relative brightness or DN in each of the 3 wavebands coupled with R, G, and B. For example, a blue pixel represents high reflectance and thus a high DN coupled with the B color plane, and the wavebands coupled with R and G have very low, or no DN values. For example deep, clear water may

3 appear blue on a true color composite because there might be higher reflectance in the shortest waveband (visible blue) and most of the energy at the other wavelengths are mostly absorbed, thus little to none reflected (and hence low DN for wavelength couple with R and G color gun in the computer monitor). Clouds that are not moisture laden are white on all color composites, because they reflect highly in all visible and reflected infrared bands on any composite image combination. The figure above depicts a generic color table interpretation chart to understand the relation of waveband DN and additive color theory (primary and complementary colors that result). Once the student understands the principles of wavelength dependent reflectance of earth surface features, image interpretation principles, and additive color theory, one can develop the skill to interpret a color composite image collected by satellites or other sensors. With these skills and understanding, one should be able to view a satellite color composite image collected virtually anywhere in the world and know what the general cover types are, without ever being there. One must keep in mind that all remote sensing work needs to be supported by field observations, but where it is not possible, these interpretation skills can be invaluable for a general overview of the area. References Cohen, W.B., M. Fiorella, J. Gray, E. Helmer, and K. Anderson An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogrammetric Engineering and Remote Sensing 64(4): [FAO] Food and Agricultural Organization Forest Resources Assessment, 1990, Tropical Countries. FAO Forestry Paper 112. Rome: Food and Agricultural Organization of the United Nations. Sader, S.A., Bertrand, M., & Wilson, E.H Satellite change detection of forest harvest patterns on an industrial forest landscape, Forest Science, 49(3),

4 True Color, CIR and False Color Composite Images using Landsat 8 OLI Interpreting a True Color Composite To create a true color composite, the three visible bands available on Landsat are coupled with the primary colors in the computer monitor (R = visible red, G = visible green, and B = visible blue). Other names for this composite are normal or natural color. Landsat -8 OLI RGB 432 true color composite Vegetation types are variations of green; urban and inert surfaces are white; water is dark blue; bog/wetlands are brownish. There is a cloud in the upper left corner of the image with thin clouds and haze trending in the southeast direction from the main cloud. This composite image will have similar color to true or normal color aerial photos and the way humans see color. Healthy vegetation is green, water is dark blue to black (depending on depth, turbidity, algae content, etc). Landsat is capable of displaying a true color composite but most other medium spatial resolution, multispectral scanners on orbiting commercial satellites cannot, because they do not contain all three visible wavebands. Many interpreters prefer true color, because colors look natural to our eyes. Perhaps one disadvantage of true color is that some different earth surface features have similar low reflectance in the visible wavelengths making it difficult to interpret subtle difference among vegetation types, for example.

5 Interpreting a Color Infrared Composite To create a color infrared composite image, two visible bands and the near infrared band are combined (R = NIR, G = visible red, B = visible green). This color pattern simulates the same color patterns as seen on color infrared photos although a different color process (subtractive primary colors) is involved with aerial photos and film. This is also called a false color composite (this is from a human perspective because it is not true color the way we see it). All composites that do not form true color can be categorized as false color. On the color infrared composite, healthy vegetation is reddish to magenta. Softwood are darker red-brown and hardwood are brighter red-magenta. Water is black. Landsat 8 OLI RGB- 543 Color Infrared composite image This composite simulates the color of a color infrared aerial photo and can be interpreted using the same logic. Vegetation types are variations of magenta; urban features and bare field are cyan. This composite contains the near infrared waveband and therefore vegetation types are better distinguished compared to a true color composite False Color Composite OLI RGB- 564 and OLI RGB- 654 This false color composite image is one of the best for showing color differences between vegetation types (e.g., hardwood, softwood, wetland types). The TM wavebands and primary colors in the computer monitor are coupled as follows: R = NIR, G = Mid IR, B = visible red. Hardwoods are orange to yellow (high R (NIR), mediumhigh G (Mid IR), and low B because the visible red is a chlorophyll absorption band. In the TM 654 composite, the near infrared and mid-infrared are rearranged with the red and green color guns so that vegetation appears green.

6 Landsat OLI RGB- 564 false color composite. This composite contains the visible red and both the near infrared and mid infrared bands. Although the colors are not natural to human eyes, the 564 band combination is arguable the best for distinguishing different forest and vegetation types in the northeastern U.S. S= Softwood H= Hardwood Landsat OLI RGB- 654 color composite The false color composite contains the same 3 bands as the previous example however the (near infrared) band (5) is coupled with the green color gun. Because vegetation reflects higher in the near infrared than the other two bands, the green color dominates. Some interpreters prefer this combination because it makes the vegetation look more natural.