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Spring Semester 2008Lab Exercise #10: 11/13/2008Due 11/20/2008Vegetation/Biosphere Applications of Remote SensingPart I: Normalized Difference Vegetation Index (NDVI)Part III: Unsupervised ClassificationName: _______________________ Remote Sensing of the Environment GEOG/GEOL 4093/5093 Spring Semester 2008 Lab Exercise #10: 11/13/2008 Due 11/20/2008 Vegetation/Biosphere Applications of Remote Sensing Part I: Normalized Difference Vegetation Index (NDVI) The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected, respectively. Researchers can measure the intensity of light coming off the Earth in visible and near-infrared wavelengths and quantify the photosynthetic capacity of the vegetation in a given pixel of land surface. In general, if there is much more reflected radiation in near-infrared wavelengths than in visible wavelengths, then the vegetation in that pixel is likely to be dense and may contain some type of forest. If there is very little difference in the intensity of visible and near-infrared wavelengths reflected, then the vegetation is probably sparse and may consist of grassland, tundra, or desert. Nearly all satellite Vegetation Indices employ this difference formula to quantify the density of plant growth on the Earth — near-infrared radiation minus visible radiation divided by near-infrared radiation plus visible radiation. The result of this formula is called the Normalized Difference Vegetation Index (NDVI). Written mathematically, the formula is: NDVI = (NIR — RED)/(NIR + RED) Calculations of NDVI for a given pixel always result in a number that ranges from minus one (-1) to plus one (+1); however, no green leaves gives a value close to zero. A zero means no vegetation and close to +1 (0.8 - 0.9) indicates the highest possible density of green leaves. Launch Internet Explorer and type \\nyx\rs4093 into address bar and hit enter, that takes you to the remote sensing class folder “rs4093” in CIRES server. Copy the folder “Lab_10” to “C:” drive. Begin by opening the file “C:\lab_10\k_ordway_preserve_TM” in ENVI. This is a Landsat TM scene of the Katherine Ordway Nature Preserve in Gainesville, FL. Display a color composite of the image and compare it to the topographic map (nw_ordway_topo.jpg). The set of lakes in the north-central TM image correspond to the lakes in the east-central topographic map image. 1. Do the lake levels appear higher in the image or on the topographic map (4)? 1Perform an NDVI (under Transforms menu in the ENVI toolbar) on the TM image. Output the NDVI to Memory. Choose Gray Scale. Open in a new window and link to composite. Use the topographic map for locations and comparisons. 2. What areas have the highest NDVI? Is this different than expected (4)? 3. What areas are lowest in NDVI? Is this expected? (4) Part II: Ratio Vegetation Index (RVI) The RVI is a simpler vegetation index, and is over 30 years old. The RVI is defined as: RVI = NIR/RED and simply divides the near infrared reflectance values by the visible red reflectance values. Here we will compare the two indices. Under Basic Tools, choose Band Math. Enter the following Expression: float(b4)/float(b3) This instructs the computer to divide the NIR (b4) channel by the Red channel (b3). The float( ) operator is necessary here since we are looking for values that are fractions and our original data are all integers. Upon clicking OK, you are asked to define the variables b4 and b3. Choose Band 4 and Band 3 of the TM image for the variables, respectively. Output the calculation to Memory. 4. Open RVI in Display #1 (still linked to NDVI display). How do these indices compare (4)? 25. Make a 2D-Scatterplot of NDVI vs. RVI. In the display window, choose Tools > 2D Scatter Plot, and set your variables (X, Y). What type of relationship do you see? Is this expected (Hint: check notes for formulation to describe NDVI) (4)? Part III: Unsupervised Classification Unsupervised techniques, unlike supervised techniques, do not require any a priori knowledge of the area. Instead, the system analyzes the spectra of every pixel and groups them into clusters. These clusters serve as classes and the user is left to determine what land cover classes correspond to each cluster. Perform an ISODATA classification on the TM image. Under Classifications > Unsupervised, choose Isodata. For Max Iterations, enter 10. Output to memory. Be patient. This may take several minutes to calculate. The Isodata classification determines general spectral clusters and lumps them into classes. It then calculates the mean of each class and iteratively clusters the remaining pixels into one of those classes based on minimum distance techniques. Then, during each iteration, means are recalculated and all pixels are reclassified with respect to the new means. This routine will continue to refine the classification iteratively until it reaches the maximum number of iterations that you specify or until the present iteration produces an answer that is very similar to the previous iteration. 6. Does the routine process all 10 iterations? If not, where does it stop (3)? 7. This scene is much smaller than the image of the Quelccaya Ice Cap that you worked with in Lab 9. If you performed an ISODATA classification on that image, it stops after 4 iterations for that scene. Why do you think that this smaller scene requires more iterations? (Hint: Think about the ground cover). (3) 348. Make a 2D-Scatterplot of NDVI vs. ISODATA. Why does the scatterplot look like this? What is the flaw in comparing these two algorithms


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CU-Boulder GEOG 5093 - Lab Exercise #10

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