Classifying Wildfires in Southwestern United States, Part 1. Background Information for Instructors

Transcription

1 ! Classifying Wildfires in Southwestern United States, Part 1 Background Information for Instructors This exercise will help students better understand remote sensing concepts, processing protocols, and applications. It is rooted in research related to forest management. Remote sensing and GIS have revolutionized how USDA Burned Area Emergency Response (BAER) teams make recommendations for post-fire treatment (Parsons & Orlemann, 2002). SPOT images have been used to monitor post-burn recovery of large areas of chaparral vegetation in southern California (Henry & Hope, 1998), while Landsat data have been used extensively to assess burn severity and recovery in the North American boreal forests (French, et. al., 2008; Allen & Sorbel, 2008; Hall, et. al. 2008). The Chediski-Rodeo wildfire, whose burn area was captured in the May 2003 Landsat image downloaded in this exercise, caused the most extreme damage that has ever been documented with the GOES satellite (GOES, 2002). The exercise will also facilitate the development of technical skills by requiring students to download, extract, and evaluate two Landsat images. A series of questions will require students do some research concerning the significance of filenames, README files, and sensor characteristics. SUPERVISED CLASSIFICATION OF TWO LANDSAT IMAGES Supervised classification is performed when the image analyst selects specific pixels (or cluster of pixels) within the image as training samples for a feature type. When a set of training samples has been selected and identified (assigned to a feature class; for example, dark blue pixels are assigned to the Water class) these samples are saved to a signature file. The signature file is then run against the image, and all pixels within the image are assigned to a feature class based on the nearness of their spectral values. Supervised classification is an iterative process. The signature file may be used on other images as well. The significance of assigning pixels to a feature class is demonstrated when the image is brought into the GIS. The image analyst may retain the GRID/IMG file format or may convert the classified image to vector format. Regardless, the classified file may now be brought into the GIS and used as input for spatial models, as a stand-alone dataset, and/or provide contextual information for various mapping applications. The answers to the four student worksheets (03-01, 03-02, 03-03, and 03-04) are included in this guideline. Reference materials are noted as appropriate at the end of each answer. Part I: Selecting Data With The Viewer (Worksheet 03-01): Student will use the USGS Global Visualization Viewer (GloVis), to explore, select, and download two Landsat images. As part of the introduction to the GloVis environment, students are expected to explore the available collections and the interface itself. As they do so, they will answer a series of questions that require them to explore available datasets and their formats (Worksheets 03-01). In addition to the GloVis site, students will use the Landsat Handbook and the Terra/MODIS site in order to answer questions related to sensor characteristics. The students will download and extract two images from the GloVis website: LE EDC00 (Path 36, Row 36, May 2002) LE EDC01 (Path 36, Row 36, May 2003) Developed by the Integrated Geospatial Education and Technology Training (igett) project, with funding from the National Science Foundation (DUE ) to the National Council for Geographic Education. Opinions expressed are those of the author and are not endorsed by NSF. Available for educational use only. See for additional remote sensing exercises and other instructional materials. Created 2009; last modified January 2012.

2 Worksheet answers: Answer the following questions concerning the collections & sensors. 1. What collections are available at the GloVis web site? Landsat Archive Aerial ASTER EO-1 Global Land Survey Landsat MRLC Landsat Legacy MODIS Aqua MODIS Terra TerraLook Select the MODIS Terra/MOD15A2 FPAR collection/series from the Collection drop down menu and answer questions 2 & 3 below: 2. How does the smaller map display (in the upper left corner of the viewer) change? The images are displayed based on a sinusoidal projection; the coordinate system MODIS data are collected. 3. How do the MODIS Terra scenes compare to the Landsat scenes? (Hint: turn on map layers to view contextual information.) The MODIS Terra scenes display a much larger area. This makes sense - as spatial resolution of MODIS Terra data range from 250 m to 1000 m, while Landsat images range from 15 m to 60 m. Characteristic Temporal Resolution Spatial Resolution Orbit Spectral Resolution Orbit & Acquisition Characteristics: Sensor: MODIS Terra Landsat 7 See: Days 16 Days 250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36) 705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular See: sfc.nasa.gov/ 30 m (bands 1-5 & 7) 60 m (band 6) 15 m (band 8) 705 km, 10:00 a.m. to 10:15 a.m. descending node, sun-synchronous, near-polar, circular Ba Wavelengths Ba Wavelengths Ba Wavelengths nd nd nd nm nm nm nm µm nm nm µm nm nm µm nm nm µm µm nm µm µm nm µm µm nm µm µm nm µm nm µm nm µm nm µm nm µm nm µm nm µm nm µm nm µm nm µm 2

3 4. What are some possible uses for Landsat images? Landsat is intended for use related to monitoring Earth s land resources and near coastal zones. For example, monitoring temperate forests, volcanic deposits, and identifying coral reef boundaries. (Landsat Handbook) 5. How do these uses compare to the ways in which MODIS images are used? MODIS is useful for tracking changes in the biosphere over very large areas. MODIS is a TERRA instrument and is capable of measuring terrestrial and oceanic photosynthetic activity, contributing to research related to global warming and carbon dioxide concentrations. MODIS builds upon and extends the Landsat program. (MODIS Site) Part II. Extracting The Files (Worksheet 03-02): In an effort to help students understand the significance and use of a README file. They are asked a series of questions related to the Landsat program (Worksheet 03-02). The answers to these questions are found in the README.gtf file that accompanies each Landsat image downloaded. Worksheet 03-02answers: Answer the following questions concerning the image files downloaded using GloVis. 1. What are the differences in sensors among the Landsat 1, 2, & 3; Landsat 4; and Landsat 7 missions? The Landsat 1, 2, and 3 satellites carried the Multispectral Scanner (MSS) sensor; the Landsat 4 and 5 satellites carry both the MSS and the Thematic Mapper (TM) sensors; and the Landsat 7 satellite carries the Enhanced Thematic Mapper Plus (ETM+) sensor. (Landsat downloaded README.gtf) 2. What is the significance of the TIFF tags that accompany these downloaded image files? The tags describe cartographic and geodetic information associated with the Landsat TIFF images. (Landsat downloaded README.gtf) 3. What does the.gtf extension stand for? GTF = GeoTIFF (Landsat downloaded README.gtf) 4. What is the organization of the downloaded TIFF images? Each band of Landsat data in the GeoTIFF format is delivered as a grayscale, uncompressed, 8- bit string of unsigned integers. A metadata (MTL) file is included with Landsat 7 ETM+ orders. A processing history (WO) file is included with Landsat 4-5 TM orders. Landsat 7 ETM+ SLC-off gap-filled products will include an additional directory (GAP_MASK) that contains a set of flat binary scan gap mask files (one per band). (Landsat downloaded README.gtf) 5. What is the meaning of the filename for each of the TIF files (bands) that make up the final image? (L7fppprrr_rrrYYYYMMDD_AAA.TIF è L _ _B10.TIF) L7... Landsat-7 mission B10... band 1 f... ETM+ data format (1 or 2) B20... band 2 ppp... starting path of the product B30... band 3 rrr_rrr... starting and ending rows of the B40... band 4 product B50... band 5 YYYYMMDD.. acquisition date of the image B61... band 6L (low gain) AAA... file type: B62... band 6H (high gain) B70... band 7 (Landsat downloaded README.gtf) B80... band 8 MTL.. Level-1 metadata TIF... GeoTIFF file extension 3

4 Part III. Viewing Landsat Images In The Image Processing Software (Worksheet 03-03) During June-July 2002 two wildfires, the Chediski and the Rodeo, merged to create the largest wildfire in Arizona history, burning over 480,000 acres. The two images downloaded in Part I contain the area where these wildfires occurred. The May 2002 image was captured before the Chediski-Rodeo event, whereas the burn scar is evident in the May 2003 image. Student are asked to load the two images as true color: Red - Band 3 Green - Band 2 Blue - Band 1 In Worksheet they will both visually and spectrally compare the two images viewed as true color. Although the burn scar is difficult to distinguish as true color the spectral values are noticeably higher in this region. Worksheet answers: Answer the following questions concerning the true color images 1. Visually compare/inspect the two True Color images; do you notice any great differences between the two images? Prepare and display a screen grab of each image where these (if any) differences appear. The burn scar of the Chediski-Rodeo fire is apparent southeast of center in the 2003 image Image 2003 Image with Burn Scar identified 2. Spectrally (pixel values) inspect the images (remember, you can link the viewers and see the pixel values and exact locations). If you found regions within the images that visually differ very greatly, how do their spectral values differ (e.g., which image has brighter values in which band)? May 2002: May 2003: Blue: Blue: Green: Green: Red: Red:

5 The May 2003 image has higher values in the area where the ChediskiRodeo fire occurred. Part IV. Comparing False Color Images (Worksheet 03-04): Once the students have viewed the images as true color they will reload the images, this time as false color: Red Band 04 Green Band 03 Blue Band 02 Viewed as false color, the burn scar is easily identified, as are those areas associated with human activities. For example, the round crop circles to the north and the rectangular green areas (whether agricultural or maintained urban areas) are associated with the towns of Zeniff, Snowflake, Taylor, Show Low, and Shumway. In Worksheet students will identify some of these areas, reinforcing the idea that along with spectral values in specific wavelengths, shape and texture are important considerations when attempting to identify features. They will use the USGS Map Locator tool as reference data for identifying the areas. This is an extremely useful website, as there are several datasets available: the map, the satellite image, and the topographic map. Again, this tool reinforces the need for reference and/or ancillary data in order to improve the feature classification process. Student are also asked to compare the spectral values in these human environments with the spectral values associated with an area of natural vegetation. (The values of the maintained area are much higher than those of the natural vegetation). Finally, the students will be asked about the spectral values of the burn scar itself. Based on their understanding of a graph displaying spectral patterns of land cover types (vegetation, water, and bare soil), they will decide which bandwidth should be displayed in order to more effectively identify and, therefore, classify the Chediski-Rodeo burn scar. Worksheet answers: Answer the following questions concerning the true & false color images. 1. Why are Landsat images skewed [inside a black square]? The start of each swath of 16 scan lines is displaced slightly westwards since the Earth rotates beneath the orbiting scanner, and rotational velocity varies with latitude. (Computer Processing of Remotely Sensed Images: An Introduction, 3rd Edition by Paul M. Mather, June 2004). 5

6 2. Considering the graph displayed in Figure 1 (left). Relate how displaying the images as false color may assist with the identification of vegetation in the images. Vegetation has high reflectance in the near infrared region from about.7 to ~1.3 of the electromagnetic spectrum (EMS). This region of the EMS is invisible to the human eye, so assigning band 4 (NIR) of a Landsat image to red will facilitate identification of vegetation. (Remote Sensing and Image Interpretation, 6th Ed, Lillisand, Kiefer, Chipman, 2008) Figure 1 B D C A E 3. Use the USGS Map Locator to identify the features labeled A, B, & C in the 2003 image to the left (list them). A: Agricultural Circles B: The town of Zeniff C: The towns of Snowflake, Taylor, Show Low, & Shumway Note: See images at the bottom of this page and on the next page Area A: Crop Circles 6

7 Worksheet (continued): Area B: The town of Zeniff Area C: The towns of Snowflake, Taylor, Show Low and Shumway 7

8 Worksheet (continued): 4. What are the spectral (red) values for the areas A, B, & C; compare these area values to the region in the southwest corner of the image labeled D. Area A Crop Circles Area B Zeniff Area C Urban Areas Area D Natural Vegetation Blue (Band 2) Green (Band 3) Red (Band 4) Note: Values should approximate those listed in the table. 5. How do the values in area D compare with the values in areas A, B, & C? Why is this? They are generally lower. Areas A, B, and C are well-maintained, healthier vegetated areas (wellwatered, etc.) and will have much higher reflectance values, especially in the NIR (band 4 Red) 6. The image above is from 2003; why doesn t the large green area labeled E appear in the 2002 image? The area in 2003 is the burn scar of the Chediski-Rodeo fire, which occurred in the summer of 2002, after the 2002 image was obtained. 7. Based on your answer to question 6, is the current color-band assignment the best combination for confirming/identifying area E? If not, create a false color using the color-band combinations you believe are best; submit a.jpg of your false color image. The best combinations would be Band 2, 4, & 5. Dry bare soil has a much higher reflectance in band 5 than vegetation; additionally the basalt flow is distinguishable with this band combination. False Color of 2003 band assignments: Band 2 = Red, Band 4 = Green, Band 5 = Blue Basalt Flow Note: Questions 3 through 7 will most likely require the student to do a bit of research concerning landuse, land-cover changes in this region. Wildfires are also discussed in the lecture. 8