Contents SPOT CIR Imagery .......................................................................................................................................... 2 CIR Use in Seismic ..................................................................................................................................... 2 CIR Band Reflectance ................................................................................................................................ 3 CIR Image Classification ............................................................................................................................ 4 Statistic Analysis of Classified CIR ............................................................................................................. 5 References .................................................................................................................................................... 6 GEOS 451 – GIS Data Capture II Figure 1. Sample of SPOR colour infrared imager with two inset images. ................................................... 2 Figure 2. Light intensity returning to sensor from dead, stressed and healthy vegetation. (After ALTAVIAN, 2016) ........................................................................................................................................... 3 Figure 3. Different plants (grass, deciduous trees, coniferous trees), bare earth (cut block) and water reflect different EM wave lengths. ............................................................................................................... 4 Figure 4. Results of image classification from CIR imagery (left) to first pass classification (Right) ............. 5 i GEOS 451 – GIS Data Capture II SPOT CIR Imagery SPOT Colour Infrared (CIR) imagery is a false color photographic (digital or film) that shows the reflected electromagnetic (EM) waves in the following order: • Near Infrared - 0.760-0.890 mm - (NIR- invisible to the human eye) as red • Red - 0.625-0.695 mm - as blue • Green - 0.530-0.590 mm - as green • 1.5x1.5 meter resolution (current supplier) A sample of CIR imagery with two inset images showing resolution in Figure 1. Figure 1. Sample of SPOR colour infrared imager with two inset images. CIR Use in Seismic An environmentally responsible seismic survey takes in to considering the impact line preperation will have on forest, grass land, riparian zones and vegetation in general. High resolution CIR imagery not only allows seismic lines to be planned around environmentally sensitive areas, but also will inform line planning about ease or difficulty in line preparation through forest and wet soil. The following is a brief discussion on what CIR is and how it is classified prior to seismic line design. 2 GEOS 451 – GIS Data Capture II CIR Band Reflectance CIR colour bands are good at penetrating cloud cover for vegetation determination (ALTAVIAN, 2016), flood plain identification (Karlin, 2016), and assessing soil moisture (Minnesota Comprehensive Wetland Assessment, Monitoring and Mapping Strategy Steering Committee , 2016). Healthy plants and chlorophyll strongly absorb visible light (RGB) while reflecting NIR. NIR, Blue and Green EM detected at the satellite sensor provides an accurate means of assessing vegetation health and density as illustrated in Figure 2. Figure 2. Light intensity returning to sensor from dead, stressed and healthy vegetation. (After ALTAVIAN, 2016) Different plants types (grass, deciduous trees, and coniferous trees) as well as bare earth, water and human made structures have discreet band reflectance which can be classified within a CIR image. Figure 3 illustrates a simple reflectance spectrum which is typical in an area seen in Figure 1. 3 GEOS 451 – GIS Data Capture II Figure 3. Different plants (grass, deciduous trees, coniferous trees), bare earth (cut block) and water reflect different EM wave lengths. CIR Image Classification To classify a CIR image, “training” samples are chosen based on project needs and minimum mapping units. These training samples can include tree types, water, bare earth, grass lands, mixed vegetation, structures, cut blocks, roads, etc. The training samples can be define visually using location, size, shape, shadow, tone/color, texture, pattern, known/suspected height, and site location. Best practice is to have crews identified training areas during a ground survey. A typical initial image classification using five training samples is shown in Figure 4. 4 GEOS 451 – GIS Data Capture II Figure 4. Results of image classification from CIR imagery (left) to first pass classification (Right) Statistic Analysis of Classified CIR The results of classifying CIR imagery provides a large volume of statitical information for data-drive project analysis. With tree types classified, we can estimate areas of high and low forest density for seismic line planning. Riparian zones can be identified more precicely when considereing different vegetation and ground types. Small bodeis of open water such as peridoict or season lakes lakes, dugouts, and flooded wetland, are also identified. 5 GEOS 451 – GIS Data Capture II References ALTAVIAN. (2016, August). WHAT IS CIR IMAGERY AND WHAT IS IT USED FOR? Retrieved from https://www.altavian.com/knowledge-base/cir-imagery/. BC Forest Safety. (2016, May 20). Qualified Faller Trainer / Qualified Supervisor Trainer. Retrieved March 19, 2017, from BC Forest Safety: http://www.bcforestsafe.org/ Karlin, A. F. (2016). Using SPOT and Aerial False-Color Infrared (fCIR) Imagery to Verify Floodplain Model Results in West Central Florida. Geosciences, 1-11. Minnesota Comprehensive Wetland Assessment, Monitoring and Mapping Strategy Steering Committee . (2016, July). http://www.mngeo.state.mn.us/chouse/airphoto/. Retrieved from Aerial Photography of Minnesota: http://www.mngeo.state.mn.us/chouse/airphoto/MNWetland_Mapping.pdf United States Geological Survey. (2016, November 1). NLCD 92 Land Cover Class Definitions. Retrieved March 20, 2017, from The USGS Land Cover Institute (LCI): https://landcover.usgs.gov/classes.php#develop University of Norhtern British Columbia. (2017). Wildlife Danger Tree Assessor Certificate: Wildland Fire Safety Module. Retrieved March 19, 2017, from University of Northern British Columbia: http://www.unbc.ca/continuing-studies/courses/wildlife-danger-tree-assessor-certificatewildland-fire-safety-module 6
