Remote Sensing Homework 6 questions only

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Lab Exercise #4: Spectral Signature Concepts Purpose: To provide exposure to: (1) acquisition of spectral radiometric data, (2) calibration to radiance values, (3) spectral signature interpretation, and (4) visualization of results in graphic and tabular formats. This will enable you to gain an awareness of several fundamental aspects of reflected radiation. Materials: • Data values previously collected with an Exotech 4 band radiometer with filters for Landsat Thematic Mapper bands 1 (blue), 2 (green), 3 (red) and 4 (NIR). • Spreadsheet program to perform calculations and construct graphs, or: • A basic calculator, data summary tables and graphing paper Email your completed word document and excel spreadsheet to: kwarkentin@sdsu.edu with your names and lab 4 in the subject line. A user may be able to choose the proper bands and filters for a given remote sensing task by collecting and analyzing reflectance data for various targets, which have been or are to be imaged in a given scene, and then by identifying spectral bands which maximize class separability. Theoretically, the greater contrast in reflectance between two imaged objects, the easier it should be to distinguish between them. The easier objects are to distinguish, the greater is the potential for fast, accurate interpretation. Table 1. Landsat Thematic Mapper & Exotech Radiometer Bands Band Spectral Bands (m) Exotech bands in bold 1 0.45 - 0.52 blue 2 0.52 -0.60 green 3 0.63 -0 .69 red 4 0.76 -0 .90 near IR 5 1.55 - 1.75 mid IR 7 2.08 - 2.35 mid IR 6 10.4 - 12.5 thermal A four channel Exotech radiometer was used several years ago to collect raw spectral radiometric measurements for general surface material types around campus. The radiometer measures the radiation reflected from the surface as a voltage. You will be provided with radiant exitance values using the calibration data in Table 2. The voltages recorded on the data sheets for each band of each target were multiplied by the calibration for the band, field-of-view (FOV) and sensor gains used in the conversion program. Radiant exitance (Wm-2) = Voltage * Calibration factor * 10exponent Calculate radiant exitance and enter below for each target. A 15 degree FOV was used for all measurements. Average the beginning and ending panel radiant exitance. You may choose to do all table-based calculations in the Excel spreadsheet which is provided as part of this exercise, following the general workflow described here. Table 2: Radiant Exitance (Wm-2) Target average panel asphalt concrete grass shrub soil shaded grass shaded concrete Band 1 140 22 93 6 1 Band 2 152 35 86 11 3 Band 3 95 12 43 5 1 Band 4 154 45 35 103 72 3 2 49 7 5 21 65 2 13 39 26 8 The halon coated panel used to measure the incident radiation at the surface is not a perfect, i.e., neither 100% nor an isotropic (Lambertian) reflector. Therefore, an adjustment must be applied to the panel measurements. The average panel radiant exitance that you calculated above must be divided by a factor taken from Table 3 below for the time that most closely matches when your observations were taken (around 2:30 PM). The magnitude of the adjustment is dependent on the solar zenith angle which is a function of the time of day, day of the year and latitude. For your calculations, use the 2:30 PM adjustment factors. Table 3 - Halon Panel Adjustment Factors Band 1 Band 2 Band 3 .935458 .939679 .934222 .932881 .937191 .931803 Band 4 .919215 .916819 2:30 2:45 3:00 3:15 3:30 .92994 .926623 .922896 .918706 .913986 .934368 .931202 .927663 .923701 .919248 .929077 .926039 .922662 .918895 .91467 .914118 .91111 .907772 .90406 .899914 3:45 4:00 .908651 .902605 .914222 .90853 .909903 .904497 .895261 .890013 Time (PDT) 2:00 2:15 2 Enter the adjusted panel values below (you can use the excel spreadsheet for the calculations) : Avg. Adjusted Panel Values = Average panel radiant exitance  panel adjustment (Divide the average panel radiant exitance by the correct panel adjustment factors) Band 1 Band 2 Band 3 Band 4 Average Adjusted Panel Values Now, calculate the spectral reflectance factor for each band of the seven targets (numbers and calculations can be stored and submitted in the spreadsheet). The spectral reflectance factor is the target radiant exitance divided by the adjusted average panel radiant exitance. Express the reflectance as a decimal fraction out to four significant digits beyond the decimal point. Reflectance = Target exitance (Wm-2)  Adj. avg. panel exitance (Wm-2) Reflectance Material Band 1 Band 2 Asphalt Concrete Grass Shrub Soil Shaded Grass Shaded Concrete 3 Band 3 Band 4 ANALYSIS 1. Plot reflectance for each target as a point with the bands on the X axis. Connect the points for each band. Create one graph for each target, and a final graph depicting all of the targets on one graph (you should create 8 graphs). You may find the graph more interpretable if you don’t use point markers, such that each target is depicted as a line. For the final graph, construct a colored bar graph, with bands 1-4 on the X-axis, and values for each target on the Y-axis, color-labelled by material type. Use colors that are reasonable for each target. Include axis titles on each graph. Save the graphs in the excel spreadsheet. You do not need to copy them to the word doc. 2. Using the matrix below, determine the bands of electromagnetic spectrum which allow the optimum discrimination of any one target from another (fill in the best 2 bands). (Use sunlit targets, i.e., not shaded, only for this analysis.) Only fill in the white boxes. Optimum band selection for discriminating between target pairwise combinations. Asphalt Concrete Grass Shrub Soil Asphalt Concrete Grass Shrub Soil 3. Select the bands which are optimum for discriminating among the three general classes of vegetation, asphalt, and concrete/soil (fill in the best band or combination of bands in the blanks in the matrix). Only fill in the white boxes. Discuss the rationale for your decisions. Optimum band(s) selection for discriminating between three general classes of targets Vegetation Asphalt Vegetation Asphalt Concrete/Soil 4 Concrete/Soil Explanation: 4. Discuss the reflectance calculated for the two shaded targets. How do the shaded reflectance compare to one another and to the sunlit targets of the same material types? 5. Why is it important to investigate the nature of wavelength reflectance from targets when planning remote sensing missions? 6. Describe the utility of "multi-spectral" approaches for separating land cover types, i.e., potential advantages of using more than two spectral bands? 5 Table 1. Landsat Thematic Mapper Bands Spectral Bands (mm) Band 1 0.45 - 0.52 2 0.52 -0.60 3 0.63 -0 .69 4 0.76 -0 .90 5 1.55 - 1.75 7 2.08 - 2.35 6 10.4 - 12.5 Exotech bands in bold blue green red near IR mid IR mid IR thermal Average Adjusted Panel Values: 1 2 3 4 Table 2: Radiant Exitance (Wm-2) Target average panel asphalt concrete grass shrub soil shaded grass shaded concrete Band 1 140 22 93 6 1 3 2 49 Table 3 - Halon Panel Adjustment Factors Band 2 152 35 86 11 3 7 5 21 Band 1 Band 2 Band 3 150.547347 162.6765 102.2517 Band 3 95 12 43 5 1 65 2 13 Band 4 168.468 Band 4 154 45 35 103 72 39 26 8 Time (PDT) Band 1 2:00 2:15 2:30 2:45 3:00 3:15 3:30 3:45 4:00 0.93546 0.93288 0.92994 0.92662 0.9229 0.91871 0.91399 0.90865 0.90261 Material Band 1 0.146133 0.617746 0.039855 0.006642 0.019927 0.013285 0.325479 Asphalt Concrete Grass Shrub Soil Shaded Grass Shaded Concrete Example Data: l Adjustment Factors Band 2 Band 3 Band 4 Bands: Values 1: Values 2: 0.93968 0.93422 0.91922 0.93719 0.9318 0.91682 0.93437 0.92908 0.91412 0.9312 0.92604 0.91111 0.92766 0.92266 0.90777 0.9237 0.9189 0.90406 0.91925 0.91467 0.89991 0.91422 0.9099 0.89526 0.90853 0.9045 0.89001 Band 4 0.21515 0.52866 0.06762 0.01844 0.04303 0.03074 0.12909 0.11736 0.42053 0.0489 0.00978 0.63569 0.01956 0.12714 0.26711 0.20775 0.61139 0.42738 0.2315 0.15433 0.04749 0.7 Reflectence Values 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 3 4 Bands Concrete 0.7 0.6 Reflectence Values Band 3 2 2 2 Grass 0.5 0.4 0.3 0.2 0.1 0 1 2 Bands Soil 0.3 Reflectence Values Band 2 1 0.5 1 0.25 0.2 0.15 0.1 0.05 0.05 0 1 2 3 Bands 4 3 4 0.5 4 2.5 5 Asphalt 0.35 0.3 0.3 Reflectence Values Reflectence Values 0.25 0.2 0.15 0.1 0.25 0.2 0.15 0.1 0.05 0.05 0 0 1 4 2 3 4 Bands Shrub 0.7 0.45 0.6 0.35 0.3 0.5 0.25 0.2 0.15 0.1 0.05 0 1 2 3 Bands 4 Shaded Grasst 4 Reflectence Values Reflectence Values 0.4 0.4 0.3 0.2 0.1 0.18 0.16 0.14 Axie 0.12 0.1 0.08 0.06 0.04 0.02 0 0.02 4 0 1 2 3 Bands 4 Shaded Concrete 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 1 2 3 4 Bands Final Chart Band 1 Band 2 Band 3 Band 4 Bands Asphalt Concrete Grass Shrub Soil Shaded Grass Shaded Concrete Band 4 haded Concrete EMR – Target/Surface Interactions Partitioning of Energy at Surface ◼ Radiant flux at the surface is partitioned among: ◼ Absorption ◼ Transmission ◼ Reflection Radiation Budget Equation (cont.) ◼ Dimensionless ratios: Absorptance: al = Fabsorbed / Fil Transmittance: tl = Ftransmitted / Fil Reflectance: ◼ rl = Freflected / Fil al + tl + rl = Fil = 1 Radiation Budget Equation (cont.) ◼ Proportion of energy absorbed/transmitted/reflected will vary from target-to-target ◼ ◼ ◼ ◼ Material type Material condition For a given target, proportion absorbed, transmitted, and reflected energy will vary with wavelength Ability to distinguish between targets or measure phenomena Hemispherical al , tl , rl ◼ Radiometric quantities based on the amount of radiant energy incident to a surface from ANY angle in a hemisphere; direct and diffuse sources Percent Reflectance ◼ Percent reflectance = rrl = (Freflected / Fil ) * 100 ◼ Widely used in RS research to describe surface/target reflectance characteristics Properties Affecting Target Reflectivity ◼ Target/surface ◼ ◼ ◼ ◼ EMR ◼ ◼ ◼ ◼ Moisture/electrical - nature of penetration & absorption Micro-roughness - concrete, leaves, soil texture Macro-roughness - terrain → shadowing, re-direction of light Wavelength - for same target, amount of reflectance varies with l Polarization – orientation of incident light may affect amount of reflectance (microwave) Angle of Incidence – angle of incoming light Interaction of properties determines target radiance detected by the sensor Types of Reflection ◼ Two types of reflection ◼ ◼ ◼ Specular Diffuse (Lambertian) Type determined as a function of: ◼ ◼ Snell’s Law Rayleigh criterion of surface roughness Types of Reflection (cont.) Uniform reflection Smooth surface Rough surface Snell’s Law (refraction) Angle of incidence Angle of reflection Zenith Air Leaf Smooth surface Angle of transmission Specular Reflection ◼ Rayleigh criterion: surface is SMOOTH in comparison to the incident wavelength if: h ≤ l / (8 cos qi) where: ◼ h = height variations above surface l = wavelength qi = angle of incidence ◼ Blue light (0.4 mm) : h ≤ 0.07 mm ◼ Microwave (1000 mm) : h ≤ 176 mm Angle of incidence = angle of reflection Diffuse (Lambertian) Reflection ◼ Rayleigh criterion: surface is ROUGH in comparison to the incident wavelength if: h > l / (8 cos qi) ◼ ◼ ◼ Blue light (0.4 mm) : h > 0.07 mm Microwave (1000 mm) : h > 176 mm Random scattering in all directions Reflection (cont.) ◼ ◼ ◼ Geometry of reflection = ƒ (surface roughness) Surface roughness = ƒ (wavelength of incident energy) Rocky terrain ◼ ◼ Smooth to longer microwave wavelengths Fine sand ◼ Rough to shorter visible wavelengths Bidirectional Reflectance Distribution Function (BRDF) ◼ Energy detected by sensor is a function of: ◼ ◼ Solar geometry AND Sensor viewing geometry Angle of Incidence/Observer Angles Spectral Signature Concept ◼ ◼ Describes spectral reflectance of a target at different wavelengths of EMR Spectral reflectance curve - graphs reflectance response as a function of wavelength ◼ Key to separating and identifying objects ◼ Selection of optimum wavelength bands Typical Spectral Reflectance Curves More Spectral Reflectance Curves A Spectral resolution B Factors Affecting Leaf Reflectance Hemispherical Reflectance, transmittance, and Absorption Characteristics of Big Bluestem Grass Jensen, 2000 Vegetation Reflectance – by wavelength Absorption Spectra of Chlorophyll a and b, b-carotene, Pycoerythrin, and Phycocyanin Pigments Chlorophyll a peak absorption is at 0.43 and 0.66 mm. Chlorophyll b peak absorption is at 0.45 and 0.65 mm. Optimum chlorophyll absorption windows are: 0.45 - 0.52 mm and 0.63 - 0.69 mm Spectral Reflectance Characteristics of Sweetgum Leaves (Liquidambar styraciflua L.) Jensen, 2000 Spectral Reflectance Characteristics of Selected Areas of Blackjack Oak Leaves Jensen, 2000 Cross-section Through A Hypothetical and Real Leaf Revealing the Major Structural Components that Determine the Spectral Reflectance of Vegetation Hypothetical Example of Additive Reflectance from A Canopy with Two Leaf Layers Jensen, 2000 Effect of Leaf Water Content on Reflectance Dominant Factors Controlling Leaf Reflectance Water absorption bands: 0.97 mm 1.19 mm 1.45 mm 1.94 mm 2.70 mm Jensen, 2000 Effect of Stress on Vegetation Reflectance Spectra for Urban Materials Measurement of Spectral Signatures ◼ Radiometer ◼ ◼ ◼ ◼ Records radiance from an object in specific wavelengths May record data for fixed bands or wavelength region Spectro-radiometer measures near-continuous spectra Reflectance Calculations ◼ ◼ Incident radiance is determined by taking measurements over barium sulfate or halon panel (reference panel) Bidirectional reflectance factor is calculated by dividing spectral radiance from object by panel measurement for given wavelength Handheld Data Logger Avoid self-shadowing or reflectance Non-imaging Goniometer in Operation at North Inlet, SC Boom Mounted Imaging Radiometer ◼ Imaging Radiometer: radiometer + “scanning” capability – builds a 2D image Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Datacube of Sullivan’s Island Obtained on October 26, 1998 Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS 224 channels each 10 nm wide with 20 x 20 m pixels Jensen, 2000
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