1. Vegetation Indices Lecture 7 prepared by R. Lathrop 10/99 Modified 3/06

2. Remote Sensing of the Earth: Clues to a Living Planet Remote sensing scientists measure the amount of energy in different spectral wavelengths reflected from the earth’s surface as one means of monitoring the earth’s biosphere. Where there are a lot of plants on the earth’s surface, less red and blue light is measured by the satellite sensor. Likewise, the more green plants, the more near infrared energy that is measured. By combining measurements in the red and near-infrared wavelengths, scientists have devised a remotely sensed vegetation index or what is sometimes referred to as a ‘greenness index’. The more plants, the greener the earth, the higher the index.

3. Vegetation Indices Linear combination of image bands used to extract information about vegetation: biomass, leaf area, productivity Most vegetation indices (VI’s) based on the differential reflectances of healthy green vegetation, dead/senescent vegetation and soil in visible vs. near IR wavelengths

4. Photosynthetically Active Radiation PAR : 0.40-0.70 um, portion of EMR absorbed by plant pigments and used in photosynthesis APAR: PAR energy actually absorbed by a plant canopy IPAR: intercepted PAR, probability that photons are intercepted by plant elements

5. How plant leaves reflect light Graphics from http://landsat7.usgs.gov/resources/remote_sensing/radiation.php

6. How plant leaves reflect light Sunlight B G R NIR Leaf Transmitted light Incoming light Reflected light Blue & red light strongly absorbed by chlorophyll Cross- section of leaf NIR light scattered within leaf: some reflected back, some transmitted through NIR

7. Reflectance from green plant leaves Chlorophyll absorbs large % of red and blue for photosynthesis- and strongly reflects in green (.55um) Peak reflectance in leaves in near infrared (.7-1.2um) up to 60% of infrared energy per leaf is scattered up or down due to cell wall size, shape, leaf condition (age, stress, disease), etc. Reflectance in Mid IR (2-4um) influenced by water content-water absorbs IR energy, so live leaves reduce mid IR return

8. Red Reflectance N I R R e f l e c t a n c e Spectral Feature Space Example Pixel X proportions: IS: 50% Grass: 30% Trees: 20% Sub-pixel Estimation Soil Line Increasing Vegetation As green leaf area increases NIR increases red decreases

9. Bidirectional Reflectance Distribution Function: BRDF Lambertian surface: reflected energy is scattered equally in all directions; no direction bias = isotropic Vegetation canopies are not Lambertian surfaces but rather demonstrate definite directional bias; anistropic BRDF is the hemispherical distribution of reflectances for a feature as a function of illumination geometry Bottom line: viewing and illumination angle are important; a nadir view of the same feature may record a different reflectance than a side view or look differently under different sun angles Some sensors designed to provide different look angles at the same feature; e.g., a forward, nadir and backwards view

10. Measuring the BRDF: example Graphics from http://car.gsfc.nasa.gov/application_brdf.html Aircraft-mounted radiometer used to fly a closed circle and record reflectance of a site Note that the reflectance is not equally distributed across all directions

11. Vegetation indices Simple ratio:nir/red Normalized Difference VI (NDVI): nir - red nir + red NDVI ranges from -1 to + 1 Transformed VI to eliminate negative values: TVI : /NDVI + 0.5

12. Vegetation Indices: Issues VI is a B&W image positively correlated with “greeness”, as NIR increases and red decreases, VI increases AVHRR Landsat TM

13. Vegetation Indices: Issues Soil brightness variations complicating the VI response Asymptotic relationship leading to loss in sensitivity at high vegetation amounts Atmospheric interference, especially in the Red band. Best practice is to convert the original DN values to radiance (preferably atmospherically corrected) or reflectance before computing the vegetation index

14. Vegetation Indices: Issues Scaling: ratio of averages (NDVI of larger pixels; e.g., AVHRR pixels) is not the same as the average of the ratios (average NDVI of smaller pixels; e.g., Landsat TM) Example (sample area from Landsat TM: 30 m pixels vs. km 2 composite) Ratio of averages: Mean red = 28 3 Mean NIR = 73 NDVI = (73-28)/(73+28) = 45/101 = 0.446 Average of ratios: NDVI = 0.416

15. Vegetation indices:PVI Perpendicular VI determines a pixel’s orthogonal distance from the soil line in image feature space (X axis: red; Y axis: NIR) The objective is to remove the effect of soil brightness and isolate reflectance changes due to vegetation only

16. Vegetation Indices:SAVI Soil Adjusted Vegetation Index (SAVI) is a technique to minimize soil brightness influences. Involves shifting the origin of the nir-red feature space to account for 1 st order soil-vegetation interactions and differential red & nir extinction through vegetation canopies SAVI = (1+L) (nir-red) / (nir + red + L) Where L = 0 to 1. L = 1 for low veg density, L = 0.5 for intermed veg density L = 0.25 for high veg density From: Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI), Rem. Sens. Environ. 25:295-309.

18. Modified Soil Vegetation Index (MSAVI) employs a correction factor to reduce sensitivity to soil variation across a scene MSAVI 0 = ((NIR – RED) (1 + L 0 )) / (NIR + RED +L 0 ) MSAVI 1 = ((NIR – RED) (2 - MSAVI 0 )) / NIR + RED +1 – MSAVI 0 Must empirically determine L MSAVI 2 = (2NIR + 1 – ((2NIR +1) 2 – 8(NIR – RED)) -1/2 / 2 Vegetation indices:MSAVI

19. Vegetation indices: ARVI Atmospherically Resistant Vegetation Index (ARVI) incorporates the blue channel to account for atmospheric scattering in the red channel by using the difference between the radiance in the red and blue channel ARVI = (NIR – RB) / (NIR + RB) Where RB = Red –  (Blue – Red) Where Blue is Landsat TM band 1,visible blue wavelengths  unless the aerosol model is known a priori

20. Enhanced Vegetation Index (EVI): developed to optimize the vegetation signal with improved sensitivity in high biomass regions, a reduction of sensitivity to the canopy background signal and a reduction in atmosphere influences. EVI = G * (NIR – RED) / (NIR + C 1 *RED – C 2 *BLUE + L) Where C 1 = atmosphere resistance red correction coefficient = 6 C 2 = atmosphere resistance blue correction coefficient = 7.5 L = Canopy background brightness correction factor = 1 G = Gain factor = 2.5 Note: Example coefficients, may vary depending on sensor/ situation Miura, T., Huete, A.R., Yoshioka, H., and Holben, B.N., 2001, An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction, Remote Sens. Environ., 78:284-298. Miura, T., Huete, A.R., van Leeuwen, W.J.D., and Didan, K., 1998, Vegetation detection through smoke-filled AVIRIS images: an assessment using MODIS band passes, J. Geophys. Res. 103:32,001-32,011. Vegetation indices: EVI

21. Graphic taken from http://tbrs.arizona.edu/projects/ modis/figures/Slide5.GIF

22. MODIS EVI vs. NDVI

23. Wide Dynamic Range VI (WDRVI) NDVI suffers from a decrease in sensitivity at medium to high leaf areas because the NIR reflectance continues to increase with increasing LAI while red absorption tends to stabilize at lower levels WDVRI = (  * NIR – RED) / (  * NIR + RED) where a is a weighting coeff, 0 <  < 1  < 1, the contribution from the NIR attenuated  between 0.05 and 0.2 have been found effective in row crops For more info: Gitelson. 2004. J Plant Physiology 161:165-173.

24. Vegetation indices Numerous studies have explored the relationship between remotely sensed vegetation indices and field measured estimates of vegetation amount: above-ground biomass, leaf area Goal is to be able to estimate and map these key variables of ecosystem state Best relationships obtained in closed canopy crops. Woody material complicates but does not invalidate the relationship

25. For good review of VI NASA Remote Sensing Tutorial http://rst.gsfc.nasa.gov/Homepage/Homepa ge.html For specifics on Vegetation Indices http://rst.gsfc.nasa.gov/Sect3/Sect3_4.html

26. Global Biosphere Vegetation Monitoring One of the main satellite systems that have been used to measure the vegetation index of the earth over long periods of time is the AVHRR satellite. AVHRR stands for Advanced Very High Resolution Radiometer This system has been largely replaced by the MODIS AQUA and TERRA systems

27. Global AVHRR composite 1 band in the Red:.58-.6 um 1 band in the NIR:.72-1.1 um Vegetation Index to map vegetation amount and productivity

28. Global Biosphere Vegetation Monitoring NOAA AVHRR used to create global “greenness” maps based on NDVI. Composited over biweekly to monthly intervals.

29. Integrated NDVI: summed over the growing season to provide index of vegetation productivity modified from Goward et al. 1985 Vegetation 64:3-14. Apr May June Jul Aug Sept Oct Temperate broadleaf forest Boreal forest Desert Int NDVI

30. Global NDVI summed over an entire year

31. Integrated Growing Season NDVI modified from Goward et al. 1985 Vegetation 64:3-14. NPP g/m2/yr 1400 0 Integrated NDVI 0 1 2 3 4 5 Moist conifer Dec. broadleaf Boreal conifer Grassland Tundra desert

32. Global NDVI converted to LAI (leaf area index m 2 /m 2 )

33. Remote Sensing of the Earth: Clues to a Living Planet You can access these images over the INTERNET You can either browse through individual images or watch an animation http://svs.gsfc.nasa.gov/search/Keyword/NDVI.html

34. Remote Sensing of the Earth: Clues to a Living Planet First, click on the Hologlobe: Vegetation Index for 1991 on a Flat Earth animation. Open it, and click on the > button. Watch closely, can you observe the Green Wave in the northern hemisphere? What about the Brown Wave? Now look at the southern hemisphere. What do you observe?

35. Can you see the Green Wave? NASA/Goddard Space Flight Center Scientific Visualization Studiohttp://svs.gsfc.nasa.gov/vis/a000000/a001300/a001308/index.html

36. Remote Sensing of the Earth: Clues to a Living Planet Now take a look at the Northern hemisphere in greater detail. Click on the NDVI Animation over continental United States. Can you find where you live? How long does it stay green? Compare Florida with Maine or Minnesota.

37. North America: Close-up NASA/Goddard Space Flight Center Scientific Visualization Studio http://svs.gsfc.nasa.gov/vis/a000000/a002500/a002568/index.html

38. Remote Sensing of the Earth: Clues to a Living Planet To access more recently acquired AVHRR imagery go to the National Oceanographic & Atmospheric Administration (NOAA) Satellite Active Archive http://www.saa.noaa.gov/

39. 36 discrete bands between 0.4 and 14.5 µm spatial resolutions of 250, 500, or 1,000 m at nadir. Signal-to-noise ratios are greater than 500 at 1-km resolution (at a solar zenith angle of 70°), and absolute irradiance accuracies are < ±5% from 0.4 to 3 µm (2% relative to the sun) and 1 percent or better in the thermal infrared (3.7 to 14.5 µm). MODIS instruments will provide daylight reflection and day/night emission spectral imaging of any point on the Earth at least every 2 days, operating continuously. For more info: http://eospso.gsfc.nasa.gov/eos_homepage/mission_profiles/instruments/MODIS.php

40. Additional variables also being measured by Aqua include radiative energy fluxes, aerosols, vegetation cover on the land, phytoplankton and dissolved organic matter in the oceans, and air, land, and water temperatures. The AQUA Platform includes the MODIS, CERES and AMSR_E instruments. Aqua was formerly named EOS PM, signifying its afternoon equatorial crossing time. AQUA was launched May 2002. For more info: http://aqua.nasa.gov/ “Aqua,” Latin for “water,” is a NASA Earth Science satellite mission named for the large amount of information that the mission will be collecting about the Earth’s water cycle, including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice.

41. Earth Observing 1 NASA’s New Millennium Program Multispectral instrument that is a significant improvement over the Landsat 7 ETM+ instrument – Advanced Line Imager (ALI) Hyperspectral land imaging instrument – Hyperion Low-spatial/high-spectral resolution imager that can correct systematic errors in the apparent surface reflectances caused by atmospheric effects, primarily water vapor - Linear Etalon Imaging Spectrometer Array (LEISA) Atmospheric Corrector (LAC)

42. EO-1: Advanced Line Imager (ALI) The EO-1 ALI operates in a pushbroom fashion at an orbit of 705 km, 16 day repeat cycle. Launched in Nov 2000. ALI provides Landsat type panchromatic and multispectral bands. These bands have been designed to mimic six Landsat bands with three additional bands covering 0.433-0.453, 0.845-0.890, and 1.20-1.30 µm. The ALI has 30M resolution multi- spectral 10m panchromatic. 37km swath width. More info: http://eo1.usgs.gov/ali.php Mt. Fuji Japan ALI Bands: 6,5,4.

43. ENVISAT In March 2002, the European Space Agency launched Envisat, an advanced polar- orbiting Earth observation satellite which provides measurements of the atmosphere, ocean, land, and ice. http://envisat.esa.int/

44. ENVISAT: primary instruments for land/sea surface remote sensing ASAR - Advanced Synthetic Aperture Radar, operating at C-band, MERIS - is a 68.5 o field-of-view pushbroom imaging spectrometer that measures the solar radiation reflected by the Earth, at a ground spatial resolution of 300m, in 15 spectral bands, programmable in width and position, in the visible and near infra-red. MERIS allows global coverage of the Earth in 3 days. http://envisat.esa.int/

45. SPOT Vegetation Earth observation sensor on board of the SPOT satellite in blue, red, NIR & SWIR Daily coverage of the entire earth at a spatial resolution of 1 km The first VEGETATION instrument is part of the SPOT 4 satellite and a second payload, VEGETATION 2, is now operationally operated onboard SPOT 5. http://www.spot-vegetation.com/

46. SPOT Vegetation Spectral Bands http://www.spot-vegetation.com/

47. Global Annual Changes of Vegetation Productivity

48. SPOT Vegetation Free products are : extracts from ten day global syntheses. available 3 months after insertion in the VEGETATION archive. in full resolution (1km). in plate carrée projection. available on 10 predefined regions of interest. in the standard VEGETATION product format. http://free.vgt.vito.be/

49. 3 visible/NIR(VNIR: 0.5 and 0.9 µm) with 15-m resolution 3 mid IR (SWIR: 1.6 and 2.43 µm) with 30-m res. 5 TIR (8 and 12 µm) with 90-m resolution 60- km swath whose center is pointable cross-track ±8.55° in the SWIR and TIR, with the VNIR pointable out to ±24°. An additional VNIR telescope (aft pointing) covers the wavelength range of Channel 3. By combining these data with those for Channel 3, stereo views can be created, with a base-to-height ratio of 0.6. Overpass every 16 days in all 14 bands and once every 5 days in the three VNIR channels. For more info: http://eospso.gsfc.nasa.gov/eos_homepage/mission_profiles/instruments/ASTER.php

50. Vegetation Water Stress Indices Moisture Stress Index (MSI) contrast water absorption in the MIR with vegetation reflectance (leaf internal structure) in the NIR MSI: MIR / NIR or R 1600 /R 820 Normalized Difference MSI : NDMIS: (NIR – MIR) / (NIR + MIR) Normalized Difference Water Index NDWI:(R 860 -R 1240 ) / (R 860 +R 1240 )

51. Other Vegetation Indices: NBR Normalized Burn Ratio (NBR) contrasts NIR(TM4) which decreased after fire and MIR(TM7) which increased after fire NBR : (TM4 – TM7) / (TM4 + TM7) Differencing of pre- vs. post-fire NBR images has been found to be an effective measure of burn severity For more info: http://nrmsc.usgs.gov/research/ndbr.htm

52. Subpixel Analysis: Unmixing mixed pixels Band i Band j Spectral endmembers: signature of “pure”land cover class unknown pixel Unknown pixel represents some proportion of endmembers based on a linear weighting of spectral distance. For example: 60% endmember 2 20% endmember 1 endmember1 endmember2 endmember3

53. Subpixel Analysis: Unmixing mixed pixels Linear mixture modeling assumes that a pixel’s spectral signature is the results of a linear mixture of the spectra from the component classes. X = Mf + e where X = mixed pixel spectral signature M = n x c matrix of endmember spectra f = c x 1 vector of land cover class proportions N = number of bands C = number of classes, c must be < n + 1 E = noise term Simple least-squares approach can then be used to “unmix” and calculate the proportions of land cover in each pixel.

54. Sub-pixel analysis: linear mixture modeling Least squares approach selecting f that minimizes the following (x – Mf) T (x-Mf) Can be unconstrained or constrained such that 0 <= f <= 1 With no constraints can be simplified

55. Subpixel analysis of urban land cover Landsat TM pixel boundaries on IKONOS image backdrop

56. Subpixel analysis of urban land cover

57. Sub-pixel analysis of urban land cover IKONOS for reference Landsat TM output Blue = IS Green – lawn Red = tree Study Area 1Study Area 2