Two-band vegetation indices Three-band vegetation indices Class 9 Vegetation Indices Two-band vegetation indices Three-band vegetation indices Leaf area index

Structure of a Leaf Red and blue light Cuticle largely absorbed for use in photosynthesis Strong Infrared reflectivity and transmittance. Cuticle Upper Epidermis Palisade Layer Spongy Tissue Lower Epidermis and Cuticle Stomates and Guard Cells Campbell 16.3

r r Moss Reflectance (%) n r Wavelength (nm) Visible Near Infrared 50 40 Visible Near Infrared 30 Black Spruce Needle Moss Reflectance (%) 20 r n 10 r r GREEN RED BLUE 400 500 600 700 800 900 1000 Wavelength (nm)

Vegetation Indices Quantitative measures for vegetation abundance and vigour. Formed from combinations of two to several spectral bands that are added, divided, or multiplied in a manner to yield a single value that indicates the amount or vigour of vegetation within a pixel. Campbell 16.5

Leaf Area Index (LAI) LAI is defined as the total one-sided (or one half of the total all-sided) green leaf area per unit ground surface area. It is an important biological parameter because: it defines the area that interacts with solar radiation and provides the remote sensing signal; It is the surface responsible for carbon absorption and exchange with the atmosphere. Campbell 16.6

Spectral response to vegetation amount (grass)

Response of Red and NIR to LAI changes in crops Martin and Heiman, 1986, Photogrammetric Engineering and Remote Sensing

croplands, grasslands Near Infrared Reflectance Red LAI Campbell 16.5

Response of Red and NIR to LAI Changes Chen, 1996, Canadian Journal of Remote Sensing

Forest remote sensing (Hyperspectral) Chen and Leblanc, 2000 Measurements Simulation Chen and Leblanc, 2000

Forests More trees-foliage means more shadows when the density is low Because transmittance in near-infrared is high infrared shadows appear less shaded than shadows in visible Near Infrared Reflectance Red LAI Campbell 16.5

r r Moss r - r NDVI = r + r Reflectance (%) n r Wavelength (nm) 50 r - r NDVI = n r r + r n r 40 Visible Near Infrared 30 Black Spruce Needle Moss Reflectance (%) 20 r n 10 r r GREEN RED BLUE 400 500 600 700 800 900 1000 Wavelength (nm)

Vegetation Indices Normalized Difference Vegetation Index (NDVI) Saturation problems NDVI LAI Simple Ratio (SR) SR LAI NIR = reflectance in near-infrared band RED = reflectance in red band

Perpendicular Vegetation Index (PVI) Near Infrared reflectance S = soil, V = vegetation X C: dry soil B: wet soil X: “pure” vegetation Y: Partialy vegetated pixel Y C W Based on Eucledian distance Near Infrared reflectance B A Red Reflectance Campbell 16.9

Near Infrared reflectance Simple Ratio (SR) > > SR1 SR2 SR3 > SR4 SR1 SR2 SR3 Near Infrared reflectance SR4 Red Reflectance

Near Infrared reflectance Normalized Difference Vegetation Index (NDVI) > NDVI1 > NDVI2 NDVI3 > NDVI4 NDVI1 NDVI2 NDVI3 Near Infrared reflectance NDVI4 Red Reflectance

Principles of SAVI Huete, 1988, Remote Sensing of Environment

Near Infrared reflectance Soil Adjusted Vegetation Index (SAVI) SAVI1 SAVI2 SAVI3 SAVI4 > SAVI1 SAVI2 SAVI3 Near Infrared reflectance SAVI4 L Red Reflectance

Two-band Vegetation Indices (1) Name Formula Reference NDVI Rouse et al., 1974 SR Jordan, 1969   MSR Chen, 1996 RDVI Roujean and Breon, 1995 WDVI , Clevers,1989

Two-band Vegetation Indices (2) Name Formula Reference SAVI , Huete, 1988 SAVI1 Qi et al., 1994 SAVI2   GEMI Pinty & Verstraete, 1992 NLI Goel & Qin, 1994

Two-band Vegetation Indices:References Chen, J. M., (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sensing. 22:229-242 Clevers, J. G. P. W. (1989). The applications of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens. Environ. 29:25-37.   Goel, N. S., and Qin, W. (1994). Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer Simulation, Remote Sens. Rev. 10:309-347. Huete, A.R. (1988). A soil adjusted vegetation index (SAVI), Remote Sens. Environ. 25:295-309. Huete, A. R. and Liu, H. Q., (1994). An error and sensistivity anbalysis of the atmospheric- and soul-correcting variants of the NDVI for the MODIS-EOS. IEEE Trans. Geisci. and Remote Sens. 32:897-905. Jordan, C.F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663-666. Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:261-270.    Pinty, B. and Verstrate, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101:15-20. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S. (1994). A modified soil adjusted vegetation index, Remote Sens. Environ. 48:119-126. Rouse, J. W., Hass, R. H. Shell, J. A., and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS-1. Third Earth Resources Technology Satellite Symposium 1: 309-317. Roujean, J.-L. and Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidrectional reflectance measurements. Remote Sens. Environ. 51:375-384.

Some useful features of vegetation indices (1) 1. NDVI, SR, MSR are based on the ratio of red and NIR bands. They are often preferred because the ratio can remove much measurement noise in individual bands 2. SAVI, SAVI1 and SAVI2 have the advantage of considering the influence of the soil background Effect, but it is not based on the ratio and much of Measurement noise is retained 3. Other more complicated indices might have Advantages in specific applications, but they have The potential to amplify measurement noise Chen, 1996, Canadian Journal of Remote Sensing

Effectiveness of VIs in retrieving LAI of boreal forests Note: The usefulness of VIs in other ecosystems may differ

Satellite-based LAI algorithm development Canada-wide LAI map validation involving all five forest research centres and several universities (satellite: Landsat; ground data: TRAC) Chen et al. 2001, Remote Sensing of Environment

LAI - Agriculture

Three-band Vegetation Indices Name Formula* Reference   ARVI Kaufman and Tanre, 1992 SARVI , Liu and Huete, 1995 SARVI2 Huete et al., 1996 MNDVI Nemani et al., 1993 RSR Brown et al., 1999

Three-band Vegetation Indices (References) Brown, L. J., J. M. Chen, S.G. Leblanc, and J. Cihlar. 2000. “Short Wave Infrared Correction to the Simple Ratio: An Image and Model Analysis,” Remote Sens. of Environ, . 71:16-25 Huete, A. R., C. Justice, W. van Leeuwen. 1996. “MODIS vegetation index (MOD 13)”. EOS MODIS Algorithm-Theoretical baiss document, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771. USA. 115pp. Kaufman, Y. J., and Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30:261-270. Liu, H. Q. and A. R. Huete. 1995 “A feedback based modification of the NDVI to minimize canopy background and atmospheric noise.” IEEE Trans. Geosci. Remote Sens. 33:481-486. Nemani, R., L. Pierce, S. Running, and L. Band. 1993. “Forest Ecosystem Processes at the Watershed Scale: Sensitivity to Remotely Sensed Leaf Area Index Estimates,” Intl. J. Remote Sens., 14, Pp. 2519-2534.

Some useful features of vegetation indices (2) 1. ARVI, SARVI, and SARVI2 are able to reduce the the influence of the atmosphere. 2. MNDVI and RSR are designed to reduce the background effects. The best way is to do proper atmospheric correction and use ratio-based indices

The mid-infrared scales the background effect Reduced Simple Ratio The mid-infrared scales the background effect Brown et al, 1999

Brown et al, 1999, Remote Sensing of Environment SR RSR LAI LAI a = aspen m = mixed s = spruce p = pine Brown et al, 1999, Remote Sensing of Environment