Differences between Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru
Remote Sensing The science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with object, area, or phenomenon under investigation.
What do sensors measure? • Measures the amount of variability of the the light source • It filters the light coming to it along the path way • What is actually being measured in sensor systems is spectral radiance or the radiant energy from the target
Light specturm • Major components of visible light spectrum are violet, blue, green, yellow, orange and red • Blue and red are used in photosynthesis
Light absorption at different wavelengths • Green leaves have a reflectance of 20 percent or less in the 500 to 700 nm range (green to red) and about 60 percent in the 700 to 1300 nm range (near infra-red). 1=bacteriochlorophyll, 2=chlorphyll a, 3= chlorophyll b, 4=phycoerythrobilin, 5= beta carotene)
NDVI, what is it? • It is normalized difference vegetation index. • Used to measure green biomass (Tucker, 1979) • Degree of greenness = chlorophyll concentration • Actually measure photo synthetically active radiation absorbed by the canopy (Sellers, 1985) • NDVI values vary with absorption of red light by plant chlorophyll and the reflection of infrared radiation by water-filled leaf cells. It is correlated with Intercepted Photo-synthetically Active Radiation (IPAR).
NDVI, what is it? • It is a function of Incident and reflected light RNDVI= NIR – Red , NIR + Red GNDVI= NIR – Green , NIR + Green NIR 750-1300 nm Red 600-700 nm Green 550 nm Where 0< NDVI< 1
Differences between the Green and Red NDVI • Red band is used to measure green biomass and estimate changes in vegetation state, but it is only sensitive to low chlorophyll-a concentration (3-5 g/cm2)(Gitelson et al., 1997) • Good at early stage • The Green band (520-630nm) is sensitive to a wide range of chlorophyll-a concentration (0.3 – 45 g/cm2) (Gitelson et al., 1997) • May work for late crop stages prediction
What do NDVI predict? • Grain yield from reflectance readings of NIR and Red (Tucker et al., 1981) • Important in variable rate fertilizer recommendation based on predicted yield • Relationship decrease as wheat became ripened. • Two late season readings (Feekes 10.5, flowering to grain filling) may give more stable prediction as compared to that of a single reading (Pinter et al., 1981)
What do NDVI predict? • Wheat biomass (NIR) and Nitrogen uptake reliably predicted at Feekes 4 & 5 (Stone et at., 1997, Lukina et al., 2000) • At the same stage percent ground cover and NDVI are co-related with vegetative biomass of wheat.
EY = NDVI1 + NDVI2 GDD EY Relationship between Estimated Yield (EY) computed from NDVI at Feekes growth stages 4 and 5, divided by growing degree days and observed grain yield, at six locations, 1998 and 1999.
The Sahel Zone The image above from July 1989 depicting the continent of Africa from the NOAA AVHRR weather satellite illustrates the NDVI concept. Areas in yellow such as the Sahara desert have very low levels of vegetation (low NDVI). Areas in red the such as the tropics along the equator are highly vegetated (high NDVI). The second image was acquired 6 months earlier and illustrate the effect of less rain on the NDVI's (a lowering effect) – notice the shrinking area of reddish areas along the equator.
The image shows senescence in northern hardwoods in some areas, and higher NDVI in conifer dominated areas in the north, and aspen-birch, and mixed conifer and deciduous forests in other parts of the Lake Superior Basin.
Limitations of NDVI • It can only measure the surface vegetation biomass at late growth stages • Reading is 2 dimensional not 3D • It can not predict the amount of nitrogen concentration in the plant