Full text: Remote sensing for resources development and environmental management (Volume 1)

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
Relation between spectral reflectance and vegetation index 
NERC Unit for Thematic Information Systems, Department of Geography, University of Reading, UK 
ABSTRACT: Atmospheric corrections are applied to the Advanced Very High Resolution Radiometer (AVHRR) 
channel-1 and channel-2 data. Both raw and atmospherically corrected Normalized Difference Vegetation 
Indices (NDVIs) are calculated. A comparison between them shows a contrast enhancement by a factor of at 
least two when atmospheric corrections are applied. Spectral reflectances and atmospherically corrected 
NDVI are partially correlated indicating a possibility of improving surface cover classification using NDVI 
and spectral reflectances instead of NDVI values alone. Raw NDVI and atmospherically corrected NDVI do not 
have a unique relationship but are highly correlated. This indicates that atmospheric corrections be applied 
to each scene of interest. 
The visible channel (0.58-0.68 pm; hereafter 
referred to as channel-1) and near infrared channel 
(0.725-1.1 Pm; hereafter referred to as channel-2) 
data from the Advanced Very High Resolution Radio 
meter (AVHRR) instrument flown on the Tiros-N/NOAA 
meteorological satellites have been found to be 
useful for monitoring health and vigour of photo- 
synthetically active vegetation canopy. These data 
have been used for mapping and monitoring 
vegetation cover on local and continental scale (for 
example, see Tucker et al., 1983, 1984 and Hayes and 
Cracknell, 1984) as well as on a global scale 
(Justice et al., 1985). There is a daily coverage 
at higher latitudes but around the equator complete 
coverage requires three days. This means that the 
global coverage data could be obtained in three days 
if there were no cloud covers. There is an 
absorption band of chlorophyll within channel-1 
wavelength range whereas wavelengths within 
channel-2 spectral band width are strongly reflected 
by green pigments. In principle, the data from 
these two spectral channels should be correlated to 
the abundance of vegetation. Many relations exist 
in the literature for calculating vegetation index, 
for example, see Hayes (1985). Most popular of all 
relations is the so-called Normalized Difference 
Vegetation Index (NDVI) which is defined as 
DN2 - DN1 
DN2 + DN1 
where DN1 and DN2 are channel-1 and channel-2 pixel 
values, respectively. There are several advantages 
of using equation (1) rather than channel-2 data 
only; because of optical properties of photo- 
synthetically active chlorophyll as noted above, 
equation (1) results in enhanced values of NDVI, 
which could be useful particularly for low 
vegetation; the relation (1) partially compensates 
for atmospheric interference, solar elevation, 
changing solar irradiance on the surface and topo 
graphic effects (see, Justice et al., 1985). 
Ideally, one would have liked to remove atmos 
pheric effects from these data first and then 
calculated vegetation indices because band ratioing 
does not remove atmospheric effects completely 
(Holben and Justice, 1981). The reason is that 
atmospheric contaminations in channel-1 and channel-2 
are not proportional to each other. The larger the 
view angle of the sensor the larger the atmospheric 
contribution is expected to be. Therefore, even if 
there were no topographic effects and if surfaces 
were Lambertian in nature, the NDVI values calculated 
from equation (1) have strong view angle 
dependence (Duggin et al., 1982), a significant 
fraction of which is expected to be due to atmos 
pheric effects. Within the framework of vegetation 
mapping and monitoring, the same surface area is 
viewed from various viewing angles (from different 
orbits) and it is evident from the work of Duggin 
et al. (1982) and many others that the NDVI values 
do depend on the view angle. However, it has not 
yet been possible to come up with a perfect atmos 
pheric correction algorithm because of the diffi 
culties in estimating atmospheric contamination due 
to aerosols. Nevertheless, an approximate estimate 
of atmospheric contribution to remotely sensed data 
can be made. 
One finds the same NDVI value for several surface 
cover types (Townshend and Tucker, 1985) and, 
therefore, vegetation type classification using NDVI 
values alone has limited success. Ideally, one would 
like to have several spectral band data which are 
partially or poorly correlated among themselves so 
that each spectral channel data carries information 
about the nature of surface cover which supplements 
information carried in other spectral channels. 
Using Landsat Thematic Mapper (TM) data Toll (1985) 
demonstrates that the land cover classification 
accuracy does not improve by adding spectral 
channel data which are highly correlated to other 
channel(s) data which are already used for classi 
fication. Also, when photosynthetically active 
chlorophyll amount increases (say, in dense forests) 
the NDVI values calculated from equation (1) tend to 
saturate thereby limiting the range of applicability 
of equation (1). Under such circumstances it would 
be interesting to see how channel-1 reflectivity 
changes and whether or not this reflectivity is 
still a sensitive function of vegetation abundance. 
It is in this spirit that a brief summary of atmos 
pheric correction technique will be presented, 
channel-1 and channel-2 reflectivities will be 
calculated, raw and atmospherically corrected NDVI 
will be calculated and relationship betweèn 
reflectivities and NDVI values will be examined in 
order to find some uncorrelated or partially 
correlated parameters which may prove to be 
valuable for land cover classification.

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.