Image Processing Reference
In-Depth Information
types and predicting harvest times with 90-95% accuracy. This facilitates the
accurate forecasting of possible famines and helps put emergency measures
into place well in advance. Another example is related to the informal
settlement areas in the cities of the developing countries where remote sensing
can play an important role by virtue of its repetitive and synoptic coverage that
helps create a base map for many governmental organizations in a very rapidly
and haphazardly growing urban area. One can monitor urban growth, locate
slums and identify the physical characteristics of the slum areas in developing
countries by means of interpretation of high resolution satellite data (Sur et al.
2003 ; Seto and Duong 2002 ; Ehlers et al. 2002 ). The range of image processing
techniques generally used in land use change analysis encompasses various
operations, including geometric, radiometric and atmospheric corrections,
image compression and enhancement, spatial filtering and many of the
image processing techniques discussed in earlier chapters. Change detection
procedure, where two or more images are compared to determine differences,
involves the use of multispectral data sets to discriminate areas of land use
change between dates of imaging. The reliability of the change detection
process may strongly be influenced by a number of environmental factors
that might change between image dates. Two of the main methods are: image
differencing, where data from one date are simply subtracted from those of
the other (the difference in areas of no change should be zero). On the other
hand, image ratioing involves computing the ratio of the data from two dates
of imaging. Here, ratios for areas of no change should have a value of 1
(Lillesand et al. 2004; Jensen 2000 ; Sunar 1998 ; Jurgens 2000 ; Treitz and
Rogan 2004 ; El-Raey et al. 1995 ; Gibson and Power 2000 ; Green et al. 1994 ;
Mass 1999 ).
Vegetation indices are another common family of techniques used to
monitor change in environmental conditions within urban settings. Vegetation
indices are defined as dimensionless, radiometric measures that function as
indicators of the relative abundance and activity of green vegetation, often
including leaf-area index, percentage green cover, chlorophyll content, green
biomass, and absorbed photo synthetically active radiation. There are more
than 20 vegetation indices in use in the literature. Many are functionally
equivalent in information content, while some provide unique biophysical
information. One of the most commonly used vegetation indices is the
Normalized Difference Vegetation Index (NDVI). It is formulated as (NIR −
R)/(NIR + R), where NIR, and R represent data from infrared and red bands.
The NDVI is preferred to other vegetation indices for global vegetation
monitoring because it helps compensate for changing illumination conditions,
surface slop, aspect, and other extraneous factors (Lillesand et al. 2004;
Jensen 2000 ; Harrison and Jupp 1990 ).
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