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Natural resource characterization through total information content using multispectral Remote Sensing satellite data
R. N. Sahoo and M. Bhavanarayana
Divison of Agricultural Physics,
Indian Agricultural Research Institute,
New Delhi – 110 012
Application of space science through remote
sensing holds tremendous prospects for modernizing agriculture and one such area is natural
resource characterization and discrimination, which is essential for optimum utilization of
our total natural resources – land, water, vegetation etc. In order to make the feature
information quantitative and characterization more specific, there are many indices
developed, but they are less target specific and require band selection. Hence a need for
numerical technique capable of complete data compression, irrespective of the number of
wavebands, was thus felt. Such a technique was evolved to compress remotely sensed data
received from either spectroradiometer or remote sensing satellite irrespective of number
of wavebands into one dimensional (1-D) index called Total Information Content Index (H)
and to evaluate its strength in characterizing different natural resources broadly soil,
water and vegetation. The objectives of the present study were (1) To develop an algorithm
for numerical technique capable of complete data compression, using multispectral
spectroradiometer and satellite data, (2) To evaluate this technique in characterizing and
discriminating different natural resources such as vegetation, soils and water body and (3)
To test its potential in differentiating different soil moisture levels in visible and NIR
region of electromagnetic spectrum. The algorithm of the technique was developed in C
language using Shannon's Information Theory and customized with digital image processing
software, IDRISI. The developed 1-D index, H, computed from DN and radiance values of
wavebands of IRS-1B, LISS-II and IRS-1C, LISS-III could differentiate not only different
natural surface features like sand, water body, fallow land, wet land, crops / shrubs and
mixed vegetation, but also different soil map units of the study area in one dimensional
space.
The study using spectroradiometer and IRS-IB, LISS-II revealed that the proposed index was
proved to be a potential estimator of soil moisture in visible and near infrared region of
the electromagnetic spectrum. This ability of the developed index was not shared by any
other conventional remote sensing techniques particularly in visible and NIR region of
electromagnetic spectrum. More ever predictive accuracy of the index for soil moisture
increased significantly with increase in number of wave bands and decrease in bandwidths.
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