4 - Central Institute of Brackishwater Aquaculture
4 - Central Institute of Brackishwater Aquaculture
4 - Central Institute of Brackishwater Aquaculture
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National Workshop-cum-Training on Bioinformatia and Information Management In <strong>Aquaculture</strong><br />
0 Once in 2 years Once in 60 years<br />
Once in 150 yew<br />
Drainage congested area<br />
Figure 7 Flood risk zone map <strong>of</strong> Kosi river ( Source : NRSA)<br />
4.1.2 Capturing the spatial variability <strong>of</strong> cropping pattern<br />
Spatial variability <strong>of</strong> cropping pattern get reflected in terms <strong>of</strong> spectral variations<br />
observed on the satellite imageries, if spatial, spectral, radiometric and temporal<br />
resolutions are adequate. Capturing these variability enables monitoring the<br />
state and conditions <strong>of</strong> the agricultural ecosystem. Cropping pattern <strong>of</strong> an area<br />
reflects the spatial arrangement <strong>of</strong> crops and intercrop variability that exists.<br />
Multi-date satellite data is essential to derive cropping pattern as in general there<br />
are more than one crop grown in a season with different growing calendar.<br />
Satellite remote sensing has number <strong>of</strong> advantages for monitoring cropping<br />
pattern such as (i) Multi-spectral observations which allows crop discrimination<br />
and growth assessment, (ii) Synoptic coverage at multiple spatial resolutions<br />
which leads to micro and macro level assessments over large areas (iii) Multitemporal,<br />
i.e. multiple observations in a crop season (iv) Multiple utility i.e. same<br />
data can be used for many applications and (v) Full data archival and reanalysis<br />
capability i.e, historical primary data can be reanalyzed with new models as well<br />
as for post-harvest validation.<br />
f<br />
Cropping pattern analysis <strong>of</strong> Gangetic active flood plain <strong>of</strong> Bhagalpur district,<br />
Bihar extending from 25' 12' 50"to 25' 24' 31"N latitude and 86O43'37" to<br />
87°13'57" E longitude was done using WiFS data <strong>of</strong> IRS-ID <strong>of</strong> the four dates<br />
(November 30, 1999, January 19, 2000, March 09, 2000 and April 03, 2000)<br />
during post flooding period (i.e. rabi season). The spatial resolution <strong>of</strong> WiFS<br />
sensor is 180m and it has two spectral bands i.e. red (0.77 to 0.86hm) and NIR<br />
(1.55 to 1.77pm). Along with multitemporal image registration, radiometric<br />
correction was carried based on pseudo-invariant features in the images.
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