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Evaluation of IRS LISS III data for apple orchard inventory - A case study covering Jubbal - Kotkhai block of Shimla district
N. S. Mehta1, Nitin Bhatt1, R. S. Thapa2,
Arvind Sharma3, R. K. Sood2 and S. Panigrahy1
1Space Applications Centre, Ahmedabad - 380 015
2H P Remote Sensing Cell, Shimla – 171 009
3State Department of Horticulture, Shimla –171 002
The remote sensing technology has potential in
estimating crop acreage and production at district, regional and national level due to its
multi spectral, synoptic and repetitive coverage. This technology is being used
operationally by many advanced nations for monitoring natural resources. Application of
space - borne remote sensing is of particular significance to India, because it is the
second largest producer of fruits (469 lakhs tons per year) and vegetables (580 lakhs tons
per year) in the world. Shimla district has occupied place of pride in the field of
horticulture not only in the state but also in the country, it is the biggest apple growing
district in Himanchal Pradesh. Apples are grown on 83 percent of horticultural land in the
district. This study discusses results obtained on apple orchard inventory covering Jubbal -
Kotkhai block of Shimla district, using IRS LISS III data. IRS LISS III data of May 27, 1999
were used to classify apple orchards of the block. Unsupervised classification was carried
out using ISO - data clustering after masking the higher / lower reflectance classes such as
cloud and shadow. This classification grouped apple orchards into four groups. The out put
of ISO - data clustering was used to collect ground truth (GT) for apple orchards and other
land cover classes. After ground verification, apple orchards were classified into three
main classes on the basis of their density i.e. dense, moderatively dense and sparse
orchards. Other land cover classes identified are forests, agricultural fallow, barren hills
etc. The GT collected was also used to train the classifier.
MXL classification was carried out using three (green, red and IR) and four (green, red, IR
and MIR) band data, using complete enumeration method. Analysis carried out using three band
data shows that apple orchards are occupying 33.18 percent of geographical area of the block
against 26.08 percent provided by the State Department of Horticulture, where as using four
band data area under orchards comes out to be 24.14 percent. MXL classification using four
band data shows more area under null class as compared to three band data. There is also
some overlap in signature between dense apple orchards and broad leave mixed forest. Overall
classification accuracy using three band data was found to be 93.53 percent and including
MIR band the accuracy comes out to be 91.82 percent.
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