The purpose of this study was to characterize the land use,
vegetation structure, and diversity in the Barnowpara Sanctuary, Raipur
district, Chhattisgarh, India through the use of satellite remote sensing and
GIS. Land cover and vegetation were spatially analyzed by digitally
classifying IRS 1D LISS III satellite data using a maximum likelihood
algorithm. Later, the variations in structure and diversity in different forest
types and classes were quantified by adopting quadratic sampling procedures.
Nine land-cover types were delineated: teak forest, dense mixed
forest, degraded mixed forest, Sal mixed forest, open mixed forest, young
teak plantation, grasslands, agriculture, habitation, and water bodies. The
classification accuracy for different land-use classes ranged from 71.23%
to 100%. The highest accuracy was observed in water bodies and grassland,
followed by habitation and agriculture, teak forest, degraded mixed
forest, and dense mixed forest. The accuracy was lower in open mixed
forest, and sal mixed forest. Results revealed that density of different
forest types varied from 324 to 733 trees ha-1, basal area from 8.13 to 28.87
m2
·ha-1 and number of species from 20 to 40. Similarly, the diversity
ranged from 1.36 to 2.98, concentration of dominance from 0.06 to 0.49,
species richness from 3.88 to 6.86, and beta diversity from 1.29 to 2.21.
The sal mixed forest type recorded the highest basal area, diversity was
highest in the dense mixed forest, and the teak forest recorded maximum
density, which was poor in degraded mixed forests. The study also showed
that Normalized Difference Vegetation Index (NDVI) was strongly correlated
to with the Shannon Index and species richness.