摘要
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 proce- dures. 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 grass- land, 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-l, 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 cor- related to with the Shannon Index and species richness.
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 proce- dures. 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 grass- land, 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-l, 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 cor- related to with the Shannon Index and species richness.
