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Plant Richness Modelling in South Gujarat Using Remote Sensing and Geographic Information System


     

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The paper presents a geospatial modelling approach for the assessment of plant richness in south Gujarat using a two-tier approach i.e., satellite image (Resourcesat-1) for vegetation type/land use mapping, landscape analysis, and the plant richness modelling on 1:50,000 scale. The study showed that nine vegetation types viz., teak mixed dry and moist deciduous forest, mangrove forest, mangrove scrub, riverain forest, ravine thorn forest, forest plantation, degraded forest and Prosopis juliflora scrub respectively. The largest area is occupied by teak mixed dry deciduous forest by 14.98 per cent. The overall accuracy was found to be 87.78 per cent. The plant richness map, generated using SPLAM software, showed three levels of plant richness. The vegetation type-wise plant richness assessments were also calculated. It was observed that 55.97 per cent of forests area had under high plant richness, rest of the area falls under low, medium and very high categories. The district-wise plant richness was also calculated.

Keywords

Plant Richness Modeling, Remotesensing, Landscape, Vegetation Type/land Use.
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G. D. Bhatt

S. P. S. Kushwaha

S. Nandy

Kiran Bargali

D. Tadvi

P. S. Nagar

M. Daniel


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  • Plant Richness Modelling in South Gujarat Using Remote Sensing and Geographic Information System

Abstract Views: 414  |  PDF Views: 12

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Abstract


The paper presents a geospatial modelling approach for the assessment of plant richness in south Gujarat using a two-tier approach i.e., satellite image (Resourcesat-1) for vegetation type/land use mapping, landscape analysis, and the plant richness modelling on 1:50,000 scale. The study showed that nine vegetation types viz., teak mixed dry and moist deciduous forest, mangrove forest, mangrove scrub, riverain forest, ravine thorn forest, forest plantation, degraded forest and Prosopis juliflora scrub respectively. The largest area is occupied by teak mixed dry deciduous forest by 14.98 per cent. The overall accuracy was found to be 87.78 per cent. The plant richness map, generated using SPLAM software, showed three levels of plant richness. The vegetation type-wise plant richness assessments were also calculated. It was observed that 55.97 per cent of forests area had under high plant richness, rest of the area falls under low, medium and very high categories. The district-wise plant richness was also calculated.

Keywords


Plant Richness Modeling, Remotesensing, Landscape, Vegetation Type/land Use.