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Vol. 12, Issue 1 (2023)

Land use land cover mapping using optical data in dry deciduous forest

Author(s):
Suma R and AG Koppad
Abstract:
The study was undertaken in dry deciduous forest part of Mundgod Taluk, Uttara Kannada district using sentinel-2A data. The supervised classification is performed by Maximum likelihood classification. The supervised pixel based image classification was used to classify the data based on ground truth data using ArcGIS Software. The different Classification of land use land cover are viz., very dense forest, moderately dense forest, open forest, water, settlement, horticulture and agricultural and fallow land. The result showed that out of total dry deciduous forest area (29,314.76 ha) higher percent of land was open forest (22.29%), followed by moderate dense forest (17.41%) and very dense forest (4.98%) and non-forest area was to the extent of 21.86%, followed by agriculture and fallow land (20.16%). The Overall accuracy of the classification was 89.24% and kappa coefficient was 0.87. The study indicated that the higher percent of open forest was due to increased anthropogenic pressure on forests, it is necessary to promote plantations in these areas for greater carbon sequestration and Climate change mitigation. LULC maps provides data about the vegetation cover which in-turn help conserve and restoring the forests and in making development, planning, resource management and policy decisions.
Pages: 1415-1419  |  281 Views  122 Downloads


The Pharma Innovation Journal
How to cite this article:
Suma R, AG Koppad. Land use land cover mapping using optical data in dry deciduous forest. Pharma Innovation 2023;12(1):1415-1419.

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