Spatial data modelling for drought assessment by using digital image processing and remote sensing based geospatial techniques
Paper Details
Spatial data modelling for drought assessment by using digital image processing and remote sensing based geospatial techniques
Abstract
The huge belt of Badin in Sindh Pakistan has dry spell like cirumstances. To evaluate and screen the dry season condition for various dry spell files involved the Landsat OLI information for 2014, 2015, 2016, 2017 and 2018. Take pictures from Landsat 8, then stake these pictures subsequent to marking subset these pictures as indicated by the limit. After that transformation to top of climatic brilliance after that ascertain files Normalized Difference Vegetation Index (NDVI), Normalized contrast water record (NDWI), Land Surface Temperature (LST) and Soil Adujstment Vegetation Index (SAVI). In ArcGIS programming raster number cruncher is utilized to compute the surface temperature. Normalized Difference Vegetation Index is utilized to recognize the vegetation or green region over the different timeframe. The standardized distinction record can be utilized to ascertain the pixel region covered by the water surface of the locale of interest. Standardized distinction vegetation record (NDVI) decresed in 2014 to 2017 with the exception of 2015 and 2018. Land surface temperature (LST) incresed in 2014, 2015 and 2017 as contrast with 2016 and 2018. Standardized distinction water file (NDWI) decresed from 2014 to 2016 which prompted dry season.
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Saira Akram, Sumaira Hafeez, Tahira Ishaq, Sajid Rashid Ahmad (2022), Spatial data modelling for drought assessment by using digital image processing and remote sensing based geospatial techniques; JBES, V21, N5, November, P275-281
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