ANALYZING THE EVOLUTION OF LIBRARY AND INFORMATION SCIENCE PUBLICATIONS AT KHUSHAL KHAN KHATTAK UNIVERSITY, KARAK: PATTERNS, TRENDS, AND CONTRIBUTIONS THROUGH A CONTENT ANALYSIS APPROACH (UP TO 2024)
Abstract
Soil salinization is a very common land degradation process, particularly in arid and semi-arid regions. Under harsh climatic conditions where evaporation is more than precipitation, soluble salts are accumulated in the soil, influencing soil properties with ultimate decline in productivity. In this study soil salinity prediction model is developed based on integrated satellite remote sensing data followed by site observations and geospatial methods. Different spectral indices including Simple Ratio, Normalize Difference Vegetation Index, Soil Adjusted Vegetation Index and Moisture Stress Index were calculated from original bands of Landsat images. Field survey was conducted in Shorkot, Punjab to collect the soil samples and were tested in lab. The results showed that maximum and minimum values of EC was 138.2 and 0.613 dSm-1 respectively. Out of 31 samples 9 samples were found with EC value ≤ 4 and 22 samples represent the saline soils. Combining these satellite based indices and field EC variables into one model yielded the best fit with R2 =0.89. Out of the total area, 9.2% and 18% were identified as moderately and slightly saline, respectively. This shows that a very fine scale spatial variability analysis and modeling of surface soil salinity of large areas is possible using satellite remote sensing data.
Key Words: Electrical conductivity; GIS; Prediction model; Salinity model; Salinity index, KKKUK
