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Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing
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Author(s): Nirmal Kumar (National Bureau of Soil Survey and Land Use Planning, India), S. K. Singh (National Bureau of Soil Survey and Land Use Planning, India), G. P. Obi Reddy (National Bureau of Soil Survey and Land Use Planning, India)and R. K. Naitam (National Bureau of Soil Survey and Land Use Planning, India)
Copyright: 2019
Pages: 24
Source title:
Interdisciplinary Approaches to Information Systems and Software Engineering
Source Author(s)/Editor(s): Alok Bhushan Mukherjee (North-Eastern Hill University Shillong, India)and Akhouri Pramod Krishna (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7784-3.ch010
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Abstract
A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of differentiating salt affected soils from other features were identified as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-affected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found affected with salinity/alkalinity. The spatial distribution of salt-affected soils in the district showed an association of shallow ground water depth with salinity.
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