scholarly journals Change Detection of Salt Affected Land Area of Unnao District in Uttar Pradesh using Remote Sensing and GIS

Author(s):  
Dipali Yadav

Reasons for excess salt in soil are due to both natural and anthropogenic activities. About 955Mha Sodic soil is present in worldwide out of which about 60% is cultivable area. This has huge impact on economic and agricultural production. Salt affected soil is locally called as reh, thur, chopan and kallar. Traditional methods are time consuming and expensive. This can be only fulfilled by using emerging technologies like remote sensing and GIS which are economical and easy in less time. Remote sensing is a technique using to conquer information without in being touch with that object. Remote sensing is very helpful for in temporal changes and spatial changes. So with the help of remote sensing mapping of salt affected area of Unnao district using satellite data of LISS-III of year 2012 and 2018 to calculate spatial changes in between this year and suggest some methods and techniques for reclamation. Analysis shows that Sodic area in Unnao is decreasing and awareness about reclamation of Sodic soil with new techniques is spreading among farmers. In this study, Unnao district has been taken as the study area for mapping and monitoring the change detection with respect to salt affected lands. Salt affected land covers mapped is 14495.63 ha area in 2012 in the district. But in 2018, the total area of salt affected lands has been decreased by 11054.62 ha. The major areas that have been reported having large salt affected land are Auras, Bichhiya & Mianganj Blocks.

Author(s):  
O. S. Olokeogun ◽  
K. Iyiola ◽  
O. F. Iyiola

Mapping of LULC and change detection using remote sensing and GIS techniques is a cost effective method of obtaining a clear understanding of the land cover alteration processes due to land use change and their consequences. This research focused on assessing landscape transformation in Shasha Forest Reserve, over an 18 year period. LANDSAT Satellite imageries (of 30 m resolution) covering the area at two epochs were characterized into five classes (Water Body, Forest Reserve, Built up Area, Vegetation, and Farmland) and classification performs with maximum likelihood algorithm, which resulted in the classes of each land use. <br><br> The result of the comparison of the two classified images showed that vegetation (degraded forest) has increased by 30.96 %, farmland cover increased by 22.82 % and built up area by 3.09 %. Forest reserve however, has decreased significantly by 46.12 % during the period. <br><br> This research highlights the increasing rate of modification of forest ecosystem by anthropogebic activities and the need to apprehend the situation to ensure sustainable forest management.


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