Aavishkar 2020

Abstract:

Land subsidence induced due to soil piping and all other structural changes in the land due to subsidence has become a new threat to Kerala. It is impossible to prevent the occurrence of catastrophic events but through mapping and early detection using ArcGIS, we can easily reduce the impact on the mankind and infrastructure. This paper deals with the detection of soil piping with aid of remote sensing in the land subsidence locations of Thrissur district, mainly focused on the land subsidence that occurred during the 2018-19 monsoon. Creating the zonation map of Thrissur district in ArcGIS helps to understand the most vulnerable zones of Thrissur and thereby helps the effective rescue, relief activities, reducing loss of life, and reducing trauma among people in those regions. By comparing different displacement maps of Thrissur using SNAP desktop software from the European Space Agency (ESA) sentinel-1 and sentinel -2 satellite data we can identify and categorize the spatial distribution of land subsidence and helps to understand the most vulnerable locations of soil piping.

Abstract:

The increase in greenhouse gas concentration, one of which is Methane (CH4), is the main cause of global warming and climate change. Rice is a staple food for almost half of all people. Rice yield improvement has been limited largely by the depletion of soil fertility due to prolonged agricultural activity. Atmospheric methane (CH4) is recognized as one of the most important greenhouse gases and may account for 20 percent of anticipated global warming. Flooded rice fields are a significant source of atmospheric CH4. The emission is the net result of opposing bacterial processes, production in anaerobic microenvironments, and consumption and oxidation in aerobic microenvironments, both of which can be found side by side in flooded paddy field soils. This study presents and discusses validation of rice mapping using classifiers and estimation of methane emission from the study area. The remote sensing of paddy growing areas can not only contribute to the precise mapping of paddy areas but can also contribute to harvest prediction modelling, the analyses of plant diseases, the investigation of erosion-control-adapted agricultural systems, and the assessment of ecosystem services in rice-growing areas.