5 August 2013 Mapping and predicting sinkholes by integration of remote sensing and spectroscopy methods
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Proceedings Volume 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013); 87951P (2013) https://doi.org/10.1117/12.2028317
Event: First International Conference on Remote Sensing and Geoinformation of Environment, 2013, Paphos, Cyprus
Abstract
The Dead Sea coastal area is exposed to the destructive process of sinkhole collapse. The increase in sinkhole activity in the last two decades has been substantial, resulting from the continuous decrease in the Dead Sea's level, with more than 1,000 sinkholes developing as a result of upper layer collapse. Large sinkholes can reach 25 m in diameter. They are concentrated mainly in clusters in several dozens of sites with different characteristics. In this research, methods for mapping, monitoring and predicting sinkholes were developed using active and passive remote-sensing methods: field spectrometer, geophysical ground penetration radar (GPR) and a frequency domain electromagnetic instrument (FDEM). The research was conducted in three stages: 1) literature review and data collection; 2) mapping regions abundant with sinkholes in various stages and regions vulnerable to sinkholes; 3) analyzing the data and translating it into cognitive and accessible scientific information. Field spectrometry enabled a comparison between the spectral signatures of soil samples collected near active or progressing sinkholes, and those collected in regions with no visual sign of sinkhole occurrence. FDEM and GPR investigations showed that electrical conductivity and soil moisture are higher in regions affected by sinkholes. Measurements taken at different time points over several seasons allowed monitoring the progress of an 'embryonic' sinkhole.
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N. Goldshleger, U. Basson, I. Azaria, "Mapping and predicting sinkholes by integration of remote sensing and spectroscopy methods", Proc. SPIE 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), 87951P (5 August 2013); doi: 10.1117/12.2028317; https://doi.org/10.1117/12.2028317
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