SDG 13.3.4: Informing and supporting the local or regional government in local climate change disaster/risk early warning and monitoring

Research has been carried out with students that have informed the government on early warning and monitoring of climate change disasters, for example, Mahdi Saleh earned BE and ME degrees in computer and communication engineering from the Islamic University of Lebanon (IUL) in 2012 and 2015. During his master’s studies, he worked with the National Center for Remote Sensing to develop an automated system for measuring snow-covered regions using satellite-based remote sensing products. 

His research focused on electrical sensing and tomographic imaging techniques. For the past few years, he has been working on developing sensing systems to measure the thickness of oil films floating on the water surface during oil spills. This project, funded by the US Department of the Interior, aims to enhance the efficiency of current oil cleanup methods. The results of the project have been published in journal articles and several conference papers. In addition, a US patent application describing the sensor was published in 2019. They are now using the technology to enhance sensors measuring moisture. They received the top award in IFI’s 2018 Climate Change Student Competition for this project.

The University has also contributed different investigations to determine the risks that exist in Lebanon, climate risk problems in Lebanon, including the potential impact of climate change on agricultural production, tourism, coastal resources and water.

The University has also contributed different investigations to determine the risks that exist in Lebanon, climate risk problems in Lebanon, including the potential impact of climate change on agricultural production, tourism, coastal resources and water. 

Research is being done to prevent climate change disasters, for example. 

Title: Implementation of an automated snow monitoring system using MODIS products in Lebanon 

Snow Cover Area monitoring is an important factor in studies of global climate change, regional water balance and soil moisture. Recently, the usage of remote sensing techniques has flourished. In fact, remote sensing data provides timely adequate snow cover information for large areas. While the National Center for Remote Sensing in Lebanon (CNRS) has recently established an operational monitoring room for natural resources and natural disasters, this paper presents the implementation of a fully automated snow cover monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The system uses snow products from EOS Terra, and Aqua satellites to monitor the Snow Cover of Lebanon during the snow season (i.e. November-April). The importance of this project lies in its daily and fully automated process of acquiring, processing, storing, and displaying statistics of the snow covered areas in Lebanon. Applying a custom algorithm based on combining Terra and Aqua snow products will reduce cloud contamination.