Suchergebnisse

A particular challenge for undertaking urbanization mapping of Beirut is the absence of a unified understanding of the city. Migration, informal settlements, a lack of urban planning, political corruption, as well as internal conflict have made this task even harder. The population in Lebanon is unevenly distributed among regions, where one third of the population resides in the Greater Beirut Area (GBA), whereas it occupies only 233 km2 (2% of Lebanon's total area). The Greater Beirut Area is subject to pressures arising from population growth and economic expansion. This study aims to follow the evolution of urbanization from 1963 till 2005 by processing and interpreting topographical maps and satellite images acquired by different space platforms. Satellite imagery change analysis shows that average annual urban growth surpassed 1.8 km2∙yr−1. Actually, a variety of factors triggers urban growth in the GBA (i.e., transportation, public policies, economic activities and environmental variables). The logistic regression method has been applied to model future urban growth in the region of Greater Beirut. Consequently, an urban growth scenario map has been generated. To validate our results, we compared an urban map derived from RapidEye satellite acquired in 2010 to our model's outcome of the same year. The output shows a satisfactory rate of success (~61%). This research aims to provide policy makers and urban planners in Lebanon an essential decision tool to support upcoming urban planning in this study area or in others major cities in Lebanon.

Sulfate aerosol particles, whatever natural or anthropogenic induced, impact the radiative forcing of the earth. With a major increase in the Middle-Eastern population, tracking the sulfate particle is critical to guide several air pollution reduction initiatives in the concerning countries. In this context, this study detects and assess sulfates over twelve countries of the Middle-East during sixteen years. The Aerosol Optical Depth (AOD), which is a dimensionless measure of the climate forcing generated by aerosols, in the atmosphere has been calculated from MODIS aerosol products. These datasets allow global and daily monitoring of atmospheric content at a spatial resolution of 10 km. Results show a high concentration of sulfate aerosol in the Persian Gulf mainly related to active volcanoes activities. Also, several anthropogenic sources (e.g. civil and military aviation, gunpowder and live artillery usage, etc.) has been noted.

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal&mdash ; End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± ; 1309 ha in 2016 to 7605 ± ; 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal—End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government ...