ASSESSING VEGETATION STRUCTURAL CHANGES IN OASIS AGRO-ECOSYSTEMS USING SENTINEL-2 IMAGE TIME SERIES: CASE STUDY FOR DRÂA-TAFILALET REGION MOROCCO

Nowadays, Moroccan oasis agro-ecosystems are under intense effect of natural and anthropogenic factors. Therefore, this essay proposes to use Leaf Area Index (LAI) to assess the consequences of the oases long-term biodegradation. The index was used as a widely-applied parameter of vegetation structure and an important indicator of plant growth and health. Therefore, a new optical multispectral Sentinel-2 data were used to build a long term LAI time series for the area of the Erfoud and Rissani oases, Errachidia province in Drâa-Tafilalet region in Morocco. Nine images of LAI spatial distribution over the study area were obtained by means of SNAP Biophysical Processor over the period since July 2015 till May 2018. Time series analysis of the resultedmaps has revealed a stable trend towards the average LAI decreasing and vegetation structure simplification as a consequence

Landslide Susceptibility Mapping Using Gis-Based Weight-of- Evidence Modelling in Central Georgian Regions

This paper explains the procedure for the generation of a landslide susceptibility map at regional level in Georgia. At the first place, this research presents the results of the weight-of-evidence model applied to estimate the probability of landslides manifestation. A spatial database, including causative factors associated with landslides was constructed from geological maps and satellite data products. The factors that influence landslide occurrence, such as terrain slope, aspect, curvature, elevation, flow accumulation and distance from drainages were calculated from a Sentinel-1 digital terrain elevation data (DTED). Lithology is derived from the Georgia’s geological map. Vegetation cover map is retrieved from Sentinel-2 multispectral satellite imagery

Air pollution mapping with nitrogen and sulfur dioxides in the south-eastern part of Ukraine using satellite data

Atmospheric pollution in Ukraine has become a significant environmental problem, especially in the eastern part where heavy industries are located, and it is particularly severe in industrial centers such as; Zaporizhia, Kryvyi Rih, Dnipropetrovs’k and Dniprodzerzhyns’k. The main emission sources are ferrous metallurgical plants and the coal industry. The purpose of this project is to estimate the degree of pollution from dioxides of nitrogen and sulfur in the south-eastern part of Ukraine using satellite data. An assessment of atmospheric pollution by NO2 is carried out using the data from satellite spectrometer EOS/OMI, and information products Level 3 from Goddard Earth Sciences Data, (GES DISC) NASA for 2009-2014. According to the results study, the largest area of propagation of SO2 aerosol was observed in the industrial agglomerations of Kryvyi Rih, Dnipropetrovs’k and Vilnohirs’k.However, a somewhat smaller content of NO2 in the air recorded near the town of Kryvyi Rih and the cities of Vilnohirs’k and Zaporizhia.The results obtained from this research will aid the creation of awareness among Ukraine’s policy makers about the need for air pollution abatement, and also serve as a stepping stone towards addressing the negative impacts of acid rains

REMOTE SENSING TECHNOLOGIES AND GEOSPATIAL MODELLING HIERARCHY FOR SMART CITY SUPPORT

The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester – Graham system dynamics model is provided over Kiev urban area