Multispectral and hyperspectral sensor data of the bio-optical parameters with a high spatial resolution are important for monitoring and mapping of the coastal ecosystems and estuarine areas, such as the Kneiss Islands in the Gulf of Gabes. Sentinel 2 S2A and Hyperion Earth observing-1 (EO1) imaging sensors reflectance data have been used for water quality determination and mapping of turbidity TU and chlorophyll Chl-a in shallow waters. First, we applied a tidal swing area mask based on uncorrelated pixel via 2D scatter plot between 665 nm and 865 nm to eliminate the overestimation of the concentration of water quality parameters due to the effect of the bottom reflection. The processing for mapping and validating Chl-a, Turbidity S2A, and EO1 were performed using a relation between reflectance bands and in situ measurements. Therefore, we were able to validate the performance of the case 2 regional coast colour processor (C2RCC) as well as our region-adapted empirical optical remote sensing algorithms. Turbidity was mapped based on the reflectance of 550 nm band for EO1 (R 2 = 0.63) and 665 nm band for S2A (R 2 = 0.70). Chlorophyll was mapped based on (457/528 nm) reflectance ratio (R 2 = 0.57) for EO1 and (705/665 nm) reflectance ratio (R 2 = 0.72) for the S2A.
The aim of this study is to produce a landslide susceptibility map in Mogods and Hedil using the fuzzy logic method. To increase the objectivity of the approach, the fuzzy membership was calculated using the frequency ratio (FR). Nine factors were considered for landslide control, including slope, aspect, plan curvature, profil curvature, distance from faults, distance from rivers, land use, precipitation, and lithology. The frequency ratio was used to calculate the fuzziness of each factor, and these results were then applied to the fuzzy operators to produce the landslide susceptibility map. The selection of the susceptibility map closest to reality was based on the spatial distribution of landslides in each susceptibility class of each fuzzy operator and on the application of the receiver operating curve (ROC). The results of the Area Under Curve (AUC) analysis show that the gamma operator (0.90) provided the most accurate prediction of the landslide susceptibility map, as indicated by the prediction accuracy of the model (0.766). The study area was classified into four classes using Jenks natural fracture classification method: low susceptibility zone, moderate susceptibility zone, high susceptibility zone and very high susceptibility zone. The use of the fuzzy gamma operator for landslide susceptibility mapping gave a very satisfactory result with a reliability rate of 76.6%.
An elevated human presence due to the involvement of the coastal oases of Tunisia in the global petrochemical industry and population pressure in the 1970s has resulted in major changes in the oases’ agro–ecosystem environment. The consequences of this have been urbanisation and rural exodus, priority to the industrial sectors and services at the expense of agriculture, high mobility and rise of trade. The coastal oases of Gabes located in the South-East of Tunisia are considered in this study. This has been affected by sharp degradation, mainly of anthropogenic origins such as demographic growth, extension of the urban areas and creation of a highly contaminating chemical zone amplifying their environmental vulnerability. Satellite data is an essential tool in the study and mapping of these types of environment and for that, we started with the mapping of the vegetative land use using the vegetation indices derived from the hyperspectral scene of the Hyperion sensor (25 April 2010) and field data. This has allowed us to better characterise the most vulnerable areas and to identify the socio–environmental risks. The analysis of the radiometric indices leads to the definition of the spatial extension of vegetation cover in the oases. This study has permitted us to outline the oases’ typologies in Gabes and to discuss their dynamics in the short term.
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