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Water erosion risk mapping using derived parameters from digital elevation model and remotely sensed data

Geo-Spatial Information Science 2012年第3期15卷 157-169页

作者： Soufiane MAIMOUNI abderrazak EL-HARTI abderrazak bannari El-Mostafa BACHAOUI Remote Sensing and Geographical Information Systems Laboratory Faculty of Science and TechniquesUniversity of Sultan Moulay SlimaneBeniMellalMorocco Remote Sensing and Geomatics of the Environment Laboratory Department of GeographyUniversity of OttawaOntarioCanada

The aim of this study is to map the areas exposed to water erosion risks in the High Atlas Mountains of Morocco around the Hassan-I *** methodology is based on the analysis of the water power index(WPI)as a hydrological parameter,the vegetation cover,and the litho-logical *** WPI was derived from a Digital Elevation Model(DEM)and the litho-logical units and vegetation cover were derived from Advanced Land Imager sensor on the Earth Observing-1 satellite *** image was corrected from radiometric and atmospheric effects,and geometrically rectified using a DEM and grounds control *** variables were integrated in a Geographical Information Systems environment,and Multi-Criteria Analyses were used to derive the water erosion risks map pointing out the most exposed areas requiring the implementation of suitable conservation *** validation of the obtained results shows the simplicity and the potential of this approach for water erosion risks mapping.

关键词： water erosion risks GIS remote sensing vegetation cover litho-logical units hydrological parameter

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PALSAR-FBS L-HH Mode and Landsat-TM Data Fusion for Geological Mapping

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Advances in Remote Sensing 2016年第4期5卷 246-268页

作者： abderrazak bannari Ali El-Battay Ali Saquaque Abdelhafid Miri Department of Geoinformatics Arabian Gulf University Manama Kingdom of Bahrain Managem Mineral ONA Group Twin Center Casablanca Kingdom of Morocco

Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.

关键词： Geology Mineral Exploration Lineaments Extraction Data Fusion Intensity-Hue-Saturation Mapping Landsat-TM PALSAR-FBS L-HH Polarization ASTER DEM

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Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

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Advances in Remote Sensing 2017年第4期6卷 260-291页

作者： abderrazak bannari Ali El-Battay Nadir Hameid Fadia Tashtoush Department of Geoinformatics College of Graduate Studies Arabian Gulf University Manama Kingdom of Bahrain Department of Natural Resources and Environment Arabian Gulf University Manama Kingdom of Bahrain

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The labor

关键词： Soil Salinity Remote Sensing Landsat-OLI GIS Semi-Empirical Model Salinity Spectral Indices Topographic Attributes Arid Environment

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