Assessment of Land Erosion and Sediment Accumulation Caused by Runoff after a Flash-Flooding Storm Using Topographic Profiles and Spectral Indices

Bannari, A.; Kadhem, G.; El-Battay, A.; Hameid, N.; Rouai, M.

doi:10.4236/ars.2016.54024

Cited by 23 publications

(19 citation statements)

References 70 publications

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“…The sodium-rich soils have a pH of more than 8.2 with a preponderance of sodium carbonate and bicarbonate. This results in sodium saturation of the clay fraction, and leads to a compact and impermeable soil, which prevents aeration and penetration of water necessary for normal plant growth [7,8]. The watershed of Oued El Abid has a moderate rate of soil erosion, which is related to soil salinity [9].…”

Section: Introductionmentioning

confidence: 99%

Modelling soil salinity in Oued El Abid watershed, Morocco

et al. 2018

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Abstract. Soil salinisation is a phenomenon considered to be a real threat to natural resources in semi-arid climates. The phenomenon is controlled by soil (texture, depth, slope etc.), anthropogenic factors (drainage system, irrigation, crops types, etc.), and climate factors. This study was conducted in the watershed of Oued El Abid in the region of Beni Mellal-Khenifra, aimed at localising saline soil using remote sensing and a regression model. The spectral indices were extracted from Landsat imagery (30 m resolution). A linear correlation of electrical conductivity, which was calculated based on soil samples (ECs), and the values extracted based on spectral bands showed a high accuracy with an R 2 (Root square) of 0.80. This study proposes a new spectral salinity index using Landsat bands B1 and B4. This hydro-chemical and statistical study, based on a yearlong survey, showed a moderate amount of salinity, which threatens dam water quality. The results present an improved ability to use remote sensing and regression model integration to detect soil salinity with high accuracy and low cost, and permit intervention at an early stage of salinisation.

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Section: Introductionmentioning

confidence: 99%

Modelling soil salinity in Oued El Abid watershed, Morocco

et al. 2018

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“…Using spectral processing to acquire indices necessitates the analysis of the spectral characteristics of remote sensing imagery to determine their quantitative relationship with wetness/water and dryness in land changes. Albedo, normalized difference water index (NDWI), normalized multi-band drought index (NMDI), and normalized difference moisture index (NDMI) are the principal spectral indices used to characterize moisture in land changes [3,4,33]. Hydrological processing involves acquiring indices to analyze the hydrological characteristics of a digital elevation model (DEM) to detect areas associated with flash floods and erosion caused by rainstorms and sediment transport and accumulation [4,25].…”

Section: Methodsmentioning

confidence: 99%

A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

et al. 2019

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This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

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“…Rocks and soils can be characterized by intensity (I), hue (H) and saturation (S) according to their mineralogy and colour [15] [61] [62] [63]. These three variables, derived by transformation of the visible bands, summarizes the absorption bands information to maps potential alteration areas and lithological formations based upon the predicted relationships between the image color and mineral absorption features [64] [65].…”

Section: Data Fusion Techniquementioning

confidence: 99%

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

Bannari¹,

El-Battay²,

Saquaque³

et al. 2016

ARS

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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.

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Assessment of Land Erosion and Sediment Accumulation Caused by Runoff after a Flash-Flooding Storm Using Topographic Profiles and Spectral Indices

Cited by 23 publications

References 70 publications

Modelling soil salinity in Oued El Abid watershed, Morocco

Modelling soil salinity in Oued El Abid watershed, Morocco

A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco

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

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