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Dr. Rachid Lhissou
INRS-Centre Eau-Terre-Environnement

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0 Digital Mapping
0 Remote Sensing
0 Soil Mapping
0 Radar and Satellite Remote Sensing
0 Geomatics and GIS

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Remote Sensing

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Journal article
Published: 17 March 2021 in Remote Sensing
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Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.

ACS Style

Anas El-Alem; Karem Chokmani; Aarthi Venkatesan; Lhissou Rachid; Hachem Agili; Jean-Pierre Dedieu. How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies? Remote Sensing 2021, 13, 1134 .

AMA Style

Anas El-Alem, Karem Chokmani, Aarthi Venkatesan, Lhissou Rachid, Hachem Agili, Jean-Pierre Dedieu. How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies? Remote Sensing. 2021; 13 (6):1134.

Chicago/Turabian Style

Anas El-Alem; Karem Chokmani; Aarthi Venkatesan; Lhissou Rachid; Hachem Agili; Jean-Pierre Dedieu. 2021. "How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?" Remote Sensing 13, no. 6: 1134.

Journal article
Published: 04 February 2021 in International Journal on Advanced Science, Engineering and Information Technology
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Forest ecosystems are exposed increasingly to a variety of human activities and accentuated by climate change. With its Mediterranean climate, Northern Morocco is very hot, which exposes forests to widespread fires. This work aims at the delineation of wildfires and the spectral characterization of burnt vegetation as well as the characterization of the fire severity in the North of Morocco by using Landsat-8, Sentinel-2 spectral data, and topographic data. The methods used include the derivation of wildfires spectral indices and the computation of topographic parameters (elevation, slope, exposure) from SRTM and PALSAR digital elevation models. Then, the Spectral Angle Mapper (SAM) classification was used to map forest fires' severity. Furthermore, we have compared the severity classes obtained from the SAM method applied to Landsat 8 and Sentinel 2 data, with different spectral indices specialized in detecting wildfires, on the one hand, and topographic data, on the other hand. Results showed that MIRBI and NBR indices allow a better characterization of burned areas than BAI index. For its part, SAM classification provides a fair characterization of the severity classes of burnt forests. It has also been shown that the MIRBI index and sun exposure are strongly correlated with severity classes. The obtained maps show the spatial heterogeneity of burns severity and how they interact with topography. These maps may help land resource managers and fire officials predict areas of potential fire hazards and study vegetation regrowth areas after fires.

ACS Style

Issam Eddine Zidane; Rachid Lhissou; Maryem Ismaili; Yassine Manyari; Abdelali Bouli; Mustapha Mabrouki. Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images. International Journal on Advanced Science, Engineering and Information Technology 2021, 11, 72 -83.

AMA Style

Issam Eddine Zidane, Rachid Lhissou, Maryem Ismaili, Yassine Manyari, Abdelali Bouli, Mustapha Mabrouki. Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images. International Journal on Advanced Science, Engineering and Information Technology. 2021; 11 (1):72-83.

Chicago/Turabian Style

Issam Eddine Zidane; Rachid Lhissou; Maryem Ismaili; Yassine Manyari; Abdelali Bouli; Mustapha Mabrouki. 2021. "Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images." International Journal on Advanced Science, Engineering and Information Technology 11, no. 1: 72-83.

Journal article
Published: 03 October 2020 in Remote Sensing Applications: Society and Environment
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This paper aims to assess the image-based and physical atmospheric corrections of Landsat 8 images for geological mapping. This study was carried out in the central Jebilet inlier in Moroccan anti atlas, which is characterized by its geological diversity, its mining potential and an arid climate. Two physical atmospheric corrections FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) and ATCOR (Atmospheric & Topographic Correction) were compared to the DOS1 (Dark Object Subtraction) image-based method. The assessment of the results was based on the ASD (Analytical Spectral Devices) spectroradiometric data as well as the SVM (Support Vector Machine) classifications issued from the atmospherically corrected images. The FLAASH provided the most accurate Bottom Of Atmosphere BOA reflectance estimation (R2 = 0.95 and RMSE = 0.033) and slightly outperformed the DOS1 method (R2 = 0.94 and RMSE = 0.034). For the SVM classification outputs, DOS1 gave results almost similar to those obtained by FLAASH and ATCOR. The similarity between the FLAASH and DOS1 SVM classifications was 97.77%. The results of this work demonstrate the effectiveness of the image-based method in atmospheric correction of multispectral data for geological and mineral mapping over the arid and semi-arid regions. The image-based atmospheric correction method is proved as an accurate, simple way and very straightforward to apply.

ACS Style

Mohcine Chakouri; Rachid Lhissou; Abderrazak El Harti; Soufiane Maimouni; Zakaria Adiri. Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions. Remote Sensing Applications: Society and Environment 2020, 20, 100420 .

AMA Style

Mohcine Chakouri, Rachid Lhissou, Abderrazak El Harti, Soufiane Maimouni, Zakaria Adiri. Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions. Remote Sensing Applications: Society and Environment. 2020; 20 ():100420.

Chicago/Turabian Style

Mohcine Chakouri; Rachid Lhissou; Abderrazak El Harti; Soufiane Maimouni; Zakaria Adiri. 2020. "Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions." Remote Sensing Applications: Society and Environment 20, no. : 100420.

Journal article
Published: 25 August 2020 in International Journal of Advanced Trends in Computer Science and Engineering
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The Central Jebilet Massif is one of the main Palaeozoic outcrops in Morocco. This massif is characterized by its arid climate, its significant mining potential and the absence of plant cover, which favors the use of spatial remote sensing for geological mapping and mineral prospecting in this site. The objective of this study is the comparison of hyperspectral data from the Hyperion sensor of the Earth Observing-1 (EO-1) satellite and multispectral data from the Operational Land Imager (OLI) sensor of Landsat 8 in the discrimination of geological units and detection of iron caps in the study area. The classification by the Support Vector Machine (SVM) method allowed for a good mapping of the lithological units in the study area. The accuracy of the SVM classification of hyperspectral data is higher than that of multispectral data, which was demonstrated by the confusion matrix, notably an overall accuracy of 93.05% and 89.24%, respectively, and a kappa coefficient of 91.25% and 84.36%, respectively. Concerning the iron detection, the band rationing using both sensors have demonstrated a performance of detecting areas that contain more iron ores, especially, the iron caps of Kettara mine, with a small advantage of hyperspectral data. In overall, our results highlight the efficiency of machine learning classifier and hyperspectral data for the detection of iron ores and the discrimination of lithological units in arid regions. The use of hyperspectral and multispectral images has been shown to be a good technique for the characterization of iron deposits and lithological units, which may help in in mineral exploration engineering with reduced fieldwork and geochemistry.

ACS Style

Mohcine Chakouri; Abderrazak El Harti; Rachid Lhissou; Jaouad El Hachimi; Amine Jellouli. Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning. International Journal of Advanced Trends in Computer Science and Engineering 2020, 9, 5772 -5783.

AMA Style

Mohcine Chakouri, Abderrazak El Harti, Rachid Lhissou, Jaouad El Hachimi, Amine Jellouli. Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9 (4):5772-5783.

Chicago/Turabian Style

Mohcine Chakouri; Abderrazak El Harti; Rachid Lhissou; Jaouad El Hachimi; Amine Jellouli. 2020. "Geological and Mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and Machine Learning." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4: 5772-5783.

Review
Published: 12 March 2020 in Cold Regions Science and Technology
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In cold regions, the high occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams. These floods can be critical hydrological and hydraulic events and be a major concern for citizens, authorities, insurance companies and government agencies. In the past twenty years, several studies have been conducted in ice jam modelling and forecasting, and it has been found that predicting ice jam formation and breakup is challenging, due to the complexity of the interactions between the hydroclimatic variables leading to these processes. At this time, several mathematical models have been developed to predict breakup processes. The current methods of breakup prediction are highly empirical and site-specific. The information on the progress of the methods and the variables used to predict the occurrence, severity, and timing of the breakup ice jams still remains limited. This study summarizes the different processes contributing to ice jam formation and breakup, the various existing ice jam prediction models, and their potential and limitations regarding the improvement in ice jam predictions. An overview of the application of artificial neural networks and fuzzy logic systems in ice-related problems is presented. Genetic programming is also explained as a possible mean for ice-related problems. Although genetic programming shows promising results in hydrological modelling, it has not yet been used in ice-related problems. The review of literature highlights that data-driven and machine learning techniques provide promising means in predicting ice jams with better confidence, but more scientific research is needed.

ACS Style

Fatemehalsadat Madaeni; Rachid Lhissou; Karem Chokmani; Sebastien Raymond; Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review. Cold Regions Science and Technology 2020, 174, 103032 .

AMA Style

Fatemehalsadat Madaeni, Rachid Lhissou, Karem Chokmani, Sebastien Raymond, Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review. Cold Regions Science and Technology. 2020; 174 ():103032.

Chicago/Turabian Style

Fatemehalsadat Madaeni; Rachid Lhissou; Karem Chokmani; Sebastien Raymond; Yves Gauthier. 2020. "Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review." Cold Regions Science and Technology 174, no. : 103032.

Review
Published: 15 January 2020 in Ore Geology Reviews
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Thanks to their wide coverage and the valuable spectral information, remote sensing data constitute a popular instrument in the mineral exploration toolbox. In recent times, the remote sensing community has witnessed the launch of the new and improved Landsat-8 and Sentinel-2 multispectral sensors. The former constitutes the eighth sensor of the Landsat series launched by the National Aeronautics and Space Administration (NASA), while the latter is linked to the Sentinel-mission launched by the European Space Agency (ESA). The main objective of our contribution is to provide a comprehensive review of the use of the Landsat-8 and Sentinel-2 multispectral sensors in mineral exploration. The free and open access to these data and their enhanced spectral and spatial characteristics (compared to the existing multispectral sensors) has clearly promoted the use of remotely sensed in mineral exploration. In addition, as illustrated by the case studies presented in this paper, Landsat-8 and Sentinel-2 data present effective and accurate mapping tools for mineral exploration. Both sensors identified iron oxides and Al-OH absorption features, in addition to silicate and carbonate minerals. Our review indicated that Landsat-8 is by far the more popular sensor in mineral exploration applications. Greater uptake of Sentinel-2 and further case studies will be necessary to better demonstrate its capabilities and potential.

ACS Style

Zakaria Adiri; Rachid Lhissou; Abderrazak El Harti; Amine Jellouli; Mohcine Chakouri. Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications. Ore Geology Reviews 2020, 117, 103332 .

AMA Style

Zakaria Adiri, Rachid Lhissou, Abderrazak El Harti, Amine Jellouli, Mohcine Chakouri. Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications. Ore Geology Reviews. 2020; 117 ():103332.

Chicago/Turabian Style

Zakaria Adiri; Rachid Lhissou; Abderrazak El Harti; Amine Jellouli; Mohcine Chakouri. 2020. "Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications." Ore Geology Reviews 117, no. : 103332.

Journal article
Published: 14 May 2019 in Geosciences
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A new method for sensitivity analysis of water depths is presented based on a two-dimensional hydraulic model as a convenient and cost-effective alternative to Monte Carlo simulations. The method involves perturbation of the probability distribution of input variables. A relative sensitivity index is calculated for each variable, using the Gauss quadrature sampling, thus limiting the number of runs of the hydraulic model. The variable-related highest variation of the expected water depths is considered to be the most influential. The proposed method proved particularly efficient, requiring less information to describe model inputs and fewer model executions to calculate the sensitivity index. It was tested over a 45 km long reach of the Richelieu River, Canada. A 2D hydraulic model was used to solve the shallow water equations (SWE). Three input variables were considered: Flow rate, Manning’s coefficient, and topography of a shoal within the considered reach. Four flow scenarios were simulated with discharge rates of 759, 824, 936, and 1113 m 3 / s . The results show that the predicted water depths were most sensitive to the topography of the shoal, whereas the sensitivity indices of Manning’s coefficient and the flow rate were comparatively lower. These results are important for making better hydraulic models, taking into account the sensitivity analysis.

ACS Style

Khalid Oubennaceur; Karem Chokmani; Miroslav Nastev; Yves Gauthier; Jimmy Poulin; Marion Tanguy; Sebastien Raymond; Rachid Lhissou. New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling. Geosciences 2019, 9, 220 .

AMA Style

Khalid Oubennaceur, Karem Chokmani, Miroslav Nastev, Yves Gauthier, Jimmy Poulin, Marion Tanguy, Sebastien Raymond, Rachid Lhissou. New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling. Geosciences. 2019; 9 (5):220.

Chicago/Turabian Style

Khalid Oubennaceur; Karem Chokmani; Miroslav Nastev; Yves Gauthier; Jimmy Poulin; Marion Tanguy; Sebastien Raymond; Rachid Lhissou. 2019. "New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling." Geosciences 9, no. 5: 220.

Journal article
Published: 13 September 2018 in International Journal of Disaster Risk Reduction
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This paper presents a probabilistic approach for flood risk assessment in a reach of the Richelieu River, south of Saint-Jean-Sur-Richelieu, Quebec, Canada. The approach is based on a combination of three simple modules: 1) flood frequency analysis (frequency and peak discharge), 2) estimation of inundation depth, and 3) damage and loss estimation. To assess the flood negative impacts, a simple hydraulic model is developed designed to replace and complement the existing 2D model coupled to an existing damage model. By simplifying the spatial coverage of flood calculations, this approach accelerates the computational efficiency enabling a broader set of elements to be combined in a large sample of Monte Carlo simulations applied in the flood risk assessment. The final result is a local scale risk map indicating the expected annual damage to each individual building in the study area useful for flood risk decision making. The analyses show that the approach is particularly powerful for flood risk assessments in areas adjacent to the river for which sufficient data is available.

ACS Style

Khalid Oubennaceur; Karem Chokmani; Miroslav Nastev; Rachid Lhissou; Anas El Alem. Flood risk mapping for direct damage to residential buildings in Quebec, Canada. International Journal of Disaster Risk Reduction 2018, 33, 44 -54.

AMA Style

Khalid Oubennaceur, Karem Chokmani, Miroslav Nastev, Rachid Lhissou, Anas El Alem. Flood risk mapping for direct damage to residential buildings in Quebec, Canada. International Journal of Disaster Risk Reduction. 2018; 33 ():44-54.

Chicago/Turabian Style

Khalid Oubennaceur; Karem Chokmani; Miroslav Nastev; Rachid Lhissou; Anas El Alem. 2018. "Flood risk mapping for direct damage to residential buildings in Quebec, Canada." International Journal of Disaster Risk Reduction 33, no. : 44-54.

Journal article
Published: 01 August 2018 in Remote Sensing Applications: Society and Environment
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Retrieving crops and their location, as well as their spatial extent, are useful information for agricultural planning and better management of irrigation water resources as well as for crop health monitoring, towards an increased food production and reduced water use. Multispectral remote sensing images with a spatial resolution of 30 m or greater are often used for mapping crops in extensive agricultural systems at global and regional scale. However, that spatial resolution is inadequate for mapping highly fragmented and intensive agricultural landscapes, such as the Tadla Irrigated Perimeter (TIP) in central Morocco. Hence, our study aims to: (1) identify and map major crops in the TIP with improving the spatial resolution of producing maps from 30 m to 15 m; (2) retrieve the area of major cultivations; (3) compare machine learning classifiers namely, Support Vector Machine (SVM), Random Forest (RF) and Spectral Angle Mapper (SAM) as a distance-based classifier. Our methodology is based on the Landsat-8 OLI (Operational Land Imager) data pan-sharpened to 15 m. SAM, RF and SVM classifiers were used and compared for retrieving crops from a multitemporal dataset of the Normalized Difference Vegetation Index (NDVI) for 10 periods during the agricultural season. The RF, SVM and SAM have classified the major crops with overall accuracies of 89.26, 85.27% and 57.17% respectively, and kappa coefficient of 85, 80% and 43%, respectively, noting that sugar beet, tree crops and cereals are delineated accurately while alfalfa is not. This study showed a high performance by using time-series pan-sharpened OLI NDVI data coupled with machine learning classifiers for mapping different crops in irrigated, very fragmented and heterogeneous agricultural landscape.

ACS Style

Jamal-Eddine Ouzemou; Abderrazak El Harti; Rachid Lhissou; Ali El Moujahid; Naima Bouch; Rabii El Ouazzani; El Mostafa Bachaoui; Abderrahmene El Ghmari. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Applications: Society and Environment 2018, 11, 94 -103.

AMA Style

Jamal-Eddine Ouzemou, Abderrazak El Harti, Rachid Lhissou, Ali El Moujahid, Naima Bouch, Rabii El Ouazzani, El Mostafa Bachaoui, Abderrahmene El Ghmari. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Applications: Society and Environment. 2018; 11 ():94-103.

Chicago/Turabian Style

Jamal-Eddine Ouzemou; Abderrazak El Harti; Rachid Lhissou; Ali El Moujahid; Naima Bouch; Rabii El Ouazzani; El Mostafa Bachaoui; Abderrahmene El Ghmari. 2018. "Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system." Remote Sensing Applications: Society and Environment 11, no. : 94-103.

Journal article
Published: 23 July 2018 in Science of The Total Environment
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Riparian strips are used worldwide to protect riverbanks and water quality in agricultural zones because of their numerous environmental benefits. A metric called Riparian Strip Quality Index, which is based on the percentage area of riparian vegetation, is used to evaluate their ecological condition. This index measures the potential capacity of riparian strips to filter sediments, retain pollutants, and provide shelter for terrestrial and aquatic species. This research aims to improve this metric by integrating the ability of riparian strips to intercept surface runoff, which is the major cause of water pollution and erosion in productive areas. In Canada and the Nordic countries, rapid surface drainage from snow melt and spring rains is often practiced to avoid production delays and losses. This reduces the efficiency of riparian buffer strips by promoting soil erosion due to concentrated runoff. A new proposed metric called Riparian Strip Efficiency Index (RSEI), incorporates not only land cover information, but topographic and hydrologic variables to model the intensity and spatial distribution of runoff streamflow, and the capability of riparian strips to retain sediments and pollutants. The research is performed over the La Chevrotière River Basin in the Portneuf municipality in Québec (Canada) using hydrological modeling, land cover and topographic data extracted from very high spatial resolution WorldView-2 imagery as a unique source of inputs. The results show that RSEI provides a better characterization of the ecosystem services of riparian strips in terms of pollutants filtration and prevention of soil erosion in agricultural areas. RSEI will allow a better management of agricultural practices such as drainage and land leveling. Further, it will provide to land managers information to monitor environmental changes and to prioritize intervention areas, which ultimately targets to ensure optimal allocation of private or public funds toward the most inefficient and threatened riparian strips.

ACS Style

Julio Novoa; Karem Chokmani; Rachid Lhissou. A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery. Science of The Total Environment 2018, 644, 1439 -1451.

AMA Style

Julio Novoa, Karem Chokmani, Rachid Lhissou. A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery. Science of The Total Environment. 2018; 644 ():1439-1451.

Chicago/Turabian Style

Julio Novoa; Karem Chokmani; Rachid Lhissou. 2018. "A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery." Science of The Total Environment 644, no. : 1439-1451.

Original paper
Published: 14 May 2018 in Journal of Forestry Research
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The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.

ACS Style

Issameddine Zidane; Rachid Lhissou; Abdelali Bouli; Mustapha Mabrouki. An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco. Journal of Forestry Research 2018, 30, 981 -992.

AMA Style

Issameddine Zidane, Rachid Lhissou, Abdelali Bouli, Mustapha Mabrouki. An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco. Journal of Forestry Research. 2018; 30 (3):981-992.

Chicago/Turabian Style

Issameddine Zidane; Rachid Lhissou; Abdelali Bouli; Mustapha Mabrouki. 2018. "An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco." Journal of Forestry Research 30, no. 3: 981-992.

Journal article
Published: 01 December 2017 in Advances in Space Research
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Certainly, lineament mapping occupies an important place in several studies, including geology, hydrogeology and topography etc. With the help of remote sensing techniques, lineaments can be better identified due to strong advances in used data and methods. This allowed exceeding the usual classical procedures and achieving more precise results. The aim of this work is the comparison of ASTER, Landsat-8 and Sentinel 1 data sensors in automatic lineament extraction. In addition to image data, the followed approach includes the use of the pre-existing geological map, the Digital Elevation Model (DEM) as well as the ground truth. Through a fully automatic approach consisting of a combination of edge detection algorithm and line-linking algorithm, we have found the optimal parameters for automatic lineament extraction in the study area. Thereafter, the comparison and the validation of the obtained results showed that the Sentinel 1 data are more efficient in restitution of lineaments. This indicates the performance of the radar data compared to those optical in this kind of study.

ACS Style

Zakaria Adiri; Abderrazak El Harti; Amine Jellouli; Rachid Lhissou; Lhou Maacha; Mohamed Azmi; Mohamed Zouhair; El Mostafa Bachaoui. Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas. Advances in Space Research 2017, 60, 2355 -2367.

AMA Style

Zakaria Adiri, Abderrazak El Harti, Amine Jellouli, Rachid Lhissou, Lhou Maacha, Mohamed Azmi, Mohamed Zouhair, El Mostafa Bachaoui. Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas. Advances in Space Research. 2017; 60 (11):2355-2367.

Chicago/Turabian Style

Zakaria Adiri; Abderrazak El Harti; Amine Jellouli; Rachid Lhissou; Lhou Maacha; Mohamed Azmi; Mohamed Zouhair; El Mostafa Bachaoui. 2017. "Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas." Advances in Space Research 60, no. 11: 2355-2367.

Journal article
Published: 01 August 2016 in International Journal of Applied Earth Observation and Geoinformation
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Soil salinization is major environmental issue in irrigated agricultural production. Conventional methods for salinization monitoring are time and money consuming and limited by the high spatiotemporal variability of this phenomenon. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Tadla plain in central Morocco using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. Six Landsat TM/OLI satellite images acquired during 13 years period (2000–2013) coupled with in-situ electrical conductivity (EC) measurements were used to develop the proposed method. After radiometric and atmospheric correction of TM/OLI images, a new soil salinity index (OLI-SI) is proposed for soil EC estimation. Validation shows that this index allowed a satisfactory EC estimation in the Tadla irrigated perimeter with coefficient of determination R2 varying from 0.55 to 0.77 and a Root Mean Square Error (RMSE) ranging between 1.02 dS/m and 2.35 dS/m. The times-series of salinity maps produced over the Tadla plain using the proposed method show that salinity is decreasing in intensity and progressively increasing in spatial extent, over the 2000–2013 period. This trend resulted in a decrease in agricultural activities in the southwestern part of the perimeter, located in the hydraulic downstream.

ACS Style

Abderrazak El Harti; Rachid Lhissou; Karem Chokmani; Jamal-Eddine Ouzemou; Mohamed Hassouna; El Mostafa Bachaoui; Abderrahmene El Ghmari. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation 2016, 50, 64 -73.

AMA Style

Abderrazak El Harti, Rachid Lhissou, Karem Chokmani, Jamal-Eddine Ouzemou, Mohamed Hassouna, El Mostafa Bachaoui, Abderrahmene El Ghmari. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation. 2016; 50 ():64-73.

Chicago/Turabian Style

Abderrazak El Harti; Rachid Lhissou; Karem Chokmani; Jamal-Eddine Ouzemou; Mohamed Hassouna; El Mostafa Bachaoui; Abderrahmene El Ghmari. 2016. "Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices." International Journal of Applied Earth Observation and Geoinformation 50, no. : 64-73.

Journal article
Published: 23 October 2015 in Agroforestry Systems
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Agroforestry has emerged as a pertinent answer to the challenges of modern agriculture. Agroforestry landscapes characterization allows to conduct a management policy of rural and mountain landscapes and to produce economical, environmental and social benefits. Due to its geographical location and its natural potential, the rural Agoudi N’Lkhir Municipality has very rich and diversified agroforestry assets: arboriculture and considerable forestry resources. However, these agroforestry landscapes are subject to accelerated degradation due to anthropogenic and natural factors are causing the weakening of the natural environment. The purpose of our study is to determine the agroforestry landscapes characteristics of Agoudi N’Lkhir rural municipality, the dynamics of these agroforestry landscapes and responsible partners for the management of these landscapes. The methodology of this study uses the synergy between several techniques: geographical information system (GIS), field survey, processing and analyzing remote sensing images data, and finally the use of landscaped and agroforestry diagnosis. The results showed that the forest of the municipality decreased strongly in the order of 36 % in 35 years (1973–2008). The most of agriculture and livestock are traditional (Bour) and should undergo an appropriate development. The paper highlights policy and efforts to do for developing the rural mountainous agroforestry landscapes of Agoudi N’Lkhir Municipality and identifies entry points for success agroforestry adoption. To improve the current situation of agroforestry system, the different managers of this space must meet, discuss and consult with all stakeholders to launch together, in a perspective of sustainable development and management actions of this territory.

ACS Style

Hicham Bouzekraoui; Yahia EL Khalki; A. Mouaddine; Rachid Lhissou; M. El Youssi; A. Barakat. Characterization and dynamics of agroforestry landscape using geospatial techniques and field survey: a case study in central High-Atlas (Morocco). Agroforestry Systems 2015, 90, 965 -978.

AMA Style

Hicham Bouzekraoui, Yahia EL Khalki, A. Mouaddine, Rachid Lhissou, M. El Youssi, A. Barakat. Characterization and dynamics of agroforestry landscape using geospatial techniques and field survey: a case study in central High-Atlas (Morocco). Agroforestry Systems. 2015; 90 (6):965-978.

Chicago/Turabian Style

Hicham Bouzekraoui; Yahia EL Khalki; A. Mouaddine; Rachid Lhissou; M. El Youssi; A. Barakat. 2015. "Characterization and dynamics of agroforestry landscape using geospatial techniques and field survey: a case study in central High-Atlas (Morocco)." Agroforestry Systems 90, no. 6: 965-978.

Conference paper
Published: 14 October 2015 in Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX
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ACS Style

Jamal-Eddine Ouzemou; Abderrazak El Harti; Ali El Moujahid; Naima Bouch; Rabii El Ouazzani; Rachid Lhissou; El Mostafa Bachaoui. Mapping crop based on phenological characteristics using time-series NDVI of operational land imager data in Tadla irrigated perimeter, Morocco. Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX 2015, 96372G -96372G-7.

AMA Style

Jamal-Eddine Ouzemou, Abderrazak El Harti, Ali El Moujahid, Naima Bouch, Rabii El Ouazzani, Rachid Lhissou, El Mostafa Bachaoui. Mapping crop based on phenological characteristics using time-series NDVI of operational land imager data in Tadla irrigated perimeter, Morocco. Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX. 2015; ():96372G-96372G-7.

Chicago/Turabian Style

Jamal-Eddine Ouzemou; Abderrazak El Harti; Ali El Moujahid; Naima Bouch; Rabii El Ouazzani; Rachid Lhissou; El Mostafa Bachaoui. 2015. "Mapping crop based on phenological characteristics using time-series NDVI of operational land imager data in Tadla irrigated perimeter, Morocco." Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX , no. : 96372G-96372G-7.

Journal article
Published: 12 August 2014 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.

ACS Style

Meriem Barbouchi; Riadh Abdelfattah; Karem Chokmani; Nadhira Ben Aissa; Rachid Lhissou; Abderrazzak El Harti. Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 8, 3823 -3832.

AMA Style

Meriem Barbouchi, Riadh Abdelfattah, Karem Chokmani, Nadhira Ben Aissa, Rachid Lhissou, Abderrazzak El Harti. Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014; 8 (8):3823-3832.

Chicago/Turabian Style

Meriem Barbouchi; Riadh Abdelfattah; Karem Chokmani; Nadhira Ben Aissa; Rachid Lhissou; Abderrazzak El Harti. 2014. "Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 8: 3823-3832.

Journal article
Published: 01 June 2014 in EURASIAN JOURNAL OF SOIL SCIENCE (EJSS)
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Soil salinity caused by natural or human-induced processes is certainly a severe environmental problem that already affects 400 million hectares and seriously threatens an equivalent surface. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. In semi-arid and arid areas, 21% of irrigated lands suffer from waterlogging, salinity and/or sodicity that reduce their yields. 77 million hectares are saline soils induced by human activity, including 58% in the irrigated areas. In the irrigated perimeter of Tadla plain (central Morocco), the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality. Experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in terms of spatial coverage. Several studies have described the usefulness of remote sensing for mapping salinity by its synoptic coverage and the sensitivity of the electromagnetic signal to surface soil parameters. In this study, we used an image of the TM Landsat sensor and field measurements of electrical conductivity (EC), the correlation between the image data and field measurements allowed us to develop a semi-empirical model allowing the mapping of soil salinity in the irrigated perimeter of Tadla plain. The validation of this model by the ground truth provides a correlation coefficient r² = 0.90. Map obtained from this model allows the identification of different salinization classes in the study area.

ACS Style

Rachid Lhissoui; Abderrazak El Harti; Karem Chokmani. Mapping soil salinity in irrigated land using optical remote sensing data. EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) 2014, 3, 82 .

AMA Style

Rachid Lhissoui, Abderrazak El Harti, Karem Chokmani. Mapping soil salinity in irrigated land using optical remote sensing data. EURASIAN JOURNAL OF SOIL SCIENCE (EJSS). 2014; 3 (2):82.

Chicago/Turabian Style

Rachid Lhissoui; Abderrazak El Harti; Karem Chokmani. 2014. "Mapping soil salinity in irrigated land using optical remote sensing data." EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) 3, no. 2: 82.

Journal article
Published: 22 May 2014 in Applied Geomatics
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The aim of this study is to map and to identify the water erosion risks in the central High Atlas Mountains of Morocco. The approach is to integrate in the geographic information systems (GIS) for multi-criteria analyses, the geomorphometric variables (the gradient and the form of the slope) and the spectral indices derived from Landsat 5 Thematic Mapper (TM) data (form index, coloration index, and vegetation index). According to different weights for these variables, several scenarios of water erosion risk maps were produced and confronted with the ground truth for validation.

ACS Style

Bahija Bachaoui; El Mostafa Bachaoui; Soufiane Maimouni; Rachid Lhissou; Abderrazak El Harti; Abderrahmene El Ghmari. The use of spectral and geomorphometric data for water erosion mapping in El Ksiba region in the central High Atlas Mountains of Morocco. Applied Geomatics 2014, 6, 159 -169.

AMA Style

Bahija Bachaoui, El Mostafa Bachaoui, Soufiane Maimouni, Rachid Lhissou, Abderrazak El Harti, Abderrahmene El Ghmari. The use of spectral and geomorphometric data for water erosion mapping in El Ksiba region in the central High Atlas Mountains of Morocco. Applied Geomatics. 2014; 6 (3):159-169.

Chicago/Turabian Style

Bahija Bachaoui; El Mostafa Bachaoui; Soufiane Maimouni; Rachid Lhissou; Abderrazak El Harti; Abderrahmene El Ghmari. 2014. "The use of spectral and geomorphometric data for water erosion mapping in El Ksiba region in the central High Atlas Mountains of Morocco." Applied Geomatics 6, no. 3: 159-169.