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Prof. Dr. Mohamad Awad
National Council for Scientific Research, 11-8281 Beirut, Lebanon

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Research Keywords & Expertise

0 Image Segmentation
0 Machine Learning
0 Remote Sensing
0 Forest monitoring and mapping
0 Crop mapping and yield estimation

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Remote Sensing
Image Segmentation
Crop mapping and yield estimation
Machine Learning

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Journal article
Published: 16 May 2021 in Sustainability
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Forest-type classification is a very complex and difficult subject. The complexity increases with urban and peri-urban forests because of the variety of features that exist in remote sensing images. The success of forest management that includes forest preservation depends strongly on the accuracy of forest-type classification. Several classification methods are used to map urban and peri-urban forests and to identify healthy and non-healthy ones. Some of these methods have shown success in the classification of forests where others failed. The successful methods used specific remote sensing data technology, such as hyper-spectral and very high spatial resolution (VHR) images. However, both VHR and hyper-spectral sensors are very expensive, and hyper-spectral sensors are not widely available on satellite platforms, unlike multi-spectral sensors. Moreover, aerial images are limited in use, very expensive, and hard to arrange and manage. To solve the aforementioned problems, an advanced method, self-organizing–deep learning (SO-UNet), was created to classify forests in the urban and peri-urban environment using multi-spectral, multi-temporal, and medium spatial resolution Sentinel-2 images. SO-UNet is a combination of two different machine learning technologies: artificial neural network unsupervised self-organizing maps and deep learning UNet. Many experiments have been conducted, and the results showed that SO-UNet overwhelms UNet significantly. The experiments encompassed different settings for the parameters that control the algorithms.

ACS Style

Mohamad Awad; Marco Lauteri. Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests. Sustainability 2021, 13, 5548 .

AMA Style

Mohamad Awad, Marco Lauteri. Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests. Sustainability. 2021; 13 (10):5548.

Chicago/Turabian Style

Mohamad Awad; Marco Lauteri. 2021. "Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests." Sustainability 13, no. 10: 5548.

Journal article
Published: 01 January 2020 in Journal of Applied Remote Sensing
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Reliable crop maps are a vital source of information for many economic sectors. Hyperspectral images and efficient supervised classification algorithms can help in obtaining these maps. One of the objectives is to use a spectroradiometer as a source of a priori information for training supervised classification algorithms. Although there are many supervised classification algorithms, few can be trained using collected spectral signatures, such as spectral angle mapper (SAM). This algorithm relies on setting the angle threshold values manually to separate different classes, and it is usually a random process. So, the second objective is to use an automated process to optimize the angle threshold values to improve the efficiency of the SAM algorithm. An innovative cooperative classification algorithm for hyperspectral images based on enhancing the supervised SAM algorithm performance using a genetic algorithm (GA) is presented. The improvement is based on selecting global optimal threshold angle values for different classes using GA. The efficiency of the developed cooperative evolutionary algorithm is proved by classifying hyperspectral images to create maps of major crops, such as wheat, potato, and alfalfa. The source of the hyperspectral images is the Compact High-Resolution Imaging Spectrometer onboard of the Proba satellite. The evaluation of the results showed that the new cooperative evolutionary algorithm classified hyperspectral images with the highest accuracy compared to well-known reliable supervised classification algorithms.

ACS Style

Mohamad M. Awad. Cooperative evolutionary classification algorithm for hyperspectral images. Journal of Applied Remote Sensing 2020, 14, 016509 .

AMA Style

Mohamad M. Awad. Cooperative evolutionary classification algorithm for hyperspectral images. Journal of Applied Remote Sensing. 2020; 14 (1):016509.

Chicago/Turabian Style

Mohamad M. Awad. 2020. "Cooperative evolutionary classification algorithm for hyperspectral images." Journal of Applied Remote Sensing 14, no. 1: 016509.

Journal article
Published: 25 May 2019 in SWS Journal of EARTH & PLANETARY SCIENCES
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In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping. The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).

ACS Style

Mohamad M. Awad. HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS. SWS Journal of EARTH & PLANETARY SCIENCES 2019, 1, 25 -37.

AMA Style

Mohamad M. Awad. HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS. SWS Journal of EARTH & PLANETARY SCIENCES. 2019; 1 (1):25-37.

Chicago/Turabian Style

Mohamad M. Awad. 2019. "HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS." SWS Journal of EARTH & PLANETARY SCIENCES 1, no. 1: 25-37.

Journal article
Published: 24 May 2019 in Data
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In many countries, commodities provided by the agriculture sector play an important role in the economy. Securing food is one aspect of this role, which can be achieved when the decision makers are supported by tools. The need for cheap, fast, and accurate tools with high temporal resolution and global coverage has encouraged the decision makers to use remote sensing technologies. Field spectroradiometer with high spectral resolution can substantially improve crop mapping by reducing similarities between different crop types that have similar ecological conditions. This is done by recording fine details of the crop interaction with sunlight. These details can increase the same crop recognition even with the variation in the crop chemistry and structure. This paper presents a new spectral signatures database interactive tool (CSSIT) for the major crops in the Eastern Mediterranean Basin such as wheat and potato. The CSSIT’s database combines different data such as spectral signatures for different periods of crop growth stages and many physical and chemical parameters for crops such as leaf area index (LAI) and chlorophyll-a content (CHC). In addition, the CSSIT includes functions for calculating indices from spectral signatures for a specific crop and user interactive dialog boxes for displaying spectral signatures of a specific crop at a specific period of time.

ACS Style

Mohamad M. Awad; Bassem Alawar; Rana Jbeily. A New Crop Spectral Signatures Database Interactive Tool (CSSIT). Data 2019, 4, 77 .

AMA Style

Mohamad M. Awad, Bassem Alawar, Rana Jbeily. A New Crop Spectral Signatures Database Interactive Tool (CSSIT). Data. 2019; 4 (2):77.

Chicago/Turabian Style

Mohamad M. Awad; Bassem Alawar; Rana Jbeily. 2019. "A New Crop Spectral Signatures Database Interactive Tool (CSSIT)." Data 4, no. 2: 77.

Research article hydrology
Published: 13 May 2019 in Acta Geophysica
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Water scarcity has been well pronounced in the Middle East Region; however, Lebanon is still characterized by wet climate and sufficient water resources. It is a paradox that Lebanon is now under water stress, and many contradictory studies on the climate of Lebanon attributed water stress to the changing climate. Most of these studies were applied with incomplete climatic data records. Therefore, all management approaches were implemented after considering climate as a major influencer on water resources. In this study, the Emberger Aridity Index (EAi) was employed to investigate the climate regime of Lebanon over more than 30 years focusing on ten representative meteorological stations where comprehensive climatic records were analysed and supported by remotely sensed data. The EAi indicates that Lebanon is still characterized by humid climate, which conflicts with the concept of drought existence. Thus, 47% of the Lebanese territory is characterized by humid to sub-humid climate and 29% by semi-arid climate according to Emberger classification. This obviously shows that even the climate impact has a role on water scarcity, but it is not the principal influencer. The results of this study help applying new approaches for water management where the negative human interference should be accounted. It guides stakeholder and decision-maker to follow appropriate water and agricultural policies and strategies for better sustainable development.

ACS Style

Amin Shaban; Mohamad Awad; Ali J. Ghandour; Luciano Telesca. A 32-year aridity analysis: a tool for better understanding on water resources management in Lebanon. Acta Geophysica 2019, 67, 1179 -1189.

AMA Style

Amin Shaban, Mohamad Awad, Ali J. Ghandour, Luciano Telesca. A 32-year aridity analysis: a tool for better understanding on water resources management in Lebanon. Acta Geophysica. 2019; 67 (4):1179-1189.

Chicago/Turabian Style

Amin Shaban; Mohamad Awad; Ali J. Ghandour; Luciano Telesca. 2019. "A 32-year aridity analysis: a tool for better understanding on water resources management in Lebanon." Acta Geophysica 67, no. 4: 1179-1189.

Journal article
Published: 05 April 2019 in Information Processing in Agriculture
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There are many crop yield estimation techniques which are used in countries around the world, but the most effective is the one based on remote sensing data and technologies. However, remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions (percentage of cloud cover), and low temporal resolution. These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete. There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images. However, this type of images are suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution satellite images, a new mathematical model is created. Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation. To verify the results of the intelligent system, several farmers are interviewed and information about their crops yield is collected. The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.

ACS Style

Mohamad M. Awad. An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation. Information Processing in Agriculture 2019, 6, 316 -325.

AMA Style

Mohamad M. Awad. An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation. Information Processing in Agriculture. 2019; 6 (3):316-325.

Chicago/Turabian Style

Mohamad M. Awad. 2019. "An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation." Information Processing in Agriculture 6, no. 3: 316-325.

Journal article
Published: 13 March 2019 in Agriculture
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Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

ACS Style

Mohamad M. Awad. Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques. Agriculture 2019, 9, 54 .

AMA Style

Mohamad M. Awad. Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques. Agriculture. 2019; 9 (3):54.

Chicago/Turabian Style

Mohamad M. Awad. 2019. "Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques." Agriculture 9, no. 3: 54.

Research article hydrology
Published: 16 January 2019 in Acta Geophysica
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Lebanon, with its geographic location facing the Mediterranean Sea and dominant rugged topography, is characterized by a strong climatic variability even between zones located few kilometres apart. The investigation of the climatic indices is necessary to delineate such diverse climatic situation over Lebanon. In this context, this paper investigates the periodic behaviour in annual Emberger aridity index (AIE) of 14 weather stations in representative sites of Lebanon. The AIE indicates that the dominant climate of Lebanon, which is mainly varying from humid to semi-arid, follows cyclonic meteorological patterns. The periodicities of AIE identified by using a robust technique seem almost altitude related and range between 2 and 21 years. The geographic distribution of periodicities implies two major zones in the northern and southern parts of Lebanon, being featured by longer and shorter periodicities, respectively. The formation of these two periodicity zones can be related to regional climatic zoning. The remarkable diversity in periodicity indicates predominant microclimates with specified cyclonic climate that characterizes Lebanon’s climate rather than a merely existence of climate change.

ACS Style

Luciano Telesca; Amin Shaban; Mohamad Awad. Analysis of heterogeneity of aridity index periodicity over Lebanon. Acta Geophysica 2019, 67, 167 -176.

AMA Style

Luciano Telesca, Amin Shaban, Mohamad Awad. Analysis of heterogeneity of aridity index periodicity over Lebanon. Acta Geophysica. 2019; 67 (1):167-176.

Chicago/Turabian Style

Luciano Telesca; Amin Shaban; Mohamad Awad. 2019. "Analysis of heterogeneity of aridity index periodicity over Lebanon." Acta Geophysica 67, no. 1: 167-176.

Chapter
Published: 31 July 2018 in Hybrid Metaheuristics for Image Analysis
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Metaheuristic algorithms are an upper level type of heuristic algorithm. They are known for their efficiency in solving many difficult nondeterministic polynomial (NP) problems such as timetable scheduling, the traveling salesmen, telecommunications, geosciences, and many other scientific, economic, and social problems. There are many metaheuristic algorithms, but the most important one is the Genetic Algorithm (GA). What makes GA an exceptional algorithm is the ability to adapt to the problem to find the most suitable solution—that is, the global optimal solution. Adaptability of GA is the result of the population consisting of “chromosomes” which are replaced with a new one using genetics stimulated operators of crossover (reproduction ), and mutation . The performance of the algorithm can be enhanced if hybridized with heuristic algorithms. These heuristics are sometimes needed to slow the convergence of GA toward the local optimal solution that can occur with some problems, and to help in obtaining the global optimal solution. GA is known to be very slow compared to other known optimization algorithms such as Simulated Annealing (SA). This speed will further decrease when GA is hybridized (HyGA). To overcome this issue, it is important to change the structure of the chromosomes and the population . In general, this is done by creating variable length chromosomes . This type of structure is called a Hybrid Dynamic Genetic Algorithm (HyDyGA). In this chapter, GA is covered in detail, including hybridization using the Hill-Climbing Algorithm. The improvements to GA are used to solve a very complex NP problem, which is image segmentation. Using multicomponent images increases the complexity of the segmentation task and puts more burden on GA performance. The efficiency of HyGA and HyDyGA in the segmentation process of multicomponent images is proved using collected field samples; it can reach more than 97%. In addition, the reliability and the robustness of the new algorithms are proved using different analysis methods.

ACS Style

Mohamad M. Awad. Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms. Hybrid Metaheuristics for Image Analysis 2018, 1 -31.

AMA Style

Mohamad M. Awad. Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms. Hybrid Metaheuristics for Image Analysis. 2018; ():1-31.

Chicago/Turabian Style

Mohamad M. Awad. 2018. "Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms." Hybrid Metaheuristics for Image Analysis , no. : 1-31.

Conference paper
Published: 20 June 2018 in 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
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ACS Style

Mohamad Awad. OPTIMIZATION OF CROP YIELD ESTIMATION BASED ON REMOTE SENSING AND NEW MATHEMATICAL MODEL. 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing 2018, 1 .

AMA Style

Mohamad Awad. OPTIMIZATION OF CROP YIELD ESTIMATION BASED ON REMOTE SENSING AND NEW MATHEMATICAL MODEL. 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing. 2018; ():1.

Chicago/Turabian Style

Mohamad Awad. 2018. "OPTIMIZATION OF CROP YIELD ESTIMATION BASED ON REMOTE SENSING AND NEW MATHEMATICAL MODEL." 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing , no. : 1.

Conference paper
Published: 20 June 2018 in 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
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ACS Style

Mohamad Awad. CROP MAPPING USING HYPERSPECTRAL DATA AND TECHNOLOGIES - A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS. 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing 2018, 1 .

AMA Style

Mohamad Awad. CROP MAPPING USING HYPERSPECTRAL DATA AND TECHNOLOGIES - A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS. 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing. 2018; ():1.

Chicago/Turabian Style

Mohamad Awad. 2018. "CROP MAPPING USING HYPERSPECTRAL DATA AND TECHNOLOGIES - A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS." 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing , no. : 1.

Chapter
Published: 07 April 2018 in Climate Change Impacts on Water Resources
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Litani River, with a 2110-km2 catchment area (about 20% of Lebanon) and a 174-km length, releases an average annual discharge of about 385 mm3/year. The river in its northern part flattens between the Mount-Lebanon and Anti-Lebanon mountain chains, thereby spanning between several mountain ridges in the southern part. Consequent streams are connected to its primary watercourse. The Litani River Basin (LRB) comprises an elongated catchment where it gently slopes from the north Bekaa Plain extending southward where it meanders westward to outlets into the Mediterranean Sea. Therefore, the river transits into the inner and coastal zones of Lebanon comprising two major drainage systems (described as the Upper and Lower Basins) occupied into one watershed. The Qaraaoun Reservoir, the largest of its type in Lebanon (about 12 km2), is located in the southern part of the Upper Basin. Before 1959, the site of the reservoir comprised a natural depression. However, when large volumes of water started to accumulate there, a dam was built. Snow has a significant contribution to feeding the river. The extension of the Litani River among the carbonate rocks and alluvial deposits makes it an open hydrogeological system that is fed from and feeds on the permeable and porous lithologies. This chapter introduces the major physical characteristics of the Litani River including the catchment and drainage properties as well as the water resources and land cover.

ACS Style

Amin Shaban; Ghaleb Faour; Mohamad M. Awad. Physical Characteristics and Water Resources of the Litani River Basin. Climate Change Impacts on Water Resources 2018, 33 -56.

AMA Style

Amin Shaban, Ghaleb Faour, Mohamad M. Awad. Physical Characteristics and Water Resources of the Litani River Basin. Climate Change Impacts on Water Resources. 2018; ():33-56.

Chicago/Turabian Style

Amin Shaban; Ghaleb Faour; Mohamad M. Awad. 2018. "Physical Characteristics and Water Resources of the Litani River Basin." Climate Change Impacts on Water Resources , no. : 33-56.

Original paper
Published: 09 November 2017 in Journal of Forestry Research
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Mapping forests is an important process in managing natural resources. At present, due to spectral resolution limitations, multispectral images do not give a complete separation between different forest species. In contrast, advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species. In this study, spectral signatures for stone pine (Pinus pinea L.) forests were collected using an advanced spectroradiometer “ASD FieldSpec 4 Hi-Res” with an accuracy of 1 nm. These spectral signatures are used to compare between different multispectral and hyperspectral satellite images. The comparison is based on processing satellite images: hyperspectral Hyperion, hyperspectral CHRIS-Proba, Advanced Land Imager (ALI), and Landsat 8. Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed. In addition, a well-known hyperspectral image classification algorithm, spectral angle mapper (SAM), has been improved to perform the classification process efficiently based on collected spectral signatures. The results show that the modified SAM is 9% more accurate than the conventional SAM. In addition, experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8 (overall accuracy 82%, precision 93%, and Kappa coefficient 0.43 compared to 60, 67%, and 0.035, respectively). Similarly, Hyperion is better than ALI in mapping stone pine (overall accuracy 92%, precision 97%, and Kappa coefficient 0.74 compared to 52, 56%, and − 0.032, respectively).

ACS Style

Mohamad M. Awad. Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research 2017, 29, 1395 -1405.

AMA Style

Mohamad M. Awad. Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research. 2017; 29 (5):1395-1405.

Chicago/Turabian Style

Mohamad M. Awad. 2017. "Forest mapping: a comparison between hyperspectral and multispectral images and technologies." Journal of Forestry Research 29, no. 5: 1395-1405.

Journal article
Published: 06 February 2017 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Using very high resolution remote sensing images to extracting urban features from very high resolution remote sensing images is a very complex and difficult task. The improvement in geospatial technologies brought forward many solutions that can help in improving the process of urban feature extraction. Data collection using light detection and ranging (LiDAR) and capturing very high resolution optical images concurrently is one of these solutions. This research proves that the fusion of high-resolution optical image with LiDAR data can improve image processing results. It is based on increasing urban features extraction success rate by reducing oversegmentation. The fusion process relies first on wavelet transform techniques, which are run several times with different parameters (rules). Then, an innovative technique is implemented to improve fusion process. The two techniques are compared, and both have reduced fragmented segments and created homogeneous urban features. However, the fused image with the innovative technique has improved the accuracy of the segmentation results. The average accuracy for building detection is 96% (maximum 100% and minimum 92%) using the innovative technique compared to 21% and 51% for no fusion and wavelet-fusion-based techniques. Furthermore, an index is used to measure the quality of the building details which are detected after using the innovative fusion technique. The result indicates that the quality index is greater or equal to 86%.

ACS Style

Mohamad M. Awad. Toward Robust Segmentation Results Based on Fusion Methods for Very High Resolution Optical Image and LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 10, 2067 -2076.

AMA Style

Mohamad M. Awad. Toward Robust Segmentation Results Based on Fusion Methods for Very High Resolution Optical Image and LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 10 (5):2067-2076.

Chicago/Turabian Style

Mohamad M. Awad. 2017. "Toward Robust Segmentation Results Based on Fusion Methods for Very High Resolution Optical Image and LiDAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 5: 2067-2076.

Conference paper
Published: 01 November 2016 in 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET)
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Supervised Artificial Neural Network such as Feed-forward Multi-layer Perceptron algorithm and advanced Remote sensing technologies play an important role in improving mapping process of various biophysical and biochemical vegetation parameters. Advanced knowledge of the crop status will help farmers, decision makers and policy makers to take precautions to reduce losses and to increase production. Leaf chlorophyll content of the crop can help to obtain information about the physiological status of the plant and it can help in studying of the carbon balance and responses to fertilizer (e.g., nitrogen) application. In this research, Leaf Chlorophyll Content (LCC) for winter wheat in a large area in Lebanon is estimated based on the technologies and algorithms listed earlier. The estimation process is based on the collected field measurements of reflectance data and chlorophyll content of the wheat by a very high resolution spectroradiometer and precise chlorophyll meter. Mathematical models for wheat crop chlorophyll content are created based on the collected data and a supervised Artificial Neural Network algorithm. The created mathematical models are used to investigate the status of the winter wheat crop throughout Lebanon using remote sensing data. The verification of the results is based on the collected LCC measurements using a chlorophyll meter during the same time a remote sensing satellite is imaging the study area. The verification of the results is based on computing the accuracies between the predicted and estimated LCC. The average accuracy of the new mathematical models is about 99.5%.

ACS Style

Mohamad Awad. New mathematical models to estimate wheat Leaf Chlorophyll Content based on Artificial Neural Network and remote sensing data. 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) 2016, 86 -91.

AMA Style

Mohamad Awad. New mathematical models to estimate wheat Leaf Chlorophyll Content based on Artificial Neural Network and remote sensing data. 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET). 2016; ():86-91.

Chicago/Turabian Style

Mohamad Awad. 2016. "New mathematical models to estimate wheat Leaf Chlorophyll Content based on Artificial Neural Network and remote sensing data." 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) , no. : 86-91.

Journal article
Published: 18 May 2015 in International Journal of Advanced Remote Sensing and GIS
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The Stone Pine “pinus pinea” is native to the Mediterranean region. It has been used and cultivated for their edible pine nuts since prehistoric times. At present most of the decision makers in the world are enforcing new policies which will increase forest cover in their countries in order to mitigate the effect of the climate change specifically forest species that withstands harsh and climate change. In this research Geospatial technologies are used to help in the forest expansion as part of the forest management by implementing a new Stone pine suitability model. This model is applicable in any area in the world where the indicated natural and geographic conditions are met. The model was applied to an area rich in Stone pine and the results show that more than 60% of the total study area can be reforested. Hundreds of existing Stone Pine forest locations is used to verify the accuracy of the suitability map. The verification showed that 96% of these locations are on the high and medium classes of the map.

ACS Style

Mohamad Mostafa Awad. Suitability Analysis for Stone pine Reforestation using Geospatial Technologies. International Journal of Advanced Remote Sensing and GIS 2015, 4, 1008 -1018.

AMA Style

Mohamad Mostafa Awad. Suitability Analysis for Stone pine Reforestation using Geospatial Technologies. International Journal of Advanced Remote Sensing and GIS. 2015; 4 (1):1008-1018.

Chicago/Turabian Style

Mohamad Mostafa Awad. 2015. "Suitability Analysis for Stone pine Reforestation using Geospatial Technologies." International Journal of Advanced Remote Sensing and GIS 4, no. 1: 1008-1018.

Journal article
Published: 01 November 2014 in Ecological Informatics
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ACS Style

Mohamad Awad. Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network. Ecological Informatics 2014, 24, 60 -68.

AMA Style

Mohamad Awad. Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network. Ecological Informatics. 2014; 24 ():60-68.

Chicago/Turabian Style

Mohamad Awad. 2014. "Sea water chlorophyll-a estimation using hyperspectral images and supervised Artificial Neural Network." Ecological Informatics 24, no. : 60-68.

Journal article
Published: 01 August 2014 in Photogrammetric Engineering & Remote Sensing
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ACS Style

Mohamad Awad; Ihab Jomaa; Fatima Arab. Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+. Photogrammetric Engineering & Remote Sensing 2014, 80, 725 -731.

AMA Style

Mohamad Awad, Ihab Jomaa, Fatima Arab. Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+. Photogrammetric Engineering & Remote Sensing. 2014; 80 (8):725-731.

Chicago/Turabian Style

Mohamad Awad; Ihab Jomaa; Fatima Arab. 2014. "Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+." Photogrammetric Engineering & Remote Sensing 80, no. 8: 725-731.

Research article
Published: 11 February 2013 in Journal of Engineering
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Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the enhanced image, the application of the morphological operators, and finally the extraction of the road network. The new model is more accurate position wise and requires less effort and time compared to the traditional supervised and semi-supervised urban extraction methods such as simple edge detection techniques or manual digitization. Experiments conducted on high-resolution satellite images prove the high accuracy and the efficiency of the new model. The positional accuracy of the extracted road features compared to the manual digitized ones, the counted number of detected road segments, and the percentage of completely closed and partially closed curves prove the efficiency and accuracy of the new model.

ACS Style

Mohamad M. Awad. A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images. Journal of Engineering 2013, 2013, 1 -9.

AMA Style

Mohamad M. Awad. A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images. Journal of Engineering. 2013; 2013 ():1-9.

Chicago/Turabian Style

Mohamad M. Awad. 2013. "A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images." Journal of Engineering 2013, no. : 1-9.

Journal article
Published: 01 January 2013 in American Journal of Remote Sensing
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ACS Style

Mohamad M. Awad. Improving Satellite Image Segmentation Using Evolutionary Computation. American Journal of Remote Sensing 2013, 1, 1 .

AMA Style

Mohamad M. Awad. Improving Satellite Image Segmentation Using Evolutionary Computation. American Journal of Remote Sensing. 2013; 1 (2):1.

Chicago/Turabian Style

Mohamad M. Awad. 2013. "Improving Satellite Image Segmentation Using Evolutionary Computation." American Journal of Remote Sensing 1, no. 2: 1.