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Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images.

The penetration ability of the Green wavelength in multispectral Light Detection and Ranging (LiDAR) pulses into water bodies makes it a valuable tool in DTM generation for land and certainly bathymetry. Titan is the first active multispectral LiDAR that extends information for 3D point clouds from a single sensor. It is provided by Teledyne Optech is an example of the multispectral LiDAR system. It has three different wavelengths mid-infrared (MIR), near-infrared (NIR), and green (1.550, 1.064, and 0.532 µm) respectively. This research aims to compare the performance of two ground-filtering algorithms to obtain digital terrain models in the forest and coastal areas. The tested area is located at the Southwest tip of Tobermory (Ontario, Canada). Two filtering algorithms are used; Adaptive Triangulated Irregular Network (ATIN) and Elevation Threshold with an Expand Window (ETEW). The two filtering algorithms performance were evaluated using the cloud to cloud (C2C) method provided by cloud compare software. The results show that ATIN gives results better than ETEW in the three different wavelengths of multispectral LiDAR data. إن قدرة اختراق الطول الموجي الأخضر في نبضات الضوء متعدد الأطياف والمدى (الليدار) في الأجسام المائية تجعله أداة قيمة في توليد نموذج تضاريس رقمي للأرض وبالتأکيد قياس الأعماق. "تيتان" هو أول ليدار نشط متعدد الأطياف يقدم معلومات عن السحب ثلاثية الأبعاد من مستشعر واحد. يتم توفيره بواسطة شرکه تيليدين اوبتک وهو مثال لنظام الليدار متعدد الأطياف. لديه ثلاثة أطوال موجية مختلفة وهي الأشعة تحت الحمراء ، والأشعة تحت الحمراء القريبة ، والأخضر (1.550 و 1.064 و 0.532 مايکرو متر) على التوالي. يهدف هذا البحث إلى مقارنة أداء خوارزميتي تصفية الأرض للحصول على نماذج التضاريس الرقمية في الغابات والمناطق الساحلية. تقع المنطقة المختبرة في الطرف الجنوبي الغربي من توبرموري (أونتاريو ، کندا). يتم استخدام خوارزميتي تصفية وهما شبکة غير منتظمة متکيفة مثلثة وعتبة ارتفاع مع نافذة موسعة. تم تقييم أداء خوارزميتي التصفية باستخدام طريقة السحابة إلى السحابة المقدمة من برنامج مقارنة السحابة. وقد أظهرت النتائج أن مرشح الشبکه الغير منتظمة المتکيفه المثلثه يعطي نتائج أفضل من عتبه الارتفاع مع النافذة الموسعة في الأطوال الموجية الثلاثة المختلفة لبيانات الليدار متعددة الأطياف.

This research is concerned with the use of remotely sensed data in the estimation of the solar energy potentiality of the flat roofs in El Maadi District, Cairo. A solar radiation model was applied using a digital surface model (DSM) generated from two Worldview stereo satellite images using the digital photogrammetric workstation (Leica Photogrammetric Suite) LPS. The procedures included applying orientation with Rational Polynomial Coefficients (RPC) with and without GCPs, tie point measurements, aerial triangulation, automatic DSM and editing, true ortho-rectification. The accuracy of the DSM has been assessed and reported for both methods. The ortho-rectification, of stereo satellite images has been performed. A PAN true ortho-images with 0.5 m resolution resulted from applying orientation with rational polynomial coefficients (RPCs) refined with four accurate differential ground control points (DGCPs) and a high accurate DSM derived from stereo images were generated. Two classification methods were conducted (SVM and ANN) on the true ortho-image. First method without utilizing texture, the second utilizing texture features of Grey Level Co-occurrence Matrix (GLCM) as inputs. The SVM of the ortho-image and texture features overall accuracy was 93.4%, kappa coefficient was 0.92 and ANN of ortho-image and texture features overall accuracy of 91.2% and kappa coefficient was 0.90. Using the generated DSM as input, the area solar radiation model in ESRI ArcGIS was run. The model resulted in an annual total radiation grid for year 2015 which was converted to average daily radiation in kilowatt hour/square meters per day. A sample of 265 buildings footprints was extracted from the classification and used for the estimation. Assuming that 50% roof areas are available for solar Photovoltaic (PV), 12% PV grid conversion efficiency and 0.6 performance ratio and using ESRI zonal statistics tool, the technical potentiality for solar PV electricity generation for the sample buildings was calculated, classified into five classes and mapped.

The rapid urbanization coupled with tremendous growth in population and built-up areas in cities, lead to problems associated with global climate change impacts such as urban heat islands. The current study is concerned with mapping the dynamics of urban growth and the urban heat island (UHI) in Tanta city, Nile Delta, Egypt using remote sensing data and techniques. The thermal bands of five multi-temporal Landsat5 TM and Landsat7 ETM+ images covering Tanta city, Egypt acquired in summer season were analysed for the spatio-temporal identification and mapping of UHIs. First, Landsat multiple bands were used to classify the landcover and to detect the changes in built-up areas. Second, the thermal bands were used to retrieve the land surface temperature (LST) in the city and outskirts. Third, The urban heat islands (UHI) intensity was identified for each image and mapped using the mean temperature and standard deviation. Finally, a regression analysis was conducted to study the relationship between LST and green areas and built-up areas. The results revealed the enormous urban growth and reduction of the green areas in Tanta during the study periods. This was accompanied by an increase in urban heat islands intensity. The results revealed that the relationships between the percentage of built-up areas, green areas and the land surface temperature in the city.

Hyperspectral imaging has many applications such as Land Use / Land Cover mapping, urban planning, mineral exploration, environmental monitoring, and military surveillance. This research is concerned with the investigation of the improvement of urban land cover mapping using airborne hyperspectral data. Geometric correction and atmospheric correction have been conducted. Quality of rectification has been assessed using DGPS check points. It was found that the total RMS of airborne hyperspectral data was 2.7372m. Minimum Noise Fraction (MNF) transformation and Pixel Purity Index (PPI) have been used for spectral and spatial data reduction. After that feature selection has been carried out. In this research a supervised classification method has been applied using the Spectral Angle Mapper,then, the discrimination of urban feature has been improved using expert system. An expert system classification was developed using the knowledge engineer of ERDAS Imagine 2010 for post classification refinement of initially classified output. The primary motivation behind using the expert system was to reclassify the initial Spectral Angle Mapper classification and reduce errors of omission and commission. Results revealed that the overall accuracy of SAM was 90% and kappa index was 0.89 whereas the expert system gives slightly higher overall accuracy 94.5 percent and a kappa index was 0.93.

Egypt has launched its first Earth remote sensing satellite EgyptSat-1 in 2007. A series of evaluations of the usefulness and limitations of EgyptSat-1 images for different applications are required. This research is concerned with the investigation of the geometric and cartographic potential of EgyptSat-1 image in order to know to what scale EgyptSat-1 image is suitable for map production and map updating. The geometrical accuracy of rectification of EgyptSat-1 based on non-parametric rectification (polynomial approach and rubber sheeting) has been evaluated. Four methods of rectification have been compared: different orders of 2D polynomials (first-order polynomial, second-order polynomial, third-order polynomial) and rubber sheeting method. Different sets of ground control points (observed with differential global positioning system) have been tested. Several experiments were performed to rectify EgyptSat-1 image using the four rectification methods. For each method, different numbers of ground control points (GCPs) were used starting at 15 points, and then the number was reduced each time till the minimum number of GCPs for each method is reached and with three different GCP distributions. Various issues such as the solutions, feasibility, accuracy, and requirements for control information are addressed. The results show that rubber sheeting followed by third-order polynomial then second-order polynomial then first-order polynomial gives the best root mean square error. Actually, a serious mapping should be based on a satisfying by the theory justified mathematical model so that rubber sheeting should not be used for a serious mapping even it shows smaller discrepancies, but for the flat test area, an approximate 2D transformation can be accepted. Also although third-order polynomial is accurate in the proximity of GCPs and shows smaller discrepancies compared to the first- and the second-order polynomials, they can lead to significant errors for regions outside the GCP range and should not be used for a serious mapping. One can be considered the best resulted rectified image that obtained from second-order polynomial followed by the first-order polynomial. It was found in our study area that the minimum number of control points for each method gives accuracy better than a pixel. The first-order polynomial and rubber sheeting are good from the economical cost that they use minimum number of control points (three). It was found that the planimetric accuracy of rectified EgyptSat image met the 1:25,000 mapping standard (National Map Accuracy Standards). Also from the assessment of the information content of the EgyptSat image, it was found that it is consistent with the map scale of 1:25,000. In conclusion by analysis of the results, it is obvious that EgyptSat images are able to produce and update map of scale 1:25,000.

A shoreline is defined as the line of contact between land and water surface. Shoreline extraction/mapping is critical in many coastal zone applications such as safe navigation and coastal environmental protection. Digitizing a feature such as the coastline is a very tedious and time-consuming operation. In this research shoreline mapping at a sub-pixel and pixel levels was evaluated in the northern part of the coastal zone of Egypt (Port Said). Three different image fusion techniques have been used to merge two co-registered data, radar (Radarsat) and optical (Landsat), to improve the classification accuracy. Spatial, spectral, and radiometric qualities of the fused images have been evaluated. The resulted three fused images, the Landsat and Radarsat images, were fed to the fuzzy and the maximum likelihood classifiers where the classification delineated two classes; water and nonwater. The accuracy of the classified images was estimated based on a reference data. After classification, the results have been compared where it was found that image fusion has improved the classification accuracy and the accuracy of the fuzzy classification is better than the maximum likelihood classification in all cases. The best resulted classified image from the ten cases is that obtained from the fused Radarsat-Landsat images using IHS technique that was classified with the fuzzy classifier. Shoreline has been extracted from the (IHS-fuzzy), (SAR-fuzzy), and (Landsat-fuzzy). Experimental results showed that the shoreline extraction accuracy is dramatically improved by the effective image fusion of Landsat and SAR data. The accuracies of the extracted shorelines from (Landsat-fuzzy), (Radarsat-fuzzy), and (IHS-fuzzy) data based on twenty differential GPS check points are estimated to be 5.69, 5.26, and 5.14 m, respectively. The three shorelines extracted/mapped from (Landsat-fuzzy), (Radarsat-fuzzy), (IHS-fuzzy) data were compared to a reference shoreline. The RMS has been computed related to thirty checkpoints on the reference shoreline. The accuracies of the extracted shorelines from (Landsat-fuzzy), (SAR-fuzzy), and (IHS-fuzzy) data are estimated to be 8.3, 7.27, and 6.75 m, respectively. The positional quality of the extracted shorelines (using generalization factor) from (Landsat-fuzzy), (SAR-fuzzy), and (IHS-fuzzy) data are estimated to be 0.977, 0.986, and 0.999, respectively.

Buildings are one of the most important geospatial features for spatial analysis and mapping. Building extraction has been an active research topic in computer vision as well as digital photogrammetry in recent years. Building detection is the process of obtaining the approximate position and shape of a building, while building extraction can be defined as the problem of precisely determining the building outlines, which is one of the critical problems in digital photogrammetry. Building information is extremely important for many applications such as urban planning, telecommunication, three-dimensional city modeling, or extraction of unauthorized buildings over agricultural lands. Three approaches for building detection based on maximum likelihood classification have been compared, firstly, building detection from classification of multispectral satellite image only. The second approach is building detection from classification of multispectral satellite image, while the height information from Light Detection and Ranging (LIDAR) data is applied as an additional channel together with spectral channel. The third approach is building detection based on classification of multispectral satellite image where normalized difference vegetation index (NDVI) and the height information from LIDAR data are applied as additional channels together with spectral channel. The contributions of the individual cues used in the classification have been evaluated. The three approaches were tested using urban blocks containing different sizes, roof color and shapes of buildings. The results show that the third approach is the best for building detection followed by the second approach then the first approach. The third approach appears to be quite successful especially in solving the problem of building detection for those urban blocks that contain closely located buildings as well as in separation of buildings from trees. The third approach results have been improved by developing a building detection module based on integration of classified image, elevation data, and spectral information. A rule-based expert system consists of essentially hypothesis (output; buildings), and variables of a knowledge base were developed in the knowledge engineer of ERDAS Imagine for post-classification refinement of initially classified output building mask. Classification rules were enriched with ancillary data such as the normalized digital surface model and the NDVI. Each rule is a representation of each node in the tree that describes a building class or probability of presence of buildings pixel. Then, the building detection result has been evaluated. It has been found that the use of an expert system, which considers expert knowledge, would further help in the discrimination of the classes and improve classification accuracy of buildings. The overall accuracy of expert classification was 96% and kappa coefficient was 0.95.

Nile River has about 144 islands from Aswan to the Mediterranean Sea. In this research remotely sensed images have been used for the assessment of land cover changes in the Al-Monib island as part of an ongoing sustainable development of this island. The island has witnessed high rates of change in land use in the past few years. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas. The most common use of many of the change detection algorithms has been to identify the change in coarse to medium spatial resolution satellite imagery. Now there is great interest in identifying the change in high spatial resolution multispectral data such as SPOT5 and QuickBird. In order to improve the quality and accuracy, different cues have been extracted such as IHS or PCA and texture derived from color image. Fuzzy classification has been performed several times utilizing from Multi-Cue integration (resulted into six classifications) for each date. Assessment of different approaches of classification (six classifications) has been performed for each date. After that fuzzy post classification comparison has been made for the best case. Value of the urban expansions for the period of 2002–2009 was calculated as 0.11 km2. The urban expansion rate had been realized as 3.04%. Another significant change was the decline in agricultural lands result was estimated to be 8.29%. The changes of landscape pattern were then analyzed using a series of spatial metrics (class level) which were derived from FRAGSTATS software.