Monitoring of Dead Sea water surface variation using multi-temporal satellite data and GIS.

Remote sensing (RS) and geographic information systems (GIS) are very useful for environmental-related studies, particularly in the field of surface water studies such as monitoring of lakes. The Dead Sea is exposed to very high evaporating process with considerable scarcity of water sources, thus leading to a remarkable shrinkage in its water surface area. The lake suffers from dry out due to the negative balance of water cycle during the previous four decades. This paper discusses the application of RS, GIS, and Global Positioning System to estimate the lowering and the shrinkage of Dead Sea water surface over the period 1810–2005. A set of multi-temporal remote sensing images were collected and processed to show the lakes aerial extend shrinkage from 1973 up to 2004. Remote sensing data were used to extract spatial information and to compute the surface areas for Dead Sea for various years. The current study aims at estimating the fluctuation of Dead Sea level over the study period with special emphasis on the environmental impact assessment that includes the degradation level of the Dead Sea. The results indicated that there is a decrease of 20 m in the level of the Dead Sea that has occurred during the study period. Further, the results showed that the water surface area of the Dead Sea has shrunk from 934.26 km2 in 1973 to 640.62 km2 in 2004

UNIVERSITY OF CALGARY Semi-Automatic Registration of Multi-Source Satellite Imagery with Varying Geometric Resolutions

Image registration concerns the problem of how to combine data and information from multiple sensors in order to achieve improved accuracy and better inferences about the environment than could be attained through the use of a single sensor. Registration of imagery from multiple sources is essential for a variety of applications in remote sensing, medical diagnosis, computer vision, and pattern recognition. In general, an image registration methodology must deal with four issues. First, a decision has to be made regarding the choice of primitives for the registration procedure. The second issue concerns establishing the registration transformation function that mathematically relates images to be registered. Then, a similarity measure should be devised to ensure the correspondence of conjugate primitives. Finally, a matching strategy has to be designed and implemented as a controlling framework that utilizes the primitives, the similarity measure, and the transformation function to solve the registration problem. The Modified Iterated Hough Transform (MIHT) is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function as well a

Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions

Bibliography: p. 129-135Image registration concerns the problem of how to combine data and information from multiple sensors in order to achieve improved accuracy and better inferences about the environment than could be attained through the use of a single sensor. Registration of imagery from multiple sources is essential for a variety of applications in remote sensing, medical diagnosis, computer vision, and pattern recognition. In general, an image registration methodology must deal with four issues. First, a decision has to be made regarding the choice of primitives for the registration procedure. The second issue concerns establishing the registration transformation function that mathematically relates images to be registered. Then, a similarity measure should be devised to ensure the correspondence of conjugate primitives. Finally, a matching strategy has to be designed and implemented as a controlling framework that utilizes the primitives, the similarity measure, and the transformation function to solve the registration problem. The Modified Iterated Hough Transform (MIHT) is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function as well as the correspondence between conjugate primitives. The MIHT procedure follows an optimal sequence for parameter estimation. This sequence takes into account the contribution of linear features with different orientations at various locations within the imagery towards the estimation of the transformation parameters in question. Accurate co-registration of multi-sensor datasets is captured at different times is a prerequisite step for a reliable change detection procedure. Once the registration problem has been solved, the suggested methodology proceeds by detecting changes between the registered images. Derived edges from the registered images are used as the basis for change detection. Edges are utilized because they are invariant regardless of possible radiometric differences between the images in question. Experimental results using real data proved the feasibility and robustness of the suggested approach

Geomatics for Rehabilitation of Mining Area

Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its original shape and aims to minimize and mitigate the environmental effects to allow new land uses. The success of different rehabilitation strategy and newly suggested urban and architecture modeling depends on the landscape characterization (topography of the study area and its derivatives such as slope and aspects, geological and geomorphologic nature of the study area). The aim of this study is to demonstrate the utility of different methodologies based on geomatics techniques (Photogrammetry, Remote Sensing, Global Positioning System (GPS) and three dimensional Geographic Information System (GIS)) for highlighting landscape characterization which is needed for rehabilitation of Mahis area. Photogrammetric adjustment procedures were used to create digital elevation model and Orth-Photo model for the study area using aerial images. Remote sensing data were used for land classification to provide vital information for rehabilitation planning. GPS field observations were used to build spatial network for the study area based on ground control point collections. Finally, realistic representation of the study area with three dimensiona

Multi-Temporal satellite imagery for infrastructure growth assessment of Dubai City, UAE

Throughout the past few decades, Dubai City has witnessed massive growth in its urban area and infrastructure facilities. The discovery of oil and gas in the Emirate significantly played a role for such a rapid growth. Given this fact and the short time-period for such an expansion, it is crucial to develop an understanding of the patterns of the development in the City, where policy-makers, researchers, and concerned authorities would gain better vision and strategy for the future. Recent advances in satellite imagery in terms of improved spatial and temporal resolutions are allowing for efficient identification of change patterns and the prediction of areas of growth. This study aims to quantify and analyse the spatial–temporal urbanization that took place in Dubai City throughout the past decades (specifically from early 1970’s until 2015). Multi temporal satellite images with various geometric and radiometric resolutions will be utilized for this purpose. The suggested methodology consists of a sequence of image processing techniques that include supervised and unsupervised classification. Subsequently, the classified images were utilized to quantify the urbanization of the City. The results show that since 1970, the urbanization and population have been dramatically increased by 5 and 12 times respectively. The resulting trend can be potentially used to evaluate the consequences of massive urban development, such as City infrastructure, water, environmental and the social impact

Evaluation of Groundwater Quality Using Groundwater Quality Index (GWQI) in Sharjah, UAE

The rapid growth in the world population resulted in an increase of the freshwater needs in many sectors. Groundwater is the most important freshwater source specially for arid and semi-arid regions due to lack of surface water sources and low precipitation rates in those regions. In this study, monthly groundwater quality data were collected from eleven well fields in Sharjah over the period of 2004-2017. Water quality parameters including bicarbonate, calcium, chloride, fluoride, magnesium, sodium and sulphate were selected for the analysis. In the study, water quality index (WQI) process is used to develop groundwater quality index (GWQI) for Sharjah using above mentioned water quality parameters. Mann-Kendall and Spearman’s Rho tests were adopted as non-parametric trend tests for temporal (trend) analysis of GWQI, whereas inverse distance weighting interpolation was used in GWQI spatial trend analysis. Temporal trend analysis results showed significant trends in 8 out of 11 well fields. Spatial analysis showed the highest values for salinity ions in the well fields closest to the northern region, whereas the lowest values were detected in the southern region

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images

Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002−2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events

Multi-Temporal satellite imagery for infrastructure growth assessment of Dubai City, UAE

Throughout the past few decades, Dubai City has witnessed massive growth in its urban area and infrastructure facilities. The discovery of oil and gas in the Emirate significantly played a role for such a rapid growth. Given this fact and the short time-period for such an expansion, it is crucial to develop an understanding of the patterns of the development in the City, where policy-makers, researchers, and concerned authorities would gain better vision and strategy for the future. Recent advances in satellite imagery in terms of improved spatial and temporal resolutions are allowing for efficient identification of change patterns and the prediction of areas of growth. This study aims to quantify and analyse the spatial–temporal urbanization that took place in Dubai City throughout the past decades (specifically from early 1970’s until 2015). Multi temporal satellite images with various geometric and radiometric resolutions will be utilized for this purpose. The suggested methodology consists of a sequence of image processing techniques that include supervised and unsupervised classification. Subsequently, the classified images were utilized to quantify the urbanization of the City. The results show that since 1970, the urbanization and population have been dramatically increased by 5 and 12 times respectively. The resulting trend can be potentially used to evaluate the consequences of massive urban development, such as City infrastructure, water, environmental and the social impact