Advances in Remote Sensing and Geo Informatics Applications

Atmospheric aerosol particles affect the formation of precipitation through influencing on microphysical properties of water and ice clouds. In early May of 2018, we observed the coincident of strong dust and rain events in the Nile Delta area. We also used Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, NMMB/BSC-dust model and NOAA-18 Cloud Height product to validate this same movement of dust and clouds that induced precipitation. In addition, Niño 3.4 index with historical rain and dust anomalous events during 1980–2017 were analyzed to reveal the relationship between these events and global climate system. The results revealed an obvious matching in both intensity and movement for both dust and precipitation events in early May 2018. Extreme precipitation anomalies during 1990–1999 occurred in company with positive Niño indices and less dust events.

The Orbiting Arid Subsurface and Ice Sheet Sounder (OASIS) mission concept is a single instrument, small-size, venture-class mission directly aimed at exploring the signatures of climate change in both cold and warm deserts regions on Earth: the polar ice sheets and the hyper-arid deserts (Fig. 1). OASIS has two well-defined science objectives. The first is to determine the thickness, inner structure, and basal boundary conditions of Earth’s ice sheets to understand their dynamics and improve models of current and future ice sheet response to climate change and, hence, to better constrain ice sheet contribution to sea level rise. The second objective is to perform detailed mapping of the spatial distribution of shallow (<100 m deep) aquifers in North Africa and the Arabian Peninisula to understand groundwater dynamic in fossil aquifers to assess their current response to climatic stresses and paleoclimatic conditions that formed them. These two mission objectives are achieved using a sounding radar operating at 45 MHz center frequency with 10 MHz bandwidth. The proposed OASIS radar would be able to map only the upper water table of fossil aquifer systems. This proceeding has been updated from the proceeding published in IEEE-IGARSS (2013).

Modeling and predicting geohazards is extremely difficult due to their complex behavior in the real-world. In fact, several aspects of these environmental applications are considered in computer-based modeling to accurately estimating real-world phenomena. Till date, none of the proposed methods have reached to zero uncertainties or errors to recognize the entire disaster’s events. Globally, many people have lost their lives due to various types of natural hazards. Therefore, it is important to detect, monitor and predict them to protect the inhabitants against the potential natural hazards that threaten human lives and properties. Recently, artificial intelligent (AI) methods have received a great deal of attraction due to their precision to model the complex problems such as natural hazards. AI can see different aspects of a complex problem with sufficient iteration and details. In recent years, implementation of AI models coupled with geospatial information systems (GIS) are the most efficient and accurate approach to model natural disasters i.e. flooding, earthquake, landslides, forest fire and drought rather than other existing methods. This gives an insight into the ability of applied AI models in some natural hazards applications.

A three-dimensional cartogram is a thematic 3D map on which the volume of each region is linearly proportional to an extensive property enclosed within. This work formulated a 3D cartogram using a linear diffusion process. The 3D cartogram algorithm may be applied to a uniform Cartesian grid such that the distribution of the property of interest, such as the hydrocarbon pore volume in each cell, is equalized throughout the transformed domain. The spatial distortion of the grid cells serves as the qualitative indicator of this property, and the color may be used to visualize the spatial distribution of another property, such as permeability. Such a 3D cartogram on which a second property is mapped is a two-variable 3D cartogram.

The present work was prepared in collaboration with the Tunisian Air Company (Tunis Air) for the creation of a GIS application used for the implementation of an EOSID (Engine Out Standard Instrument Departures) process. The study area is Tabarka-Ain Draham International Airport (NW of Tunisia) and the chosen aircraft is the Boeing 737-500. This study presented the procedure to be followed in case of engine failure after takeoff which consists of changes of trajectory made in order to avoid obstacles. This would allow the aircraft to bypass the penalizing obstacles, thus eliminating the need to reduce the maximum take-off weight. The usefulness of GIS is observed in its proposed solutions: the presentation of the terrain in 3 dimensions, the presentation of the simulation of the flight as well as the possible paths to follow. This work will be very useful for aeronautical companies in the field of aviation safety.

Data heterogeneity and multidimensionality are major challenges when dealing with the integration of exploration data sources in the frontier basins. We took advantage of the fact that the geology and geophysics (G & G) do not have boundaries either with the continents or their associated countries. Frontier basins may have generated enormous digital-data on geological structures and reservoirs in areas where the continents and their tectonic plates drifted. The digital data are indeed in Big Data scale, characterized by volumes and varieties in spatial dimensions that may have emerged in the form of conceptualization and contextualization attributes. The frontier basin research thus needs knowledge-based ecosystems, with entities, dimensions and their logical data models interpreted in geo-informatics focus. An integrated framework is proposed, generating a multidimensional metadata structure for meta-knowledge. The meta-knowledge derivable from metadata models can immensely be useful in interpreting the connectivity between G & G events and assessing the hydrocarbon potential of frontier basins. The metadata models are made so flexible as to be extended and implemented in worldwide super basins.

Two landform classifications (Jenness and Shary methods) using spatial statistics and image processing algorithms were performed for the first time in Tunisia. They were applied to the J. Chaambi area (West-central Tunisia). Attempting to highlight geo-morphometric properties of 30 m resolution Digital Elevation Models (DEM), the Jenness classification leads to define ten classes using the ‘Topographical Position Index’ as performed by the module in Arcgis 10.2. The Shary classification is based on a combination of five slope curvature signs producing twelve main landform categories. Plains characterize the main landform type in the first classification; however, convex and concave saddles correspond to the second. The window of perception, in both classification cases, has a major influence on the representation of results. Indeed, the scale effect on classification should be tested by applying both methods to the same area but at different scales.

The assessment of irrigation demand is an important component for an effective water management in the canal command area. The rapid increase in the water scarcity on recent years have been a boon to the farmers who require water for irrigation purposes. Effective water management technique is the utmost required system to schedule the water for irrigation purposes so as to overcome the issues due to water scarcity. But there exist serious issues related to the water management system; they include the availability of real time information on the agricultural land in the command area of the canal, its area, the crop variety and the water demand for each crop. These data cannot be availed that easily as many irrigation schemes are vast and cover about hundreds and sometimes thousands of square miles and have vast numbers of farms; their scale severely limits the effectiveness of the data collection. The present study focused on assessment of the irrigation and agriculture potential for Pullambadi Irrigation project using Geospatial Techniques. The daily weather parameters, the land use/land cover of the region were collected as they serve to be the primary requirements. The study incorporated the data availed from satellite imageries and other possible sources and the water requirement for the years 2015 and 2016 were computed. As per the obtained results, the canal command area required 1069.96 and 965.1 Mm3 for the years 2015 and 2016, respectively.

After the civil war, the electricity sector in Lebanon faced numerous challenges due to the lack of maintenance and funding. Rationing hours increased dramatically and consequently, citizens diverted toward the private sector in order to compensate for this electricity shortage. The majority of the produced electricity is based on burning fuel leading to an extensive pollution. To overcome both the environmental and electrical shortage dilemmas, this paper studied the feasibility of solar energy as a complementary solution. North Lebanon district is the adopted case study. The suitable zone to establish a solar plant is selected scientifically using Geographic Information System (GIS) while taking into account all the restrictions and criteria. The raster analysis overlay method using weighted average sum was implemented. The study succeeded in selecting one appropriate region that can compensate for the North region power deficit.

It is known that the creation of a 3D map has become a necessity for some applications especially in managing of city planning. In this paper, Geographic information system tools were used to create the 3D model; these provide the best visual interpretation of spatial data that supports a project designer in terms of planning and decision processes. This paper presented a simple strategy to create a 3D model for the University of Baghdad, Aljadrya Campus using GIS tools that would be integrated with a SketchUp software. A certain number of reference control stations were created and distributed within the whole study area using GNSS static technique, which is required for a georeferencing process. The methodology of this study also includes an assessment procedure regarding the positional accuracy and length by measuring arbitrary buildings and points in the field work of this study. Then, the resulting 3D model may be employed for clearly planning, creating new buildings and updating specified networks. Moreover, it may be used for getting positioning coordinates and length that have an accuracy level of less than 10 cm in addition to the ability to utilize it by new students for exact inference of locations. It is worth mentioning that the whole work of this study was uploaded to the GIS website online for use as an open source data.

The study examined the relationship between seasonal vertical loading deformation and seasonal hydrological loading from precipitation specified as rain and snow. The vertical loading deformation is characterized by time-series estimated from continuous Global Positioning System (GPS) network across the contiguous United States for a timeframe of 48 months (January 1st, 2013 to December 31st, 2016). The data processing used custom-built R scripts and spatial libraries that were integrated with Hive framework which is a data warehouse extension of Apache Hadoop that is used as a database query interface. The relationships of vertical displacement were explored by visualization techniques such as spatial maps and wavelet coherence plots.

The purpose of this work was to characterize the signals and noises of Geocenter variations time series obtained from different space geodesy techniques as Global Positioning System (GPS), Doppler Orbitography and Radiopositioning Integrated on Satellite (DORIS), and Satellite Laser Ranging (SLR). The methodology proposed is based on the estimation of periodic signals by performing the frequency analysis using FAMOUS software (Frequency Analysis Mapping On Unusual Sampling) and evaluation of noises level and type by Allan variance technique. The available data covers 13 years (from 1993 to 2006) of weekly series of Geocenter residuals components, according to ITRF2000. The results obtained are more accurate according to GPS and SLR of about 2–8 mm than DORIS of about 8–42 mm. The estimated seasonal signals amplitudes are of about of few mm per technique with centimetre level for Z Geocenter coordinate of DORIS. The Geocenter motion derived from the SLR technique is more accurate and close to the geodynamic models. The noise analysis shows a dominant white noise in the SLR and DORIS solutions at level of 0.6–1 mm and 10–40 mm, respectively. However, the GPS solution is characterized by a flicker noise at the millimetre level, relating to mismodelling systematic errors.

This work described the operational capabilities of a platform developed to assist decision makers to determine the optimal location and configuration of desalination plants to be powered by renewable energy sources. The platform, called DESiRES, operates under an open source geographical information system interface. Interoperability with geostatictical and optimization design algorithm modules was achieved via the location based information provided by the geographical module. The current stage of DESiRES platform development (i.e., study areas, data used, etc.) was presented along with the basic platform functionalities, showing a great potential for design and assessment of desalination in the Mediterranean region.

The objective of this study was to propose concepts allowing the establishment of an interactive system of decision support adapted to the conduct of territorial processes relative to the challenges of the different phases of decision making. To support effectively the problem of Territorial Planning which consists in the search for a surface satisfying at best some criteria among a set of variants, Geographical Information Systems (GIS) and existing geotechnical data were analyzed and aggregated using MultiCriteria Decision Support (AMCD), namely the Simple Additive Weighting method (SAW) and the Analytic Hierarchy Process method (AHP). The combination of the two methods allowed us to make a concordance map to select the best suitable sites.

Building segmentation of aerial images in urban areas is of great importance for many applications, such as navigation, change detection, areal monitoring and urban planning. However, due to the uncertainties involved in images, a detailed and effective solution is still critical for further applications. In this paper, we proposed a novel deep convolutional neural network for building segmentation of aerial images in urban areas, which was based on the down-sampling-then-up-sampling architecture. The suggested network is similar to that of the FCN, but with ours differs as it takes into account the multi-scale features using Atrous Spatial Pyramid Pooling. Additionally, motivated by the recent published works, the depth-wise separable convolution was also adopted to replace the standard convolution in our proposed method, which largely reduced the training parameters. To evaluate the performance of our proposed method, a very high resolution aerial image dataset (0.075 m) was used to train and test the images. In addition, two state-of-the-art methods named FCN-8s and SegNet were also compared with our method for performance evaluations. The experiments demonstrated that our method outperformed the state-of-the-art methods greatly both in terms of qualitative and quantitative performance.

For proper monitoring and scheduling of the supply of drinking water in reservoirs, it is necessary to carry out the capacity surveys. Remote sensing techniques can be used to estimate the capacity of the reservoirs in an inexpensive and less laborious way. In this paper, a super resolution mapping based on hybrid approach was developed and applied to Landsat OLI image of the Puzhal reservoir, Chennai city, southern India and the reservoir water-spread area was estimated. The estimated water-spread was used to find the capacity of the reservoir using Trapezoidal formula. The hybrid approach uses New Fuzzy Cluster Centroid (NFCC) algorithm for sub-pixel mapping and multi-objective genetic algorithm for super resolution mapping. The super resolution mapping is an advanced classification technique which accurately maps the location of classes within a pixel. The capacity determined from the image processing technique is compared with that estimated from the field survey data with a meagre 1.35% error. Hence, it is observed that the super resolution mapping is a prominent methodology to estimate the water-spread area of the reservoir which in turn increases the accuracy of the estimation capacity of the reservoir.

This paper proposed a novel observation system that is based on multi sources of collected data for urban extension detection. In addition to the satellite image processing, the evolution of unmanned aerial vehicle (UAV) technology created a practical data source for image classification and mapping. For the detected data analysis, storage and processing, a big data framework for urban extension detection was presented. In this Framework, Deep Learning (DL) algorithms were used for the classification and the analysis of multi source images.

Photogrammetry is the use of two dimensional (2D) images to provide measurement data. Photogrammetry uses a procedure referred to as “Structure from Motion” (SfM) to solve feature positions within a defined coordinate system. SfM photogrammetry offers the possibility of fast, automated and low-cost 3D data acquisition. The aim of this study was to investigate some applications of 3D reconstruction of residential areas generated by SfM photogrammetry. 3 different sites were reconstructed by 3 different methods using a UAV system. Each method was examined according to its advantages and drawbacks in the light of the SfM photogrammetry.

Throughout the Quaternary, the Arabian Peninsula witnessed alternation between dry and wet periods. During the wet periods, annual precipitation significantly increased leading to recharging the fossil aquifers, elevation of the groundwater table and development of drainage systems and waterfalls along the Central Arabian Arch (CAA). Landforms that were left behind these periods provide valuable clues for deciphering the paleohydrology and paleoclimate of the Arabian Desert. Herein we used logistic regression modeling with topographic analysis to delineate theater-headed valleys (THV) along the CAA. These valleys have peculiar morphometry (e.g. theater-like heads, stubby-looking geometry, U-shaped profiles, occurrence along escarpments and propagation along fractures) and are largely attributed to groundwater sapping and/or megaflooding and water fall erosion. The model uses different topographic datasets including stream power index, plan and profile curvatures, stream density and slope gradient. The model results show that THV are abundant along the plateau escarpments and deep fluvially-incised valleys in the CAA. Additional geological, hydrological and field assessments are required to further characterize the origin of THV and examine the respective roles of groundwater processes and surface water activities in shaping the CAA landscape. This paper presents a novel mapping approach of landforms on a regional scale.

At present, a number of newly satellite-derived precipitation estimates are freely available for exploration and could benefit the hydrological community. This study aimed to evaluate the Tropical Rainfall Measuring Mission TRMM 3B42 rainfall estimate algorithm in forest basins in Northern Tunisia. We selected 77 events, with 50 mm/day heavy rainfall as selection criteria, for at least one station of the study area observed during 2007, 2008 and 2009. Rainfall stations were interpolated using the inverse distance method. Results were discussed in terms of the TRMM product accuracy in comparison with rain gauges over the forestry zone (169 stations). The Pearson’s correlation coefficient between satellite estimations and ground maps reached 0.7 for some events and were weak for others. The comparison results of TRMM algorithm over forestry zone within Northern Tunisia shows a weak difference in terms of false alarm ratio (FAR), and bias. However, it shows a better detection for the whole of Northern Tunisia in terms of correlation coefficient and the probability of detection (POD). Some uncertainties have been found, across the TRMM algorithm over forestry region. Thus, the evaluation of satellite algorithms before use as input for other models is recommended.

The application of space techniques to geological studies is an important source of information for geological mapping. The optimal exploitation of high spatial resolution satellite imagery can contribute to the improvement of the geological map, particularly when it comes to mapping in arid and desert areas where outcrops are often inaccessible. Indeed, the region of Djanet, main object of our study, is characterized by rather important reliefs and this made the access difficult or sometimes even impossible in certain zones. The study area is located at the northeastern end of East Hoggar. It covers the terrane of Djanet and part of the terrane of Edembo. In addition, this region is probably one of the least explored areas of the Hoggar. This is why different remote sensing techniques have been used to make geological mapping faster and more efficient. The obtained images were subject to various treatments ranging from contrast enhancements to spectral enhancements, and using images in 742 RGB color compositions and calculation processing of bands (3/1, 5/4, 7/5) and the (5/7, 2/1, 4/2), Thus, the analysis of the processed Landsat-7 ETM + images from the Djanet area allowed us to establish a tele-analytical lithological map. The correlation of this map with the field data allowed us to check the validity and the correspondence of the different facies and especially to clarify the lithological contours derived from the analysis of Landsat satellite data.

Because of the importance of the Marshlands and their vital role in economic and environmental aspects in the south of Iraq, it is necessary to know the changes in their land cover, especially water and natural vegetation for current period depending on image processing techniques for Landsat images. Hence, this research aimed to produce a land cover map for the marshes area using GIS and remotely sensed data. In this research, Landsat satellite images and set of maps were used to analyze and extract the information using GIS and remote sensing techniques. Two essential indices were extracted from satellite images: Transformed Normalized Difference Vegetation Index (TNDVI) and Normalized Difference Water Index (NDWI). A supervised classification was also used to produce a land cover map for the study area. After conducting all the necessary analyses, the results showed that there is an increase in the surface area of waters and natural vegetation of the marshes in the current period.

This research investigated soil texture information extraction in agricultural soil using SAR imagery of C band (5.36 GHz) frequency. The soil backscattering coefficient ( $$\sigma_{soil}^{o}$$ ) could act as an effective estimator to the relative percentage of sand, silt, and clay when the influence of vegetation is considerably reduced from the total backscattering energy ( $$\sigma_{total}^{o}$$ ). The contribution of vegetation in the SAR imageries of VV ( $$\sigma_{vv}^{o}$$ ) and VH ( $$\sigma_{vh}^{o}$$ ) polarization has been significantly reduced by Water Cloud Model, and Dual polarized SAR Vegetation Index. One of the target parameters, namely roughness (hrms), was derived from the cross-polarization ratio between $$\sigma_{vh - soil}^{o}$$ , and $$\sigma_{vv - soil}^{o}$$ and Dielectric Constant ( $$\varepsilon_{soil}^{{\prime }}$$ ) was obtained from the modified Dubois model. The extracted target parameter such as hrms is adequately correlated with in situ Sand texture measurements (R2 = 0.81) and, $$\varepsilon_{soil}^{{\prime }}$$ was sufficiently correlated with in situ Clay measurements (R2 = 0.78). The positively correlated regions of the correlation coefficient (CC) analysis between hrms and $$\varepsilon_{soil}^{{\prime }}$$ were extracted and thus represented the percentage of silt with reasonable accuracy (R2 = 0.77). From the soil triangle formed with three estimated parameters, we found that the Clay category shared around 36% of the total area followed by Sandy loam (24%) and loamy sand (19%).

In the last 20 years, a number of Earth observation satellite missions has been launched, resulting in a considerable improvement in the spatiotemporal capabilities of applied remote sensing. In order to survey the main recent flooding events in Chott El Jerid, Tunisia, dated to April 2007, September 2009, June 2014 and February 2015, time-series of MODIS-Terra data have been used. The maximum surface area of the water bodies reached 660 km2 in 2007 and 2014. The flooding event of September 2009 was the longest event (88 days) compared to only 35 days for the exceptional June event in 2014. The daily acquisition of images, the spectral range and the large swath of the sensor are well-adapted to estimate the extent of the ephemeral lake in an area that is difficult to investigate in the field. It remains difficult to decipher the part of water from direct precipitation, runoff from the reliefs and aquifer resurgence.

Remote Sensing Change Detection is designed to detect stand-replacing disturbances such as land cover, harvest and wildfire. Digital change detection essentially jnvolves the quantification of temporal phenomena from multi-date imagery. The main purpose of this study was to distinguish change in land cover within each land cover type (class), and to find the real changes on the land cover features between 1996 and 2016 in the region of Gabes, Tunisia. Two images were downloaded from Google Earth and then georeferenced and masked out the study area, which is imported since Kml File from Google Earth is too. The two images were also enhanced and then classified using the Maximum Likelihood algorithm. Five classes were identified: water, settlements, vegetated area, bare soil and zone under development. The supervised classification was assessed with 94% for 1996 imagery and 95.5% for 2016. The result showed a decrease in bare soil class (from 115 to 94 km2) and an important increase in zone under development (from 96 to 834 km2). Regarding the water, the vegetated area and settlement classes, a slight increase was noted.

Over the past several decades, the conversion of native forest to tea plantation and crop land has accelerated across the Coonoor watershed in Nilgiris. It is notable that the present study explored the severity prevalent in the land cover changes including deforestation activities at Coonoor watershed region as a result of urbanization, recreation parks, resorts and tea plantation development. The Revised Universal Soil Loss Equation (RUSLE) is one of the most widely used soil erosion model which estimates the average soil loss over a long-term period. This paper therefore, imprinted the impact of land use changes on land degradation and the consequent vital phenomenon like the soil erosion. The change detection was carried out between the periods of 2005–2018. Landsat images of corresponding periods were classified using supervised classification technique and also Normalized Differential Vegetation Index (NDVI) was computed for the determination of C-factor (cover and management factor) for the corresponding periods. Conversion of forest land into tea plantation, wasteland and settlement significantly decreases the soil organic matter (SOM) and hydraulic conductivity (HC) of the soil, which leads to different K-factor (soil erodibility factor) for the study duration, whereas the R-factor (rainfall and runoff factor) and LS-factor (length-slope factor) are considered to be constant throughout the period. The results of this study indicates via promising results, that the total sediment yield of the study area has remarkably increased due to land use/cover changes. The most significant rise in soil erosion was found evitable in the deforested region where there has occurred a changeover from forest/orchard to infrastructure and wasteland.

The urban heat island (UHI) phenomenon has gained increasing attention being an indicator of the anthropogenic activities’ effects on urban areas. Moreover, the establishment of the sustainable development goals (SDGs) for urban areas underscores the need to monitor the UHI phenomenon. This paper explores the influence of land use type and population density on UHI intensity using two Saudi cities, Jeddah and Yanbu, as case studies. Landsat images from 1990 to 2015 were used to extract the land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) of the study areas. Statistical measures and analysis of variance (ANOVA) were used to examine the variations in LST due to land use type. The correlations between LST and the indices (NDVI and NDBI) and population density were also computed. The results show variations in LST due to land use type (at 0.01 level of significance) and the differences in the thermal regimes of the two cities. The population density is positively correlated with LST with R2 varying from 0.3 to 0.87 for Jeddah and Yanbu from 1991 to 2016.

Rapid growth in urbanized areas is a worldwide phenomenon. The rate of urban growth is very fast in developing countries like Iraq. This study illustrated urbanized area development in Sulaimaniyah Governorate from 2001 to 2017 using different Landsat imagery, Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI). The Environment for visualizing images ENVI 5.3 and GIS software was utilized for image pre-processing, calibration and classification. The Maximum likelihood method was used in the accurately extracted solution information from geospatial Landsat satellite imagery of different periods. The Landsat images from the study area were categorized into six different classes. These are: forest, vegetation, rock, soil, built up and water body. Land cover variation and land use change detection in the area were calculated for over a 17 year period. The Change detection Analysis shows an explosive demographic shift in the urban area with a record of +8.99% which is equivalent to 51.80 km2 over a 17 years period and the vegetation area increased with 214 km2. On the other hand, soil area was reduced by 257.87 km2. This work will help urban planners in the future development of the city.

Multidate satellite images have been used in this study to detect the trends of environmental changes in Qarun Lake, This study explored the applications of remote sensing and Geographical Information Systems (GIS) in the collection of information and analysis of data. NDWI index was calculated to detect and analyze the change detection of Qarun Lake coastal area. The results revealed that the overall size of the water decreased by about 5% and island area decreased by around 9% from 1972 until 2017. This could be due to the increase of the amount of drainage water to El-Rayan Depression.

Water sources have been severely contaminated by heavy metals (HM) in Imo river basin due to different land uses including industrialization and intensive agriculture. Six land use types were identified using GIS and water samples were collected from both surface and underground water sources to test the HM concentrations in different land uses. Geostatistical tools such as interpolation (Kriging) and regressions were used to determine the extent of chemical concentrations, and their relationships with the land use types. Higher concentrations of the heavy metals (NO3, Cr, and Pb) were observed in the center (middle stream watershed) around the urban built and grassland areas. Downstream watershed (wetlands areas) had low concentrations of the HM except Fe. The water quality in the built-up industrial areas were found to be of poor quality relative to other parts in the study area. The findings of this work will support the Federal Ministries of Water resources, Agriculture, Environment in sustainable decision making towards reducing pollutants and restoring the river basin and its catchments.

This paper described an experiment of developing a complex technique for satellite imagery time series processing when estimating spatial distribution of framing (or changing) calendar dates of the growing seasons. Particularly, we reflected on allocation of growing season framing dates in conditions of unstable weather. As surface air temperature may fluctuate in many cases around bordering values during some days or weeks, the allocation of stable crossing of temperature through the control values (that marks time frames of growing seasons) is a fundamental problem in the case of ground observations. We compared some results of the growing season frames allocation based on ground data observations of the temperature (needed for the verification and calibration purposes), and the estimation results made relying on the data of remotely observed Normalized Difference Water Index (NDWI).

Monitoring variations in the spectral reflectance of thermal bands of Landsat data provide land surface temperature information of earth’s surface features. This research tried to examine the variations of Land surface temperature (LST) from 1987 to 2017 at the Gomishan district and its soundings in Iran. Images preprocessing was conducted including the geo-shifting and atmospheric correction. NDVI and LST maps and their change map using a change detection technique were generated. Basic inferential statistics and spatial analysis were performed. The results show that LST mean reached approximately 42.5 °C with 9 °C increase, while it was 33.8 °C in 1987. However, comparing the statistical analysis of NDVI data did not show any differences between the two study dates. Land cover classes include water, urban, and rural covered areas had the lowest LST shifts between the two study periods. The LST of rangelands, wetlands, and bare lands with more than 10 °C increase have experienced considerable LST shifts between during the study periods. Interestingly, some parts of wetland areas had the highest increase approximately 13 °C from 1987 to 2017. This study emphasized that LST change detection approach and spatial analysis can be used successfully in LST monitoring investigations. The results can be used to identify regions that experienced LST shifts (change or no change) and also to identify the most critical and impacted areas. The obtained results can be used effectively in sustainable natural disaster management plans.

The recent introduction of low cost small unmanned aircraft systems (sUAS) to remote sensing has provided a significant improvement in the quantity and quality of high resolution imagery. The purpose of this research was to describe the acquisition of very high resolution imagery using sUAS (drones) and assess the effectiveness of spectral-based classification for distinguishing vegetation (species, total cover), percent bare ground, litter, and rock from this data. Images were obtained from a semiarid rangeland site in central Nevada, USA. Flight missions were flown 15 m above ground level using automated flight paths, and individual images were processed into orthomosaics using the Pix4D software. Features were classified using a spectral unsupervised classification. Ground-based measurements were collected in the field to compare rangeland structure with generated classification output. Results indicate that very high resolution imagery can be effectively used to assess rangeland ecosystems that can aid in rangeland assessment and monitoring. The ability to use sUAS to monitor ecosystem structure and condition can be an important resource for rangeland managers, as they improve their ability to access high quality data for making informed management decisions within and across multiple years.

Surface modeling is the process of creating a 3D representation of any surface by manipulating polygons, vertices, and edges in three dimensions. The 3D model represents a physical body using a collection of 3D points in space and connected by several geometric entities. These processes mainly depend on the scenario used to generate the 3D points and the filtering methodology. Lidar is an active remote sensing technique, which has rapidly developed over the last decades to remotely determine the geometry of the Earth’s surface in a rapid and accurate way. However, the FWF-lidar system provides extra information for better 3D digital representation of the features and further improves the modelling for different applications. This paper discussed the correlation between FWF-lidar physical information and the potential to improve the quality of surface modeling. It also discusses the improvements in geometric point quality when integrating pulse width value in the filtering process based on a developed filtering scenario. In this scenario the pulse width value is used as an index to distinguish surface features and improve geometric filtering process. The scenario was tested and analyzed in vegetated and urban areas to show the improvements. The results show decreasing discrepancies between overlapping flight lines in terms of mean and STD values after integrating the pulse width values following Gaussian modeling.

Ground-Penetrating Radar (GPR) data analysis provides quantitative information about the objects buried in a medium, such as their sizes or depths. This is achieved by studying the wave reflections caused by the electromagnetic contrasts in the medium. This paper proposed an innovative technique for the determination of the radius of a buried cylindrical object based on the fitting of selected reflected points in the GPR trace to a geometric model. The performances of this technique were evaluated using data generated by a GPR simulator, gprMax, which uses the Finite-Difference Time-Domain (FDTD) method. Simulation results show that our technique can estimate the radius of the buried cylinder with Mean Absolute Percentage Error (MAPE) of 0.5%. A comparative study of our technique with another one from the literature shows a higher accuracy of our technique with radius estimation carried out in simpler steps, reflecting its robustness.

The purpose of this research was to detect landslide features using a light detection and ranging (LiDAR)-based digital elevation model (DEM) and back-propagation neural network (BPNN). The study area is in north-east of Taiwan. A high-resolution LiDAR-based DEM was used. Six training and four testing data sets were selected and manually digitized on landslide features were used as ground truth data. The relationship between landslide features and six trigger factors (slope angle, area solar radiation, roughness, profile curvature, plan curvature, and topographic wetness index) was computed from the LiDAR-based DEM. The experimental results indicated that the overall accuracy and kappa accuracy of the classification of landslide features using the BPNN algorithm were 0.950 and 0.772, respectively.

The estimation of accurate, fast and up-to-date building and building floor data is inevitable for 3D urban map projects. These maps mostly required for tracking building construction speed, monitoring horizontal and vertical urban growth and illegal constructions, updating building inventories, preparing feasible urban plans, assessment of hazard and risk and creating infrastructure plans. Up-To-Date the passive remote sensing technologies are to detect and map urban features and to obtain land use and land cover maps. They can record large and continuous land cover information in urban areas. The buildings can be perceived as different from each other in urban areas by using high-resolution remote sensing images. For this reason, high-resolution satellite imagery is very useful for obtaining areas and locations of buildings that are difficult to identify compared to medium resolution satellite imagery. However, these systems have several constraints in creating 3D maps of urban features and detecting urban building heights. Active sensors can overcome some of these constraints when used together with passive systems. These systems create highly accurate 3D height maps for buildings, therefore, can be used to estimate an accurate floor value for each building in urban areas. 3D building detection studies have shown that the Lidar technique is promising and suitable for 3D object detection. In this study, a combination of aerial photograph and Lidar data were used to produce individual building heights and then estimate building floor in the urban central part of Kucukcekmece, Istanbul. The accuracy of the proposed algorithm was evaluated for each building floor using ground truth data and has proved an overall accuracy of 79% and a kappa equal to 0.74 which is a promising result.

Determination of up to date 3D maps of urban areas and estimation of building height has crucial importance for variety of disciplines such as: city regional planning, architecture, construction industry, population directorates and planning units. In order to track information for building construction speed and illegal constructions, updating building inventories, preparing feasible urban plans 3D maps of urban areas is important to get. Unmanned aerial vehicle (UAV) is promising and suitable for 3D objects detection. These systems create high accurate 3D height maps for buildings and, therefore, can be used to estimate accurate building boundaries in urban areas. In this study, SenseFly eBee RTK Unmanned aerial vehicle (UAV) was used to obtain 3D data. The aerial photographs obtained with the UAV were processed in order to create a three-dimensional point cloud. By processing the point cloud data Digital surface model (DSM) and digital elevation model (DEM), were created. In order to determine the buildings’ height, Normalized Digital Surface Model (nDSM) was formed by removing DSM from DEM. In order to determine building boundaries, high resolution aerial photographs obtained from the unmanned aerial vehicle (UAV) were classified using machine learning algorithms and support vector machines. After classification, we obtained 160 buildings. Then we estimated the buildings floor height for the selected ten buildings.

Accurate methods for biomass estimation is necessary for numerous topics related to global warming. Amongst different component of trees, crown biomass is the most difficult to measure. In this study, we tested a simple approach using a hand-held consumer grade camera to estimate the biomass of different components of crown including large and small branches and leaves. Two perpendicular images were taken from 36 Oak trees and the trees were cut down and fresh weight of components was measured in the field. Biomass was calculated for each component by multiplying fresh weight and density of each component. For the estimation of biomass using terrestrial photogrammetric method, pixels of each component were separated and used as predictor in regression equations. Biomass of each component were estimated and bias and RMSE were calculated. Based on the result, this approach provided the most accurate results for medium size trees. In general, the bias and RMSE of total crown biomass estimation were 1.45 and 4.64, respectively. Also, the accuracy of biomass estimation of large branches was the highest while that of leaves biomass was the lowest. However, the density of the stands and the size of trees are two important factors that limit the applicability of this approach.

Predicting fire hazards and simulating fire intensity require knowledge of fuel conditions. Many aspects of wildfire behavior including the rate of spread and intensity are influenced by the amount of vegetation that fuels the fire. Coppice Oak Forests (COF) are strongly influenced by wildfires. In the present study, we examined the ability of airborne LiDAR data to retrieve available canopy fuel (ACF) of coppice Oak forest in Zagros Mountains, Iran. Two different oak-dominated stands were selected based on the stand density including sparse and dense forests. Systematically, 127 plots were established in the field and ACF was calculated using species-specific allometric equations. An outlier filter was used to remove any outlier pulse from the point clouds. Canopy Height Models (CHM) were generated by subtracting DSM and DTM. Different metrics were calculated from CHMs at the plot locations. Linear regression (LR), Artificial Neural Networks (ANN), Boosted Random Forest (BRF), and K-Nearest Neighbor (KNN) were used for modeling. The result showed that there is a strong correlation between ACF and LIDAR-derived metrics (r2 = 0.74 − 0.79). BRF was the best modeling technique. ACF was estimated more accurately in the sparse stand (r2 = 0.79). LIDAR-based predictions can be used to map ACF over coppice oak forests.

Volumes of geological and geophysical (G & G) data sources exist in the Nile Delta basin, covering approximately 60,000 km2 in both onshore and offshore areas. Although several varieties of data continue to support the exploration and field development activities in the basin, the Big Data sources are largely unstructured, heterogeneous and multidimensional. Connecting various G & G events of onshore-transition-offshore zones and integrating them into a single repository is a complicated process. The authors proposed a holistic data warehousing and mining methodology that can support logical and physical data organization, easing the data integration process in the warehouse repository. In addition, an implementable framework, the Nile Delta Digital Petroleum Ecosystem (NDDPE) was articulated, assessing its usability and interoperability with associated data artefacts. The NDDPE can deliver Big Data guided digital ecosystem solutions that can minimize the risk of exploration.

ASTER observes the thermal infrared (TIR; 7–14 µm) spectral radiation from the Earth at five bands where the major terrestrial minerals exhibit characteristic spectral properties. The lithological and mineralogical indices for ASTER-TIR proposed by the author have been proved to be quite effective and robust in detecting silica, carbonate, sulfate and mafic-ultramafic minerals as well as delineating silicate rocks. High quality mapping results with the indices have been supplied in the scales of local to regional with the developed procedures for efficient selection of well-conditioning scenes in the vast and expanding archive of ASTER data for mosaicking. However, the procedures are not efficient enough for global mapping. On the other hand, the global emissivity dataset version 3 (GEDv3) are developed and recently supplied to the public by one of the ASTER data providers. In this study, the global map generated with applying the indices to the GEDv3 was shown. It is compared with the regional map covering a part of Arabian Peninsula produced with the procedures developed by the author. The result indicates the GED-based map revealing the rough trend of lithology and mineralogy on Earth, however, the quality is much lower compared to the regional map produced with the author’s procedures.

The visual and interaction tools are useful in providing decision-makers with a comprehensive overview of multifaceted information gathered from multiple sources. Petroleum Data Management (PDM) is getting more complex due to the diversity in data providers as well as multiple sources, such as well logs, seismic sections, GIS databases, subsurface 3D cross-sections and other forms of technical reports. This research attempts to introduce Geovisual Analytics as a suitable interactive approach for petroleum exploration decision support. The paper will overview research issues and provide implicit insight on research agenda in this important earth science domain.

The estimation and modeling of carbonate porosity is of increasing interest in different aspects of geology. Several models have been developed to visualize the pore network systems of carbonate rocks. However, no modeling tools have been designed to predict changes in pore system resulting from dissolution. Therefore, this paper introduced an algorithm for predicting spatial variations in pore network. Carbonate outcropped samples representing different facies from Subis limestone, Sarawak, of Miocene age were used for this study. Continuous imagery along a 10 cm rock chip was conducted using Micro Computer Tomography (CT) scan imagery. The Artificial Neural Network (ANN) predictive code receives images which were read as a matrix. The images were processed using the Image Analysis, coded before use as a training and input data set for ANN. The ANN produced a predicted image with the same properties (such as bits, scalar or raster …etc.) as the input images and at the same interval. The predicted image was compared to the original one to estimate the prediction accuracy. The method proved to give good results in terms of the predicted images accuracy. The method can be applied to study the dissolution phenomenon in carbonates as well as siliciclastic rocks to predict spatial variations and development in a pore network system.

This study reviewed, captured, analyzed and synthesized all geochemical data of carbonate rocks in South Africa. It then applied the information to determine targets for small-scale cement manufacturing. So far, the geochemical data and the samples locations were not indicated in any open electronic database. In this investigation, the geographic locations were determined and coordinates entered into an electronic database, in which they were linked to the tables of geochemical data. Comparison with the oxide levels necessary for cement manufacture i.e., CaO (≥42%), MgO (≤4%), SiO2 (≤15%), Al2O3 + Fe2O3 (≤16%), silica ratio (SiO2/(Al2O3 + Fe2O3)) and Na2O + K2O (≤0.6%) were then conducted. Sites passing the geochemical tests were considered further after eliminating those locations further than 10 km from vital infrastructure namely roads, rail, electric power, water sources and human population clusters. Of the 709 sites identified, only four met the foregoing criteria used. These fall on the farms Wieduow 309DH, Syferfontein 457KR, Rietfontein 736KS and Rooikop 1590HR. The assessment has also revealed many deposits with inadequate geochemical information. Some of them fall close to the above infrastructural features, and thus warrant the sampling and chemical analyses.

The application of remote sensing and GIS technology has shown a great promise, over the years, and offered opportunities for improving identification of areas that are likely to be locations of lineament and mapping. Based on the ability to identify geological features, Landsat ETM-7 satellite data images were used and band-5 was found as the most suitable for lineament delineation. Parameters such as drainage patterns, previously mapped faults, lineaments, and lithological contact layers were used in this study, to produce a fault potential prediction map using the overlay model technique. The generated fault density map classifies the study area into 5 potential zones, thus, very low, low, moderate, high, and very high potential zones. 33 faults, which may represent new faults in the area of investigation were obtained from the correlation between fault segments and faults data collected from field work. NW–SE is the general orientation of the fractures and N100-1100E was the major trend obtained from fault analyses. Poly-phase ductile-brittle structures such as shear zone and faults in the study area were confirmed by our findings. Iron mineralization in the area is controlled by those structures since they form pathways for mineralizing fluids.

This paper aimed at predicting spatio-temporal variations and new lamination patterns based on available core samples and core images. For this purpose, a scan line method was applied on core photographs showing heterolithic laminations to generate a signal. Then, the generated signal from the scan line was analyzed and interpreted as time series data that can be used in many applications. Samples were taken from different depths to reflect the temporal or vertical variations, and their signal data were compiled into one continuous time series. A new pattern of sedimentary structures represented by new signal data was identified and was converted back into sedimentary structures. The predicted pattern varies in space and depth, and therefore, represents variations on both spatial and temporal bases. The study is therefore significant as the prediction of different patterns can provide information on the depositional environment, paleogeography, and petrophysical properties.

The issue of leakage and spillage of light non-aqueous phase liquids (LNAPLs) and aqueous phase liquids (APLs) contribute to groundwater contamination, resulting in groundwater pollution and rendering the quality of groundwater unsafe for drinking and agricultural use. This paper aimed to investigate the APL and LNAPL in the deformable double-porosity soil, which has become important for sustainability of groundwater utilization and a comprehensive understanding of the characteristics of APL and LNAPL migration into the groundwater through the use of digital image processing techniques. The results of the experiments show that the flow of the APL and LNAPL migration was not uniformly downward. Faster migration occurred at the cracked soil surface condition compared to other locations on the soil surface that were not cracked, even when not using a liquid such as toluene. It was concluded that the factors that significantly influenced the APL and LNAPL migration was the soil sample structure, soil sample fracture pattern, physical interaction bonding between the liquid and soil sample, and capillary pressure of the fluid. The output of this study indicates that digital image analysis can provide detailed information to enable researchers to have better understanding and simulating the pattern of liquids migration characteristics that influence the groundwater resources.

Soil erosion resulting from heavy rainfall, depends on a number of factors, such as the native soil properties (the angle of internal friction, cohesion, unit weight, etc.), hydraulic conditions (surface runoff, groundwater seepage, etc.), the removal of vegetation and change in the surrounding environment. This study investigated the preliminary factors that could cause Soil erosion and detailed the literature through an exhaustive review of the previous published materials on the field and laboratory research works dealing with the soil erosion control techniques.

Learning the spatial distribution of soil organic matter content is essential for the planning of land use and environmental protection. Because laboratory measurement of soil samples is time-consuming and costly, a good alternative is required to estimate spatial content of soil organic matter. This problem can be solved by using remote sensing and GIS techniques. In this study, soil organic matter content was estimated from remote sensing data derived from LandSat8 satellite image by generating a multi linear regression model using the backward regression technique. The multiple regression equation between SOM and remote sensing data was significant with R = 0.678. The resulting multi linear regression equation was then used for the spatial prediction for the entire study area. The predicted SOM derived from remote sensing data was used as auxiliary variable using cokriging spatial interpolation technique. Integrate remote sensing data with cokriging method improves significantly the estimates of surface soil organic matter content.

Soil salinity is one of the most important controlling factors in agricultural production. For an agrarian country like Bangladesh, it is vital to map the saline affected area and keep track of changes. It would be very convenient if the conventional time consuming method of detection soil salinity could be replaced with the application of remote sensing indices. So this study aimed to evaluate the applicability of Landsat 5 TM images, both level 1 and 2 for detecting saline affected areas of Coastal Bangladesh. Seventeen indices were used and compared with the field salinity data collected from SRDI. The R2 values reveal no significant correlation between the aforementioned indices and soil data. So this study concluded that Landsat TM images cannot be used to detect soil salinity in Coastal Bangladesh.

This study used remote sensing tools to quantify spatial dynamics of date palm plantations (DPP) in desert oases of Ziban region (NE Algeria) over the past three decades and understand the impacts of agricultural development on land use-land cover changes. Spatiotemporal changes of DPP (Phoenix dactylifera) were detected using likelihood supervised classification for each of three Landsat satellite images (TM-1984, ETM+-1999 and OLI-2013). The DPP area quadrupled over the last three decades (1984–2013), accounting for about 75.46% of date palm cultivation expansion, while arid rangelands decreased with about 25,932.96 ha as result of political agricultural programs that favored the exploitation of natural habitats into DPP. This study reported the potential of remote sensing imagery processing technique for monitoring and rapidly detecting land change in landscapes of desert oases, with accuracy and a relatively low cost over a long-time period and large scale regions.

Vegetative inventories, whether they are agronomic, ecologic, range, or forestry, often measure plant cover. Vegetation cover is a fundamental parameter in many studies of plant ecology. It is used to measure the surface of the ground exposed to the direct impact of rain drops, sunlight and it is also used to monitor changes in vegetation structure over time. VegMeasure®, an image processing software, was developed to monitor vegetation cover over a period of time, through utilizing green leaf and brightness algorithms as well as K-means classifications. This study demonstrates that digital image processing of vegetation can be fast and affordable, through creating a permanent digital record that can be revisited over time.

This study investigated the linear relationship between the normalized difference vegetation index and total column ozone. Total Column Ozone and Normalized Difference Vegetation index data were obtained from January 2005 to November 2017 from NASA’s AURA and MODIS archives respectively. The obtained results showed a predominant and significant positive linear relationship between the two variables over the West African region. The prevalent practices of bush burning, deforestation, crude oil exploration and other activities will continue to contribute to global warming through an increase in the total column ozone and its precursors.

This paper summarized a new and precise spatio-morphological mapping of cultivable area located at northern-eastern oasis Algerian region affected by land salinization phenomena. We gave qualitative and quantitative views by mapping the presence of salt deposit transported by watersheds, accumulated in the same region and raised to the surface following the Albien’s drainage for the irrigation of the settlements, or by tectonic movement at depth. In order to study a spacious area with a metric precision, processing optical remote sensing data were required. We processed heterogeneous data; from satellites (sentinel-2b multi-spectral) Combined with an old map of the area and field measurements to produce a new and more accurate up-to-date map. The results obtained by applying supervised classifications (Artificial Neural Network) combined with unsupervised (K-means) methods show the amount of salt on cultivable region over the areas of Touggourt. We deduced the risk that the nearest areas (not yet affected by this phenomenon) have to face. Five classes extracted from the satellite image: salt, sand, agricultural land, palm trees, and buildings Show the distribution of salinity levels as well as the cultivability trend of each class. The total salt (salinization) occupation rate of ~24%, which represents 1/4 of the area affecting almost all cultivable and palm classes.

Grasslands are globally important for their economic and environmental services, for these reasons their conservation and their mode of exploitation must be monitored. In this work we wanted to study the relationship between temporal interferometric coherence, radar backscattered coefficient and types of agricultural practices associated with grasslands such as grazing, mowing, and the mixed exploitation of these two practices. High coherence values due to backscattering from the ground were linked to ploughed bare fields and low vegetation height and the backscattering coefficient σo increases with the biomass up to a saturation level. Results revealed that grazing actions are easily detectable especially with the coherence values while mowing date cannot be clearly detected whether by coherence or by backscattered coefficient.

Arid and semi-arid environments characterize the Middle East and North Africa (MENA) region. Climate change posed significant effects in these regions making them drier and suffer water scarcity especially in recent years. In this research, we used the enhanced vegetation index (EVI) as an indicator of this drying trend over the MENA region the past 20 years. We used harmonic analysis model for comparison with observation data to reveal trends in some capital cities within the MENA region, namely, Tunisia, Egypt, Iraq, Saudi Arabia and Iran. The models performed well and different vegetation changes among these countries were observed. The results revealed a changing behavior over different areas among the MENA region. For instance regions such as Iraq and middle Saudi Arabia suffer drier climate, whereas parts of Iran is more humid.

People in mountainous regions are likely to settle on alluvial fans that are primarily composed of accumulated debris flow sediment. Despite their many agricultural benefits, these areas are also highly vulnerable to debris flow hazards. Dabaini catchment, in the Xiaojiang basin, Yunnan province, was chosen as a study area to monitor the long-term change of alluvial fans as well as the corresponding influencing factors. Landsat images of the area from 1987 to 2013 and an object-oriented classification method were used to interpret the debris flow fans in each of these years. Their evolution characteristics revealed that (1) the hazard area of debris flow fans has shrunk during the recent 30 years; (2) the debris flow alluvial fans in Dabaini are affected by the size of the debris flows; (3) less precipitation, sediment source, human activities and higher vegetation are the main factors that contribute to the changes of volume in the alluvial fan in recent years.

The main purpose of the present study was to model gully erosion susceptibility by Evidential Belief Function (EBF) data-driven technique via different scenarios in Maharloo Watershed, Fars Province, Iran. So, according to extensive field surveys, the locations of the head cut, end cut, and also the boundary of the gully locations were identified and the gully inventory map was prepared. Then, different spatial layers such as: elevation, slope degree, plan curvature, TWI, distance from rivers, distance from roads, drainage density, slope aspect, lithology, annual mean rainfall, NDVI, land use, and soil characteristics (pH, clay percent, electrical conductivity (EC), and silt percentage), were identified as effective factors on the occurrence of gullies and their maps were prepared and classified in the GIS software. In the next stage, the correlation among each agent and gully erosion positions was considered using the EBF algorithm. Finally, gully erosion spatial maps were prepared and evaluated using ROC curve. The accuracy of the maps prepared using EBF algorithm by three scenarios was 0.833, 0.756, and 0.809 for the head cut, end cut, and polygon of gully locations, respectively.

This article introduced a novel method for mapping information related to fire events, from a source of Volunteered Geographic Information (VGI) and from Twitter, in particular. As a case study, the fire of North East Attica (August 2017, Greece), was used. The fire event resulted in the burn of 15,000 decares of woodland. Moreover, state of emergency was declared in the region and thousands of citizens who were in the middle of summer vacations were incited to leave from the area of Kalamos, even if they were located at the coastal part. Regarding the methodology, as a first step, all the tweets that were published within 168 h of the fire event and contain relevant information, were collected. Next, they were classified into certain groups the most important of which are: (i) to information regarding fire event tracking, (ii) to the tracking of the consequences and (iii) to the simple identification of the fire event. The geo-referencing of the classified information is performed by using a script written in R. The final output consisted of thematic maps that visualize the classified information.

Sinkhole is not named new marvel in this nation, particularly encompass Klang Valley, Malaysia. Since 1968, the expanding quantities of sinkhole occurrence have been reported in Kuala Lumpur and surrounded areas. As the outcomes, it represents a genuine danger for human lives, resources and structure especially in the capital city of Malaysia. Therefore, a Sinkhole Hazard Model (SHM) was created with incorporation of GIS system by applying Analytical Hierarchical Process (AHP) procedure in order to created sinkhole susceptibility map for the specific territory. Five successive criteria for principle criteria each classified by five sub-classes were chosen for this study which is Lithology (LT), Groundwater Level Decline (WLD), Soil Type (ST), Land Use (LU) and Proximity to Groundwater Wells (PG). An arrangement of relative weights were allotted to each instigating factor and registered through pairwise correlation framework got from expert judgment. Lithology and Groundwater Level Decline has been distinguished gives the highest effect to the sinkhole phenomenon. A sinkhole susceptibility risk zones was grouped into five inclined territories which is very low, low, moderate, high and very high zones. The outcomes acquired were approved with 33 past sinkhole inventory data. This assessment demonstrates that the model shows 61 and 15% of the sinkhole events fall inside high and very high zones respectively. In light of this result, it unmistakably shows that AHP approach is helpful to foresee catastrophic event, for example, sinkhole hazard.

This research presents a two-dimensional flood inundation modelling in urbanized areas when some features such as roads, buildings, and fences have great effect on flood propagation. Wadi Qows located in Jeddah City, Saudi Arabia was chosen as case study area because of the flood occurrence of 2009 causing lots of losses either economic or loss of life. The WMS and HEC-RAS program were used for a hydraulic simulation based on channel geometry built by incorporating urban features into DEM using GIS effectively. A resampling method of DEM 90 × 90 m become 10 × 10 m grid cell sizes was conducted to produce a higher resolution DEM suitable for urban flood inundation modelling. The results show that a higher resolution leads to increasing the average flood depth and decreasing the flood extent. Although the change of the grid cell sizes does not affect its elevation values, this approach is helpful to perform flood simulations in urban areas when high resolution DEM availability is limited. In addition, the integration of WMS, HEC-RAS and GIS are powerful tools for flood modelling in rural, mountainous and urban areas.

Geomorphometric indices such as Amplitude Relief (Ar), Stream length gradient (SL), Drainage density (Dd), Stream frequency (Fu), Slope gradient (S), Aspect and Topographic Wetness Index (TWI) calculated from SRTM-DEM with hydrographic network analysis and density of lineaments (Ld) automatically extracted from Landsat ETM+ satellite image were used to extract lineaments maps of the Sahel of Sfax (eastern Tunisia). The synthesis of these lineaments allowed the establishment of morpho-structural map and understanding the structural evolution of the area. The produced lineament map was then confronted to gravimetric maps and to the previous studies carried out on this area using geophysical data. This map is in high agreement with all these works.

Effective flood risk management requires updated spatial information to ensure that the correct decisions are made. Therefore, developing appropriate responses to prevent surface water flooding is highly required. This paper aimed to map the spatial distribution of vulnerability of communities to flooding, the hazard and the socioeconomic factors including land use that affect people’s exposure to flooding and nature of settlements. In addition, it focused on weights determination using Intelligent Decision System (IDS) by the means of pairwise comparison approach. The results reveal high risk of Manouba Essijoumi in the Northern part and particularly in Sebkhat Essijoumi, corresponding to the urban areas with high rain intensity and especially spontaneous settlements. The results of this study allow a new vision to the urban management schema of the region and propose some efficient strategies of flood risk management.

The development of arid and semi-arid climatic mountain areas is strongly affected by land conservation and the fight against land degradation, which is the consequence of a set of several processes. In this study, we applied the Mediterranean Desertification and Land Use (MEDALUS) method on a mountainous area (the Oued Labiod valley) located in the southern foothills of the Aurès in eastern Algeria. The mapping of the vulnerability of this region to land degradation was developed by crossing four thematic layers (vegetation, climate, soil and management system, and human influence). The application of this method was based on the identification of vulnerable areas by making different parameters that can affect the process of desertification. Spatial analysis is a powerful tool that allows the modeling of each indicator. The cartographic and alphanumerical data are input and structured in a basic data, and analyzed by the Geographic Information Systems GIS. The results provide a document on the spatialization of priority zones and allowed achieving an integrated management of this mountainous area.

Numerous land deformations (land subsidence and fissures) events have been reported from the Central part (Al Qassim) of the Kingdom of Saudi Arabia. An integrated approach (geo-informatics, geologic, and hydrogeology) is adapted to identify areas threatened and affected by land deformations, and also to evaluate the causes of these phenomena. A fourfold approach is applied in this research including; (1) Conducting field visits to collect observations, (2) Constructing spatial correlations in a GIS for the damaged locations which related to the registered spatial datasets (surface and subsurface geology) and temporal datasets (e.g., land use, groundwater extraction, distribution, depth and magnitude of earthquakes), (3) Extracting deformation rates (subsidence) using SBAS radar inter-ferometric technique using ENVISAT data sets, and (4) Correlating the extracted subsidence rates spatially and temporally with GRACE mass variations data. The SBAS investigation revealed high subsidence rates (−5 to −12 mm/yr) along a NW-SE direction, with some subsidence (−2 to −4) in the southwestern part of the study area. This subsidence is correlated with areas witnessing a huge drawdown in the fossil groundwater levels and a depletion in GRACE-derived TWS. Most earth fissures are located around the margins of the subsiding areas and are caused by bending beam activities surrounding the subsiding lands.

Combating desertification includes having an accurate knowledge on the existing land degradation status and the amount of the potential risk. The remoteness, size and harsh nature of the world’s desert make it expensive and difficult to map or monitor this landscape or to determine the effect of the land use on them. Using the combination of Geographic Information system (GIS) and remote sensing (RS) techniques, this study tried to detect the changes in the land surface which may enable us to determine the existing land degradation and identify suitable sites for dams. The thematic maps extracted based on Normalized Differential Vegetation Index (NDVI) and Land Degradation Index (LDI) should be useful to identify and determine the priority of the areas with the highest potential for rainwater harvesting.

Groundwater is depleting at a more rapid rate than its replenishment in Indus Basin due to increased demand attributed to urbanization, inefficient water management practices especially in the agricultural sector and increase in impervious area in the name of development that can expose the country to severe challenge in the future. Through an unregulated groundwater exploitation now farmers often meet inadequacy in surface water supplies. The concurrent use of surface water and groundwater water now takes place on more than 70% of irrigated lands. Therefore, water resources should be monitored on frequent intervals to sensitize policy makers to formulate an optimal framework for water management practices. This study assessed the competence of Gravity Recovery and Climate Experiment Satellite (GRACE)—based estimation of changes in Ground Water Storage (GWS) as a substitute approach for groundwater quantitative approximation for management of groundwater resources in the Indus basin. The GRACE satellite Total Water Storage (TWS) data from 2011 to 2015 was used to calculate GWS. A common reduction trend was seen in the Upper Indus Plain (UIP) where the average net loss of groundwater was observed to be 1701.39 km3 of water amid 2011–2015. A net loss of around 0.34 km3/year groundwater storage was deduced for the UIP where flooding in 2014 assumed a fundamental part in natural replenishment of groundwater aquifer of the UIP. In view of TWS varieties three out of four doabs Bari, Rachna, Thal demonstrated a decrease in groundwater capacity though Chaj doab brought about increment of 0.09 km3. Based on this study, GRACE-Tellus satellite data is competent enough to hint for groundwater storage variations, however there is a vibrant need to calibrate GRACE-Tellus data with hydrological stations data periodically in order to take a maximum advantage for utility of GRACE to monitor groundwater variations on regional scale. Future studies should focus on this aspect.

This study analyzed Groundwater Productivity Potential (GPP) using different models in a geographic information system (GIS) in Okcheon area, Korea. These models used the relationship between groundwater-productivity data, including specific capacity (SPC) and transmissivity (T), and its related hydrogeological factors. Data about related factors, including topography, lineament, geology, forest and soil were constructed to a spatial database. Additionally, T and SPC data were collected from 86 well locations. Then, GPP were mapped using the Logistic Regression (LR) and Boosted Tree Regression (BT) models. The resulting GPP maps were validated using Area Under Curve (AUC) analysis with the well data. The GPP maps using the LR and BT models had accuracies of 85.04 and 81.66% with T value, respectively. And the GPP maps using the LR and BT models had accuracies of 82.22 and 81.53% with SPC value, respectively. These results indicate that LR and BT models can be useful for GPP mapping.

Two case studies, in northern Greece, that monitor surface deformation were presented. Both are related to aquifer overexploitation and its impact on the earth’s surface. Using the archive of ERS and ENVISAT satellites, radar time series were performed applying the PS and SBAS techniques. For the justification of the remote sensing results and the interpretation of the physical mechanism behind the detected deformation, in situ data were also exploited. The areas of focus are: the village of Kalochori, a significant industrial hub, and the Anthemountas basin, an active tectonic region, both located in the vicinity of the metropolitan area of Thessaloniki. The Kalochori case indicated that the aquifer activity is directly affecting the surface movements. A low underground water level in the 90’s caused a subsidence of more than 20 mm/year and the subsequent recharge of the aquifers in the 2000s caused a surface rebound with a rate of up to +12 mm/year. Regarding Anthemountas basin, the subsidence has a maximum rate of −18 mm/year in the 90’s, and the deformation appears to exist over the whole basin. In conclusion both cases reveal that northern Greece is suffering from a hazard imposed by anthropogenic causes.

The satellite based technology adopting the efficacy of Geographical Information System (GIS) plays a dynamic role in groundwater exploration, assessment and management. The current study investigated the demarcation of groundwater potential zones by integrating RS, GIS and Multi-Criteria Analysis for the hard rock terrain of Gundihalla watershed which lies in Bellary district of Karnataka, India. The thematic layers incorporated in this research includes the Geomorphology, Soil, Drainage Density, Lineament Density, Rainfall, and Slope. Saaty’s Analytical Hierarchy Process was used to determine the weights and ranks of all the thematic layers and the significant classes within each layer. All the thematic layers were then integrated to create the groundwater potential zonation map for the study area. The resulting map was categorized into five different groundwater potential zones, viz., ‘very good,’ ‘good,’ ‘moderate,’ ‘poor’ and ‘very poor.’ The area coverage of these zones in the study region are: 263 km2 (18.76%), 332.3 km2 (23.7%), 327 km2 (23.3%), 238 km2 (17%) and 229 km2 (16.3%) respectively.

The current study highlighted the usefulness of satellite images in monitoring and predicting changes occurring on the shorelines through a bi-dimensional data based and situational based strategy. Three coastal areas of the Jeddah city were selected as Salman Bay, Sharm Abhar and Jeddah Port. For the data-based dimension, data collected through satellite images were used in the analysis covering the period 1972–2016. Four regression models were used to study the variation in the coastal borders of the study area. Predictions for the next 9 years (up to 2025) were carried out using the four Regression models. The results of the findings revealed that shrinkage has been witnessed in all areas under study. Another fact came to the limelight is the proximity of the objective results with expectations of the experts thus providing credence to the appropriateness of the used statistical models. For the situational based dimension, the effect of various anthropogenic activities and geo-environmental natural processes in the study area were identified. Based on the study findings, a continuous monitoring of the coastal areas is suggested along with maintaining a concrete database. The proposed techniques can be extended to study the coastal shrinkage and extension in other regions as well.

Satellite remote sensing is considered a promising technique for studying some phytoplanktonic algae because of such advantages as large-scale, real-time and long-term monitoring. The application of statistical models in the field of remote sensing is a crucial tool. The main objective of this study was to quantify the spatial distribution, and develop an empirical model, to detect phytoplankton algal density (diatoms and dinoflagellate). We used ratios of transformed reflectance values (REF) from Landsat Operational Land Imager (OLI) data to establish statistical relationships to dinoflagellate and diatoms densities cells, in the coastal area of Algiers Bay in Algeria. Another additional advantage of our study is that in situ measurements, it coincides with the passage of the satellite at the same time. The result shows that the proliferation prediction model could predict diatoms algae with an accuracy of 77%. The results of this research provided the possibility for the development of an appropriate methodology for remote monitoring of this phytoplankton types in coastal water.

In response to the sub-surface basement fault reactivation, few coast-parallel shallow-depth normal faults have been nucleated in the Quaternary cover sediments along the coastal plain of Odisha. Lack of exposure, extreme flatness of the plain, intense fluvio-aeolian and anthropogenic activities makes mapping of these faults difficult. Therefore, fluvial response to faulting like stream convergence, channel offset, variation in sinuosity, initiation of new streams recognized by remote sensing have been used for marking the position of these faults. The Jajpur fault offsets the Baitarani and Kharsuan rivers ~1.4 km to NE and ~4 km to SW, respectively. The Gop fault offsets the Kushabhadra River ~4 km to SW. Channel sinuosity varies from 1.3 to 1.5 on the up-thrown and down-thrown blocks across the Jajpur fault and that of the Gop fault is 1.8 and 2.1, respectively. Exaggerated DEM around these faults shows significant relief break. GPR imaging across these faults confirms their subsurface continuity with wide zones of displacement consisting of several synthetic normal faults.

Coastal areas are likely to be eroded or submerged and this can be accentuated by the effect of global warming; yet some coasts are more vulnerable than others to natural or man-made hazards. The improvement of knowledge on coastal risks inevitably involves a better cartographic coverage conveying and locating the different dynamic themes directly or indirectly affecting the coastline. The coastal vulnerability and exposure Index is one of these themes and it aims to move from the general detail to a holistic model so as to allow decision-makers to take the right decision in terms of optimizing the intervention procedures provided by the regulations in force, especially the rationalization of expenses when renting classified sites vulnerable by the coastal vulnerability and exposure Index.

Oil extraction and transportation practices have turned the Gulf region into one of the most polluted sea areas in the world. In this context, monitoring oil related pollution as persistent in the Persian Gulf was implemented through application of the interactive classification method on the SAR images. The achieved findings appear to reveal that no less than 5000 oil spills were detected and identified to prevail in the Gulf area.

The sun photometer instrument measures atmospheric dust and aerosols distribution showing aerosol optical properties of the atmosphere. The study shows different uses of the device when used in an urban area (K) or desert area (S) investigating 339 days of collected data from August 2015 to July 2016 in the visible wavelength i.e. 870, 675, 500, 440 nm. The results show that there were 35 days of extreme dust events. Values varied from one location to another but in General, data in an urban area were always higher than those of a desert area. The objective of this study was to investigate particle distribution difference between two different locations. The obtained values for the urban area exceeded those of the desert on dusty days with a 32.21% due to wind element that is considered as a critical factor in obtaining data.

Air pollution refers to the release of harmful, excessive amounts, above the normal values, of pollutants into the atmosphere. Although, it is crucial to map and analyze the temporal distribution of pollutants, meteorological factors also have great influence on these parameters distribution and their effect on the ecosystem. Therefore, this study aimed to analyze the relationship between dispersion characteristics of air pollution with the meteorological parameters such as, wind speed, wind direction, relative humidity, air pressure and temperature for different seasons using the correlation analysis. Additionally, the seasonal distribution changes of pollutants were mapped and analyzed using GIS techniques in Kocaeli case. In this study the daily data obtained from 8 air monitoring stations including SO2 and PM10, wind speed, wind direction, relative humidity, air pressure and temperature belonging to 2015 year were examined. Pollution and meteorological distribution maps of the study area were obtained using Kriging interpolation method using GIS. By integrating the pollution and meteorological data, the regression analyses were carried out. The results of seasonal regression analysis and pollution distribution maps were evaluated using the wind maps obtained for each seasons.

Emission techniques are generally useful over the majority of the Earth’s surface. Low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes. The passive microwave satellite based data used includes the Special Sensor Microwave Imagers, SSMI Sounder, SAPHIR (Sondeur Atmosph rique du Profil d Humidit Intertropicale par Radiom trie), Advanced Microwave Sounding Units (AMSU), and Microwave Humidity Sounder (MHS), along with land surface model emissivity estimates. The analysis of SAPHIR sounder data brightness temperature data relating to selected vegetated land with different surface obtained for continuous period of 3 years from 2014 to 2016 was achieved. From our study SAPHIR sounder data is found useful for retrieving surface emissivity. This study relates 183.31 ± 11.0 GHz emissivity values, retrieved from a radiative transfer model using collocated SAPHIR sounder brightness temperature measurements. This examination is noteworthy for microwave sounder assimilation in climate conjecture models and for the usage of the information from passive microwave sensors on-board the Indo-French satellite “Megha-Tropiques,” which is committed to tropical environmental studies.