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Using RS and AI GIS in land management and urban planning in Mosul, Iraq for sustainable development
The importance of Remote Sensing (RS) and Geographic Information Systems (GIS) comes from the fact that they are means that have been proven effective in supporting and developing the decision-making process in the field of urban control. The city of Mosul in Iraq has been subjected to a tremendous change in the dynamics of land use due to the wars. This study aims to use RS techniques and Artificial Intelligence Geographic Information Systems (AI GIS), as the study relies on Sentinel-2 images and online data sources to evaluate the changes that occurred in the natural environment by comparing the years during and after the destruction. Four classification methods were used in this study; Support Vector Machine (SVM) learning, Scene Classification technique (SC), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). High classifications accuracies were obtained ranging between (92%–97%). The residential area had decreased, followed by vegetation, which was converted to bare land by 64% in the year 2017 due to the war effect. In 2023 some recovery of settlement and increasing in other sectors was observed. The approach highlights the capability of spatial technologies and the role of RS and GIS in sustainable development.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Geodetic monitoring of landslide processes in coastal territories
The study is devoted to the methodology and features of geodetic monitoring of territories to provide analytical and predictive information regarding predicting the danger of coastal strip landslide processes. The causes, consequences, and risks of the development of landslide processes are analysed. The purpose of the study was to justify the need and to consider the methodology of stable high-precision observations of the coastal fortification structure, to identify the zone of active landslides, and to provide suggestions regarding the features and conditions of further observations. To conduct direct observations, it was necessary to lay benchmarks on the territory of the research object. The method using GNSS technologies was chosen for determining the coordinates of benchmarks at the research object. The advantages of using GNSS equipment for monitoring slope deformations compared to traditional geodetic methods have been determined. To achieve the maximum accuracy of measurements, it was decided to install a constantly operating satellite station of the “System.NET” network directly in the work area. During the study period, 33 geodetic soil benchmarks and 53 benchmarks were installed on the coastal fortification structure in the coastal zone. In combination with the methods of geodetic observations, such a density and distribution of benchmarks both in the soil and on the coastal fortification structure made it possible to obtain accurate data on the dynamics of landslide processes and to prevent the negative consequences of the landslide.
Determination of location of historical and cultural heritage objects using photogrammetric and geophysical methods
Two central problems related to the study of historical fortification systems are apparent. First, there are high labour costs for the excavation of defensive structures. Therefore, studying each line of defence along its entire length by traditional archaeological methods is practically impossible. That’s probably why special studies of the fortification system are the exception rather than the rule, and information about defensive structures is given in single sections. The second problem is related to the fact that some lines of fortifications were destroyed in ancient times or were practically destroyed due to later economic activity. The specified circumstances determine the need to use photogrammetric and geophysical methods for the preliminary search of the infrastructure of defence structures. This work provides an example of deciding mass graves during the Second World War using the interpretative properties of German aerial photographs of 1944, archival cartographic data on the territory of the Lviv Citadel, where the Nazi concentration camp for prisoners of war Stalag-328 was located during the war. After predetermining the places of mass graves by photogrammetric methods, geophysical surveys were carried out with the help of ground-penetrating radar (GPR) for the exact localisation of the graves. 13 locations of mass burials and mass executions and burning of bodies of prisoners of war were discovered.
Investigation of the Africa splitting into two as new ocean forms
Scientists believe that Ethiopian tectonic plate changes indicate that Africa is breaking apart, opening the path for the emergence of the planet’s sixth ocean. Ever since the East African Rift, a 35-mile-long fissure in Ethiopia’s desert, appeared in 2005, there has been a continuous movement between the tectonic plates. The second-largest continent in the world will virtually split in two due to the separation of the Somalian tectonic plate from the larger Nubian tectonic plate. This event has not been seen in hundreds of millions of years, when South America and Africa were separated into separate continents. The Somali and the Nubian tectonic plates are slowly disintegrating from each other, while the Arabian plate continues to pull away. The continental rift will happen along the east African Rift Valley, a geologically active region whose formation millions of years ago is similar to that of the tectonic movements that happen at the bottom of oceans. This means landlocked countries such as Rwanda, Uganda, Burundi, the Democratic Republic of Congo, Malawi, and Zambia would inadvertently find themselves with a coastline, and thus, build harbours that connect them to the rest of the world directly. Kenya, Tanzania, and Ethiopia would have two territories each. Though that will take between five to 10 million years, the continent will eventually split into two sub-continents, creating a new ocean basin between them.
Harmonization of marine gravity data in Eastern Baltic
Marine gravity datasets covering areas of state scale typically are made up of data that has been surveyed over multiple campaigns. These campaigns often take place many years apart, are of varying resolution and accuracy. This is due to ship-borne campaigns being much more expensive and time consuming than the ones on land; because of this, there is added value in validating and possibly correcting older data sets. Over the course of recent BalMarGrav marine gravity project, dense, high quality gravity data was obtained covering most of the Latvian exclusive economic zone. The new data have been compared to campaigns, both as means of new data validation, and to check for possible biases among older data sets. Purpose of this research is to further the effort, to provide wider coverage of old marine gravity points, to test automated gravity point digitization, and to perform inter-campaign comparisons, using new, filtered and more precise data. Data recovered during this research covers the previous data gaps between sets used in previous research. Using a more complete data coverage can improve new campaign data set validation and provide insights on inter-campaign biases within older data. Recovered data cover shallow coastal areas, where gravity mapping was not done over BalMarGrav project. Thus, by applying correctional values geoid errors can be minimized in the problematic transition zone between terrestrial and marine data. Survey reports containing 20th century marine gravity point data were digitized, using optical character recognition. Gravity point values were transferred to sea surface and transformed to modern reference frames. Both modern and historic marine gravity data were filtered for gross errors and bias tracks. Data set robustness was checked, using leave-one-out cross validation. After processing, a comparison was made between old and new data. Results present re-processed and filtered marine gravity datasets, and their comparison statistics. Comparison statistics before and after filtering reveal the increased accuracy and precision of filtered data. Mean comparison values reveal inter-campaign biases and provide correction values, which can increase data accuracy for use as input in future research and surface modelling.
Drought safety levels assessment in Uzbekistan part of the Khorezm oasis by geospatial methods
The issue of drought has emerged as a significant challenge in the Khorezm oasis over recent decades. Furthermore, the construction of the Kushtepa canal in Afghanistan is expected to exacerbate the impact of drought in the region. It is of the utmost importance to evaluate the resilience of the oasis to drought in order to ensure effective planning and mitigation strategies. This study employed geospatial data, including the normalized difference of vegetation index (NDVI), land surface temperature (LST), normalized difference of moisture index (NDMI), soil brightness, groundwater table, digital elevation model (DEM), and distance to Amudarya river, derived from Landsat 5 TM, and Landsat 8 OLI/TIRS data (2000–2023). A weighted overlay analysis was employed to identify the most influential factors, which were found to be distance from the river, canal density, soil brightness, LST, and groundwater table. The findings indicate that 3746 km2 of the oasis is safe, while 4644.32 km2, 5563.77 km2, 5486.17 km2, 7832.64 km2 are classified as dangerous, mid dangerous, high dangerous, and extreme dangerous, respectively. It is recommended that agricultural use be prioritised in areas deemed safe, that construction be restricted, and that population migration from high-risk regions to safer areas be facilitated.
Estimation of land surface temperature based on GIS and remote sensing data in Dahuk city
Land Surface Temperature (LST) is a crucial variable across various domains, including studies on the global ramifications of climate change, urban land use and cover, and geo- and biophysical modelling. Satellite data from Landsat 8, specifically the NIR channel, was utilized to generate LST maps, NDVI, and LU/LC for Dahuk City. The correctness of LULC maps was verified by ground observation locations. The results indicate that the LST ranged from 4 to 14 degrees Celsius in 2013 and from 10 to 20 degrees Celsius in 2023. The highest temperatures, ranging from 14 to 20 degrees Celsius, occur in urban areas, whilst the lowest temperatures, recorded in 2013 and 2023, are in forests and aquatic environments, measuring 4 and 10 degrees Celsius, respectively and this occurred because of unplanned expansion of urban areas on behalf of green area as indicated by NDVI. Through this study planners and decision making could predict the future increase in LST if no action taken against these activities.
Normalized Difference Vegetation Index (NDVI)
Structuring and validation of photogrammetric territorial data
In the loving memory of Mariasofia Paparo, this publication focuses on the validation procedures of photogrammetric geographic information and the production and interpretation of complex reports, which are essential for handling the vast amount of generated data. Furthermore, sources of error in the structuring of geographic data and the quality parameters and conformity criteria necessary for the utilization of such data within the national geodatabase have been investigated.
Conceptual modeling of the structure of a geoinformation system for formation of land plots in state and municipal ownership of settlements
The article deals with the issues of automation of land cadastral information processing based on the modern international standards for management and processing of geospatial information. Object-relational databases together with the GIS provide powerful opportunities for collection, systematization, processing, analysis of various data, as well as storage and visualization of spatial information. According to the results of the analysis of the current legislative provision and processing of the existing data based on the international standards of the ISO 19110 series, the research developed a conceptual scheme of the geospatial database for the formation of state and municipal land plots within settlements. The research provides a model for checking the topology of the geospatial database of the system of automated formation of state and municipal land plots within settlements according to the structure and requirements of the international standard “ISO 19107:2019 Geographic information. Spatial schema”. The developed conceptual model and topology verification model are the basis for physical data modeling and allow ensuring the principle of interoperability in the Spatial Data Infrastructure.
Accuracy assessment of a three-dimensional model obtained using the LiDAR sensor of the iPhone 13 Pro Max
Scanning of an educational classroom was performed using the LiDAR sensor of the iPhone 13 Pro Max. Comparison was conducted between the lengths of lines and coordinates determined from the model scanned by the LiDAR sensor with precise data measured using the Leica TCR 405 ultra electronic total station and determined from the model created photogrammetrically from images captured with the Sony Cyber-shot DSC-RX100 camera. The Root Mean Square Error (RMSE) of distance and spatial positioning was calculated for the LiDAR-scanned model.
PHASE: a Matlab-based software for the DInSAR PS processing
The availability of free Synthetic Aperture Radar (SAR) images, like the ones delivered by the ESA Copernicus Sentinel-1 satellites, has led to the development of several processing tools, some of which are also free and open source. In this framework, when analyzing Sentinel data, the ESA SNAP software is usually required for data preprocessing, while, in the context of free and open source (FOS), Persistent Scatterer Interferometry (PSI) analysis can be performed by StaMPS (released by Stanford University). The workflow could be completed by the snap2stamps package, aiming at integrating the two main software packages. However, these tools are not designed to automate all the required steps to perform a complete PSI analysis. For this reason, the aim of this work is to develop PHASE (Persistent scatterer Highly Automated Suite for Environmental monitoring), a Matlab-based software suite that relies on already available FOS software, properly updated, enhanced and integrated, all accessible and customizable through a simple GUI. The focus is on minimizing the user interaction with the software, thus decreasing potential sources of error, while improving processing repeatability. The user will therefore primarily be responsible for configuring the processing parameters. Indeed, a streamlined procedure has been established, covering the entire process from downloading the SAR images to exporting the PS time series into a simple table format. In the paper, we present the developed software, highlighting its strengths compared to the status quo, while also providing a short example of successful application of the entire procedure.
Impact of data structure types and spatial resolution on landslide volumetric change measurements
Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.
Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Evaluation of the low-cost depth cameras for non-destructive testing
The primary aim of this paper is to assess the effectiveness of a low-cost stereo (depth) camera as a non-destructive tool for the detection and measurement of cracks in concrete surfaces. The experiment was carried out on four concrete beams with cracks, created with different concrete mixes. The mixes of the four beams were made up of lightweight aggregates with 12% of normal weight aggregates. One beam was used as a reference without fibers, while 3D steel fiber reinforcement, 5D steel fibers reinforcement, and a hybrid fibers mix of 5D steel fiber and synthetic were used for the other three beams. The cracks in the beams were measured manually followed by taking their stereo images with a ZED camera. The ZED images were processed to produce 3D models of the concrete surfaces, which are useful for crack measurement in a three-dimensional framework. The project results are particularly significant in the measurement of all three dimensions (length, width and depth), with less than a 10% error between the actual and the experimental procedure. Relatively, multiple differential approaches gave a less accurate result of a 15% error mainly due to syntax errors. Results suggest that the ZED depth camera is an effective tool for robust detection and measurement of cracks in concrete surfaces.
Building algorithms and classification thresholds for objects from point cloud data to create 3D city models
This article aims to develop an improved algorithm for classification of point cloud data. The primary component of this algorithm is determination of the classification thresholds for different geographical objects, which helps in the automatic classification of the LiDAR point cloud data. The algorithm was tested to classify the point cloud of three different areas of Ha Long city in Quang Ninh province. The results from the three areas show that for the ground points our algorithm is on average 6.4% more accurate than the traditional progressive TIN densification (PTD) algorithm. Further, with the proposed point cloud classification algorithms the average accuracy for asphalt roads is 87.77%, 98.09% for vegetation, and 96.86% for roof objects. The classified roof objects were further processed for house digitization, which provided an average accuracy of 92.07%. The whole dataset was used to develop 3D city models of the three areas (A1, A2 and A3 in Figure 7) in Hon Gai ward, Ha Long city with Level of Detail (LoD) 2.
Cluster analysis of factors influencing the valuation of real estate objects
The current real estate market analysis reveals challenges in valuation methods, procedure adequacy, and evolving technological approaches. Uncertainty arises from using localised methods for valuing individual real estate objects. A significant concern is the reliability and completeness of valuation data. Researchers emphasise market-driven aspects as trends in real estate valuation. Features for valuation are identified through quantitative characteristics, uncovering components and their nature. The research analyses foreign and domestic practices for real estate object valuation. Challenges include understanding methodological and informational support through mathematical methods and addressing factors affecting real estate object valuation. The need for cluster analysis to identify factors affecting real estate object valuation is recognised. To implement cluster analysis of factors affecting real estate valuation, a method is proposed involving the development of classification features, optimal typological grouping, and clustering implementation technology. Six groups of factors were chosen: spatial formation, urban planning provision, environmental impact, investment indicators, infrastructure provision, and limiting characteristics. An agglomerative process calculated the distance matrix between clusters of factors. The MacQueen k-means clustering method determined final clusters, confirming the validity of the proposed factor groups. The clustering of factors affecting real estate valuation was based on obtained distance data. The result identifies a high level of factors influencing real estate object valuation. Nine coincidences justify this in their clustering with four units of factors influencing real estate object valuation.
Surface runoff estimation at the Densu River Basin using geographic information system (GIS) and remote sensing (RS)
Accurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management.
Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine
By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.
Investigation of Hatay-Defne earthquake (20.02.2023) by using GNSS station
No studies have been conducted on the spatial changes related to the Hatay-Defne earthquake (20 February 2023) up to now. The reason for this situation is that the requested data cannot be accessed as a result of the power and internet interruptions caused by the earthquake. The southern Turkish city of Hatay had a 6.3 magnitude earthquake at 20:04 on February 20, 2023. Three minutes later, a 5.8 magnitude aftershock occurred, and 90 more aftershocks followed. These earthquakes, which were felt across the area, caused individuals who survived the horrific quakes on February 6, 2023, to experience new levels of dread. In the district of Hatay-Defne, the epicenter began. In its first assessment, the United States Geological Survey gave the earthquake a magnitude of 6.3 at a depth of 16 kilometers. In this study, firstly, GNSS data belonging to ARST station in 30 seconds recording interval were downloaded into CSRS-PPP. GNSS data was processed as static and then was processed as epoch to epoch by using kinematic method. The coordinate differences between the obtained coordinates by using the kinematic processing and the obtained coordinates by using the static processing were computed. According to the obtained results, a horizontal movement of approximately 8.30 cm in the south-west direction was observed at the ARST station, which is the closest to the earthquake center.
Prediction of building subsidence in Vietnam using machine learning techniques based on leveling results
Vietnam’s rapid urbanization and economic growth have led to an increase in high-rise buildings, making building subsidence a significant concern. Monitoring subsidence is crucial for ensuring building safety and reducing potential risks. The leveling method is commonly used in Vietnam to monitor subsidence, providing valuable data for predicting future subsidence behavior. However, traditional prediction methods based on mathematical models have limitations in capturing complex subsidence patterns. Machine learning techniques have shown promise in enhancing subsidence prediction accuracy. In this study, we analyze machine learning methods for predicting building subsidence using leveling results in Vietnam. We utilize a dataset from a subsidence monitoring network in Hoa Binh General Hospital and compare the performance of linear regression, decision tree regression, and random forest regression models. Our results show that the decision tree and random forest models produce consistent predicted subsidence values, aligning with the observed stability of the building. In contrast, the linear regression model fails to capture the diminishing nature of subsidence over time. We discuss the implications of these findings and highlight the advantages of machine learning in accurately forecasting subsidence. The study demonstrates the potential of machine learning in revolutionizing subsidence prediction and enhancing the monitoring and management of building stability and structural integrity in Vietnam.
Integrated index analysis for monitoring urban growth based on GIS and remote sensing data in Karbala province, Iraq
Remote sensing techniques and GIS were used in this study to monitor urban growth in Karbala Governorate, Iraq. A compiled database was created from available Multi-temporal Landsat (TM, ETM+ and OLI) data from 2000 to 2018. The near infrared (NIR), visible red (R), and short infrared (SWIR) wavelength areas covered by Landsat bands have been used to generate spectral indices known as the Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Salinity Index (NDSI). Images of the Landsat 7 and 8 satellites that were free to download from the USGS website between the years of 2000 and 2018 were the data used in this study. The layers were classified and merged to reveal the dynamic changes of land cover in the study area. The result shows that the built-up area increased from 264.75 km2 in 2000 to 391.23 km2 in 2018, indicating an increase over 18 years, but over the same period, the amount of water decreased.
Accuracy test of satellite imagery-derived bathymetry in shallow waters using Sentinel-2A multispectral imagery
Continuous bathymetry mapping for shallow waters is very important considering that these waters are prone to change. Bathymetry measurements obtained from satellite imagery are an alternative that can be used. This study aimed to evaluate and develop algorithms that can be used to estimate shallow water depth values obtained from satellite imagery. In this study, the depth mapping results were obtained from Surface Reflectance derived from Sentinel-2A image processing. A comparative analysis was performed by comparing measurements obtained with an echosounder and estimated depths estimated with Lyzenga, Stumpf, and modified Stumpf algorithms. In this study, where the depth ranged from 2–6 meters, the Lyzenga algorithm performed the best algorithm with the R2 value of 0.94 and the RMSE 0.23, followed by the modified Stumpf algorithm with an R2 value of 0.93 and RMSE 0.24, and Stumpf algorithm with a R2 value of 0.88 and a RMSE of 0.32. Overall, this study provides an important contribution to comparing Lyzenga and Stumpf algorithms for estimating water depths. This study provides guidance on choosing the correct algorithm for bathymetric mapping using satellite imagery in similar water locations.
Investigation of urban biodiversity and factors influencing it based on modern technologies
In the article, the development of the cities of the Shirvan plain, located in the central part of the Republic of Azerbaijan, and the impact of this development on the loss of biodiversity of the cities were analyzed. Currently, cities, where more than 50% of the world’s population live, are the main reasons for the change of natural landscapes. The development of cities and the intensity of urbanization accelerate these changes. Therefore, our research is dedicated to the current issue. During the research, preference was given to modern methods. Landsat 5, 8 and 9 satellite images of 6 cities located on the Shirvan plain were obtained. As a result of the processing and analysis of these satellite images, the development of the territory and population of the cities was studied, and their effects on the natural landscape, its components, vegetation and water bodies were analyzed. The boundaries of cities in 1975 and 2023 were determined and compared. As a result of the conducted studies, it was determined by the processing of the 1975 topo plans and the 2023 satellite images that the area of the cities has increased by at least 2 times. The high average annual growth of the population has led to the transformation of cities from small towns to medium and large cities in a short period of time. This causes the loss of biodiversity as soon as possible and accelerates this process.
Investigation of the effects of Kahramanmaraş earthquake series on Cyprus Arc, Dead Sea fault, Hatay regions and stations close to two earthquakes epicenters
In various parts of the globe, there have been several earthquakes of a modest size. Monitoring the change of the points over time is a key component of typical techniques for extracting dynamic responses. This technique was unable to completely extract all of the earthquake’s dynamic properties. The GNSS precise point positioning (PPP) may be a useful tool for obtaining values of the point’s displacement that are more exact up to millimeters, which can help to overcome these flaws and evaluate the seismic wave of such earthquakes. Ultimately, PPP is a crucial tool for getting the precise observations. In this study, Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) approach to analyze the station’s displacement components and the station’s heights in periods from the two Kahramanmaraş earthquakes. The earthquake sequences that occurred in Turkey’s Kahramanmaraş in 2023 is an example of complicated faulting brought on by interactions between three plates close to the Hatay Triple Junction (HTJ). While the relative plate movements in this area are minimal (usually less than 10 mm/year), even sluggish plate motion zones may nevertheless see earthquakes that are quite destructive. Due to the three-plate system’s unusual geometry, a number of large earthquakes with very varied fault orientations were active throughout this series. A 7.8-magnitude earthquake happened on February 6, 2023 in southern Turkey, close to Syria’s northern border. A magnitude 7.5 earthquake, situated about 95 kilometers to the southwest, was occurred nine hours after the first one. The first earthquake was as big as the most powerful one ever recorded there in 1939 and was the most catastrophic to strike earthquake-prone Turkey in more than 20 years. In this study, the effects of two earthquakes in Kahramanmaraş were investigated on the Cyprus Arc, the Dead Sea fault, Hatay and the points close to two earthquakes zone. In the obtained results, it was computed that the greatest horizontal displacement occurred at the HAT2 station with 68.97 cm.
Influence of Noise Equivalent Beta Naught estimation on backscattering image classification of TerraSAR-X
Noise Equivalent Beta Naught is the different noise influence that beneficence to the radar signal. This type of noise is available in TerraSAR-X satellite images and expressed in forms of scaled polynomial described the noise power. On the other hand, Sigma naught or backscattering coefficient represents the average reflectivity of a horizontal material samples which used to reflect the nature of the land use and land cover in radar images. In this paper, radar satellite images in dual VV and HH polarization were used to study the influence of the noise on backscattering image classification. The result demonstrated that the visual interpretation of sigma naught which is result from the comparison between existence case and absence case (in the other word: with and without noise) of the noise illustrated that there is no different between them. In the other hand, for more details and more precise, an example of small images are used to show the values of obtained backscattering. The result demonstrated that the NEBN plays the main roles in decreasing the values of backscattering coefficient in TSX image. The influence of this noise had usually high in water body surface, because this surface is generally having small backscattering coefficients compared with land cover.
Modeling market value of land plots using GIS
This work examines the regulatory framework for expert monetary valuation of land plots. Scientific research on the development and improvement of economic-statistical methods for mass monetary valuation of land plots is analyzed. The peculiarities and advantages of using GIS in the valuation procedure are considered. A structure of the valuation database is developed for the effective selection of comparable plots during valuation by comparing sales prices and market analysis. The valuation database is formed based on data on land plot sales. Price formation factors are analyzed in order to construct an economic-statistical model of market value for land plots in Rivne. Scales of indicator values are proposed for formalizing the assessment of price formation factors. Functions of the ArcGIS are used to establish indicator values. The market value of land plots is modeled using multiple regression analysis. Additionally, a surface of land plot values is constructed, which serves to determine the approximate market value. The model has been verified using GIS tools. An accuracy assessment of the developed model has been carried out.
Bathymetric analysis using multifrequency multibeam echosounder
Making a nautical chart for safe navigation is a bathymetric survey’s primary goal. Multifrequency MBES have been developed over the last few decades, and their introduction has dramatically improved the efficiency, accuracy, and spatial resolution of coastal and ocean mapping. The goal of multifrequency MBES is to increase the subsurface’s detection resolution. To obtain an accurate picture of the seabed, the user can lessen the impact of this subsidence by running surveys in three different modes at once. With the help of multifrequency MBES, this study will analyze bathymetry in shallow coastal waters. The digital bathymetric model’s (DBM) frequencies are remarkably close. The depth value of the study site ranges from –20 m to–70 m with reference to lowest water surface (LWS) based on the produced DBM. Generally, the difference between 100 kHz, 200 kHz, and 400 kHz is as small as 0–30 cm, and a small part is 30–60 cm. The volume between frequencies for an area of 1 ha is between 90 m3 to 440 m3. If the thickness of the dredged sediment is 1 m, then the difference in volume between frequencies is less than 5%. The bathymetry difference between 100 kHz and 400 kHz frequencies to –10 cm is dominated by the region of 0 cm. Dredging volume inter frequency ranges from 0.042 m3/m2 to 0.068 m3/m2.
Coastal ecological dynamics: monitoring vegetation changes in the Sfiha-Swani region (Morocco)
This study examines the dynamics of vegetation cover in the coastal region of Al Hoceima, focusing on Sfiha and Swani beaches. It aims to understand changes in land cover categories, particularly the distribution and dynamics of vegetation cover. The research uses a comprehensive spatio-temporal analysis using advanced remote sensing technology and GIS. The methodology uses four Landsat satellite images and four aerial photos from Google Earth from 2003 to 2021. It involves digital processing, radiometric and geometric corrections, and on-site ground visits to validate remote sensing data outcomes. The study used land cover maps and Normalized Difference Vegetation Index (NDVI) maps to analyze changes in vegetation cover over the past 18 years. The maps revealed percentages of 21% (2003), 25% (2010), 33% (2015), and 15% (2021) of vegetation cover changes, indicating degradation in the study area. The study reveals a significant decline in vegetation in the coastal region due to a combination of human and natural factors. This assessment is crucial for developing effective conservation and management strategies. The findings extend beyond Al Hoceima, providing insights for broader environmental conservation efforts, and highlighting the intricate relationship between human activities and environmental changes.
Identification of geological units in Alborz Mountain in Iran using Landsat-9 image
In the present study, the suitability of principal components analysis (PCA) based techniques was evaluated for identification of geological units from Landsat-9 satellite imagery. In this respect, a scene of Landsat-9 operational land imager 2 (OLI–2) data of the year 2023 was acquired and a geological map scale 1:100000 of the study area was used as the reference. The results indicated suitability of the PCA based techniques for discrimination of geological units from Landsat-9 image, especially the PCA of decorrelation stretch (DS) approach. The PCA-DS approach, which considered the advantages of both PCA and DS techniques, successfully identified all the geological units in the study area, including the Basalt, Sandstone, Dolomite, and Conglomerate. However, the performance of the PCA and DS techniques was also reasonable for this purpose. On the other hand, the study revealed weak performance of the minimum noise fraction (MNF) and PCA-MNF techniques for geological mapping using Landsat-9 imagery. In conclusion, the study demonstrated the advantage of the PCA-DS approach for geological mapping using Landsat-9 imagery; therefore, it may be useful in futures studies for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.
Subsidence analysis of hydroelectric dam using the Kalman filter – a case study in Hoa Binh hydropower plant, Vietnam
Hydroelectric dams have a great influence on the safety of the downstream area. Therefore, deformation monitoring for assessing the safety of dam should be carried out regularly. In order to improve efficiency of the dam management, it is necessary to analyse the displacement values in space, over time to assess overally the displacement of dam. In this purpose, an attempt was conducted to analyse the subsidence of hydroelectric dams located in Hoa Binh, Vietnam using one of the most useful method – Kalman filter. Kalman filter is the unique method that can determine influence of external factors (particularly, elevation of water level in the reservoir) on dams, simultaneously forecast the displacement values of dam in the future. Moreover, Kalman filter allows to predict subsidence accurately in about 6 months that is longer prediction time than other static models. These are clearly presented and discussed in the article. The obtained results demonstrate the high applicability of Kalman filter method in analysing and forecasting the subsidence of the Hoa Binh hydroelectric dam.
Prediction of land use/land cover change in Indonesia using the open source land cover dataset: a review
Indonesia, as a promising developing country, faced with the fact that the development is not evenly distributed. Moreover, the number of people living in urban area is more and increasing at least 2.1% per year according to Central Statistics Agency (BPS). Hence, urban area has better transportation access and public facilites. However, high number of people living in urban area leads to spatial confilcts if spatial planning is not carried out based on sustainable development. For this reason, it is necessary to carry out long-term spatial planning using predictions of changes in land use/land cover in Indonesia. The purpose of this literature review is to get an overview of research development trends related to predictions of land use/land cover in Indonesia. Based on bibliometric analysis, the research trend related to this topic is that most research locations are in urban areas using satellite imagery input data and the Cellular Automata-Marcov Chain (CA-MC) method for making predictive models. Meanwhile, open source land cover datasets have not been widely used in land use/land cover prediction research in Indonesia. This can be used as input for updating further research.