Shaded Relief Research Articles

A novel global multi-level terrain data tile production method

Abstract. Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services.

Lineaments Mapping Using Shaded Relief Images Derived from Landsat-9 Imagery at Diyala Government, NE Iraq

The Landsat-9 satellite images with a 30 m resolution at Diyala government was evaluated using the shaded relief technique to extract and map the surface lineaments. The processing of the azimuth and latitude method results in four shaded images (315\45, 200\50, 50\90, and 110\60). The optimal shaded relief (110\60) was used to extract the lineaments using the manual process. The results indicated that the primary structural trend of the lineaments was in the NW-SE direction, with a minor presence of NE-SW trends. It can be concluded that these structures are closely associated with the Makhoul-Hamrin trend in the Low Folded Zone (LFZ) influenced by the prevailing tectonic regime. In contrast, the lineaments structural trend of NE-SW would result from the neotectonics regime that linked to the listric fault system. On the other hand, the southeastern part of the study area shows no surface features due to the stress of the tectonic regime that affected the Tertiary deposits.

The Megaliths of Mols Peninsula – a Case Study of Applied Digital Photogrammetry in a Survey of Neolithic Monuments in Denmark

Abstract This study presents a practical case analysis detailing the outcomes of a survey project conducted on Neolithic megalithic structures on the Mols Peninsula in East Jutland, Denmark. The survey methodology employed digital photogrammetry, utilizing consumer-grade drones and handheld digital cameras. This approach facilitated the merging of aerial perspectives of the local surroundings with terrestrial photographs specifically focusing on the densely clustered stone chambers. Subsequently, the gathered data underwent conversion into various forms, such as orthophotos, surface elevation models, 3D representations, hill shades, point clouds, and textured low-resolution models optimized for online visualization. Among the noteworthy findings, the study included an evaluation of 19th-century drawings, revealing their remarkable precision. However, it was also noted that one of the monuments underwent substantial alterations in 1943. Moreover, the study successfully generated 3D models of the capstones from two dolmens, allowing for their virtual reconstruction. Importantly, the research introduced an innovative technique for mapping burned flints on the field surface surrounding the dolmens by employing low-altitude vertical drone photography – a previously unexplored method.

A geospatial approach to delineate lineaments on the granite gneissic terrain of Mpwapwa district, Dodoma, central Tanzania

The study sought to utilise Geographic Information Systems (GIS), Remote Sensing, and a blend of manual and automatic techniques to delineate and analyse the spatial distribution and orientation of lineaments within the terrain of Mpwapwa District. The study employed various techniques, including the Optimum Index Factor (OIF), Band Combination, Principal Component Analysis (PCA), and Directional Filtering on the Landsat 8 imagery, and Hill Shading technique was used on Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). These techniques were employed in the processing steps for both manual and automatic lineament extraction, utilizing various software tools. Lineament control and validation were executed using digitized geological maps, topographical maps, and Google Earth images.The results revealed lineaments with a total length of 2060.6 km, non-uniformly distributed and predominantly present in hard rock lithologies, such as migmatitic granite, migmatitic biotite gneiss, biotite gneiss, granitoid gneiss, and porphyroblastic gneiss. The findings further indicate that NE-SW and NW-SE are the dominant lineament trends. Additionally, the study revealed that areas with medium, high, and very high lineament density and intersection are primarily located in the southwest and northeast parts of the Mpwapwa District. Meanwhile, regions with low lineament density are spread through the entire district.In this study, GIS and remote sensing emerged as valuable tools for identifying and mapping lineaments within a challenging hydrogeological setting. Findings from this study will serve as a foundational framework for subsequent geological and hydrogeological investigations in the area of study.

Geo-sustainability Challenges and Remediation Strategies: A GIS-based Assessment of Sripur Coalmine Areas, West Bengal, India

This study examines the health, risk, and safety aspects of coal mining in the Sripur area, focusing on the Bhanora West Block, Asansol, West Bengal, India as one of the 14 operational mines of Eastern Coalfields Limited in Asansol. The study addresses the increasing risks of accidents, fires, and subsidence in active, ageing, and closed mines and storage yards. The study’s incorporation of field surveys, satellite imagery, and environmental data ensures that the findings are based on a well-rounded and multi-faceted dataset. The satellite imagery data (GIS and Remote Sensing) are perceived along with available literature and environmental impact compliance reports from time to time, with particular interest in the Bhanora West coal mine, both open cast and underground. The aspect, Hill shade, LULC and stream order maps of the Damodar are generated using Q-GIS, 3.28.7. The environmental clearance data, accident data, subsidence area, and landslide data are analysed. A vast area in Sripur is observed to be under subsidence, with cracks appearing even near residential areas. The flow of the Nunia nallah is nearby, and old closed mines are not adequately backfilled, so the chance of seepage cannot be neglected though regularly attended. Pneumoconiosis (CWP), a lung disease (black lung), chronic obstructive pulmonary disease (COPD), and silicosis are common in Sripur due to dust and substandard water. Effective and riskless mining extractions using proper technologies can avoid mining hazards, such as subsidence, the influx of water in underground mines, health hazards, air/water pollution, mine fire, and intact physical landscape and observing criteria of sustainable Development Goal SDG-1 SDG-3, SDG-6, SDG 12, SDG-13, and SDG 16.

The Potential of Hybrid Data Integration Approaches Based UAV-SfM Photogrammetry in Identifying New Archaeological Features in The Archaeological UR Site

Structure-from-Motion (SfM) Photogrammetry-based unmanned aerial vehicles (UAV) are considered one of the most effective non-destructive techniques to detect features in archaeology. However, relying on only standalone-based photogrammetric approaches is not always valid in revealing new archaeological features and monuments. This study aims to verify the efficiency of the integration-based methodologies of SfM photogrammetry products to improve the detection of new archaeological features in the non-excavated regions of the archaeological site of Ur, south of Iraq. The methodology proposes an automatic workflow to extract 3D topographic models such as Digital Terrain Model (DTM) by Agisoft Metashape (MS) software and orthomosaic images as standard products for UAV-photogrammetry. These have delivered the following Structure-from-Motion and Multi-View Stereo (SfM-MVS) algorithms using an optimized flight planning, data capturing, and data processing pipeline. Stand-alone raster images derived from photogrammetry, such as (slope images, hill shade rasters, etc.), have limitations in archaeology compared to hybrid raster images in detecting covered and unrevealed features. The methodology focuses on fusing multiple rasters extracted from DTM using the QGIS software package analysis tools to detect features that may never be detected from single standalone image rasters. This is because combining multiple rasters of different parameters may highlight new potential features such as paths, temples, etc. The results findings revealed that the fusion methodology of raster images obtained from UAV-SfM Photogrammetry through its standard products and using high precision settings could successfully help to identify many new archaeological features in the undiscovered site in Ur, such as walls and houses, which may date back to the late periods of Ur. The results concluded that this enhances the role of photogrammetry and remote sensing techniques as non-destructive excavation tools to provide a rich database for archaeologists for further analysis and interpretation of undiscovered archaeological sites without destruction. Many new archaeological features were also revealed, and a general digital map of the site was prepared. It also prompts a review of the settlement theories in previous studies in the ancient city of UR and the documentation of excavated archaeological ruins.

Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

Integrating terrain structure characteristics into generative adversarial nets for hillshade generation

A hillshade is a visualization technique that represents three-dimensional terrain in a two-dimensional plane by illumination mapping. The digital relief shading promotes the visualization of the terrain efficiently using DEM data. However, compared with manual shading, the digital algorithm-based method still has a gap in the visual effect of illumination strategy and terrain generalization. The typical landform characteristics and micro-geomorphic properties usually are destroyed. The reason is that the complexity of illumination rules is hard to summarize for different terrain phenomena. In this study, namely the data-driven artificial intelligent method, an alternative strategy based on generative adversarial networks (GANs) is proposed rather than the rule-based method. The DEM and the terrain skeleton lines are input to the model and part of the manual relief shading of Swisstopo is used for the training samples. Through the GAN training and learning, the manual skill imbedded in the hillshade is discovered in the generation model. The results show that the proposed model performs better than the digital relief shading on various landforms in aesthetic visualization and geo-scientific representation. Compared to other models, including other convolution neural network (CNN) based methods, terrain structure is maintained more significantly through the proposed model.

Shadow-constrained shape-from-shading for pixel-wise 3D surface reconstruction at the lunar south pole

ABSTRACT High-resolution digital elevation models (DEMs) of the lunar surface are crucial for lunar exploration missions and scientific research. The emergence of advanced technologies, such as Shape-from-Shading (SfS), has enabled the generation of pixel-wise high-resolution DEMs from monocular images of the lunar surface. However, SfS encounters significant challenges in locations with limited illumination, such as the lunar south pole, where the surface is largely covered by shadows. Therefore, this paper presents a novel shadow-constrained SfS approach for pixel-wise 3D surface reconstruction at the lunar south pole. The proposed approach uses multiple high-resolution images captured under different illumination conditions and an existing low-resolution DEM as the inputs and generates a high-resolution DEM with the same resolution as that of the input image through hierarchical SfS processing incorporating shadow constraints. Experiments were conducted using actual images collected by the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) at the lunar south pole. Comparisons with respect to photogrammetric DEMs generated from stereo NAC images show that the DEMs generated using the proposed approach exhibit the smallest root mean square error (RMSE). Moreover, shaded relief images rendered from the DEMs generated using the proposed approach demonstrate the highest similarity to the actual NAC images. Detailed profile comparisons further validate the effectiveness of the shadow constraint in optimizing 3D reconstruction in regions in proximity to shadows and within shadowed regions. The proposed shadow-constrained SfS approach can be used to generate high-resolution DEMs to support future missions to explore the lunar south pole, with applications including landing site evaluation and route planning for lunar probes or astronauts.

Lineaments Extraction and Analysis Using Sentinel 2B, Sentinel 1A Data and ALOS PALSAR DEM: A Case Study of the Igoudrane Region, Jbel Saghro, Eastern Anti-Atlas, Morocco

The remote sensing techniques have been used several years ago in many geological studies, and have proved their utility especially in lineament extraction. During this research, we extracted the lineaments automatically as well as manually in the Igoudrane region, Imiter inlier, eastern Anti-Atlas. The purpose of this research is to extract and map lineaments in the study area, and then compare the results provided, where the VNIR bands of Sentinel-2B, Sentinel-1A and the ALOS PALSAR DEM (12.5 m) were used. The directional filters were applied to the VNIR bands of Sentinel-2B into six directions, in the interest of attaining a significant lineament contrast. The VH and VV radar data provided the greatest number of lineaments (7243) by LINE module algorithm, while the shaded relief and slope were applied to the ALOS PALSAR DEM, which gave a higher number of lineaments than the VNIR bands of Sentinel-2B (4699, 4647 respectively). The manual extraction on the VV and VH polarizations yielded 441 lineaments, while the VNIR bands of Sentinel-2B provided 453 lineaments. Furthermore, the main orientations of the lineaments are NE-SW, ENE-WSW and NNE-SSW. The lineaments extracted from the VNIR bands (Sentinel-2B) and ALOS PALSAR’s DEM exhibited an optimal match along with the fractures digitized manually and from the geological maps of the research area. The results revealed that Sentinel-1 data is less efficient than ALOS PALSAR DEM data, especially in arid regions where the capacity of DEM radar waves to penetrate rises for thinner sedimentary layers, offering deeper information.

Comprehensive geospatial mapping and monitoring of an eastern coalfield in India

Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.

DEM super-resolution guided by shaded relief using attention-based fusion

Deep-learning based approaches have been proven effective for Digital Elevation Model (DEM) super-resolution (SR) tasks. Previous networks typically treat DEM elevation values as single-channel image for input. However, DEM images alone cannot fully capture spatial and terrain features. Shaded relief images (SRIs), derived from DEMs, serve as crucial visual cues that intuitively convey terrain characteristics, addressing the limitations of DEM images and providing synergistic benefits for training DL models. The primary challenge in utilizing SRIs for guiding DEM SR lies in accurately selecting a consistent structure to extract and effectively integrate features from SRIs and DEMs. In this study, we propose an Attention-based Hierarchical Terrain Fusion (AHTF) framework for guided DEM SR. Specifically, an Attention-based Feature Fusion Module (AFFM) is designed to efficiently fuse relevant information from LR DEM and SRI, which includes a feature enhancement block to select valuable features and a feature recalibration block to fuse diverse terrain features. Additionally, we optimize the loss function from the perspectives of terrain analysis and visual effects. We validate AHTF on our newly constructed real-world Shade-DEM SR dataset and two open-source DEM SR datasets. Compared to the current state-of-the-art methods, our AHTF achieves the best results in terms of root mean square error (RMSE) for elevation, slope, and aspect. Furthermore, the extracted stream networks are closer to real-world conditions. This study offers new insights and methods for further research and application in the field of DEM super-resolution. Our dataset can be obtained at https://doi.org/10.6084/m9.figshare.25590945.

A comprehensive review of urban growth studies and predictions using the Sleuth model

Urban growth is a complex phenomenon It has been the subject of in-depth research for the last few years. There are various models used to measure and simulate urban growth. Most of these methods are founded on GIS & RS techniques coupled with the CA algorithm, as only these tools and techniques have the capabilities to conduct spatiotemporal studies, manage spatiotemporal dynamics, and provide a detailed depiction and modeling from the bottom-up tactic. Recently, the slope, land use, exclusion, urbanization, transportation, and hill shade (SLEUTH) model has been the most commonly used model. It is easily accessible because it is open source; moreover, its source code is also easily accessible. The SLEUTH model’s name alludes to the necessary inputs —slope, land use, excluded area, urban extension, transportation, network and hillshade. The model has been used in many cities and has proven to be efficient. The present review paper reviews the past literature pertaining to urban development and prediction to further support the research on urban planning, urban growth and prediction.

Ambient Occlusion for Terrain Shading

Ambient occlusion is widely used in computer graphics to accentuate areas in a three-dimensional scene that are hidden from bright lighting. On maps, ambient occlusion adds spatial depth and nuanced texture to shaded relief images. Ambient occlusion images for maps can be calculated from digital elevation models using the Eduard relief shading application. This paper illustrates the use of ambient occlusion for cartographic terrain shading and introduces two extensions: (1) oriented ambient occlusion for modulation with a direction of illumination, and (2) reduced ambient occlusion for valley bottoms with consistent brightness.

Digital elevation model‐based lineament analysis of the Zindapir anticline, Sulaiman fold‐and‐thrust belt, Pakistan

The N‐S oriented Zindapir anticline is an eastward verging mega‐structure (~130 km along and ~40 km across the strike) in the eastern Sulaiman fold‐and‐thrust belt on the western margin of the Indian Plate in Pakistan. In this study, we carried out a lineament analysis of the anticline based on shuttle radar topographic mission (SRTM) images and digital elevation model (DEM) data to demonstrate the application of this technique for modelling of the fractured reservoirs. We used shaded relief images, variations in the vertical exaggeration, sun and azimuth angles to enhance the DEM data and structures in the area. Stereographic projections indicated that the anticline is an upright, asymmetrical, gentle, doubly plunging and east‐vergent fold. The synthesized lineament map of the fractures indicates a total of 402 fractures across the Zindapir anticline. A total of 204 and 198 fractures were observed in the eastern and western limbs, respectively. Statistical analysis portrayed that the lineaments of the majority of fractures followed northwest‐southeast (NW‐SE) and west‐northwest—east‐southeast (WNW‐ESE) trends. These are oblique or transverse to the fold axis and could be interpreted as shear and tensional fractures related to drag and compression along the western boundary of the Indian Plate. Our study provides an example of lineament analysis for structural characterization and modelling of fractured reservoirs in a collision‐mountain setting.

Comparative Spatial Modeling of Bretziella fagacearum Distribution in Northern Wisconsin, United States.

Bretziella fagacearum, the ascomycete fungus oak wilt, is considered a virulent threat to North American oaks, but the influence of the physical environment on this pathosystem remains unclear, particularly at the forest scale. This study explored the influence of terrain and soil factors on B. fagacearum infections, applying discrete and continuous spatial models to investigate the question, besides proximity to other infections, which environmental factors influenced B. fagacearum incidence? Locations of infections were recorded from 586 confirmed B. fagacearum sites, identified from 2004 through 2021 in a 76 km2 area of deep, sandy glacial outwash in Chequamegon-Nicolet National Forest, northern Wisconsin. Public datasets derived from remote sensing were incorporated as covariates, describing terrain elevation (USGS 10-m DEM), soil physical and chemical properties (POLARIS), and forest composition (WiscLand2). Spatial models included generalized additive models (GAMs) and Neyman-Scott cluster process models. The results indicated that spatial dependence and the distribution of oak forests were the most important drivers of B. fagacearum distribution in this area, with more minor influence from elevation, hill shade, and drainage patterns. Comparison between modeling approaches indicated that-at this scale and in this area-the most accurate models were those that included host distribution, spatial dependence, and quantitative terrain and soil descriptions. However, a close approximation could be attained using nonlinear models (GAMs) that incorporated only host distribution and spatial dependence.

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

Landslide susceptibility assessment along the National Highway-244 from Batote to Doda, J & K, India: A study based on the Frequency Ratio Method

The National Highway-244 is highly susceptible to landslide occurrences, frequently resulting in road blockades and causing significant hardships for the local population. These landslides pose a threat to human lives, property and the environment, leading to substantial losses. In this study, an attempt has been made to carry out landslide susceptibility assessment through frequency ratio method along the National Highway-244 utilizing GIS and statistical computations. It considers eight parameters, which include topographical (slope, slope aspect, slope curvature, hill shade, and relief), anthropogenic (distance to road and distance to river) and geological parameters that mostly influence the occurrence of landslides in the area under investigation. The present study focuses only along National Highway-244 and data has been gathered from field visits and secondary sources. The results of the study inferred that the area under investigation falls into different susceptibility zones, namely very high, high, moderate, low, and very low, covering approximately 15%, 31%, 27%, 19%, and 8%, respectively of the total area. This study reveals that a considerable proportion, around 73%, of the study area falls within very high to moderate susceptibility zones. The conclusions drawn from this study hold significant implications for stakeholders and also provide valuable insights for future planning and infrastructure development, enabling them to make informed decisions. By considering the susceptibility zones identified in this study, stakeholders can implement appropriate measures to mitigate the impact of landslides, ensuring the safety and stability of the region.

Mapping and Assessment of Geological Lineaments with the Contribution of Earth Observation Data: A Case Study of the Zaer Granite Massif, Western Moroccan Meseta

The Zaer granitic massif is one of the most important Variscan granitoids in the Central Zone of the Western Moroccan Meseta. It is characterized by a deformation which is manifested by a network of fractures of different scales. Thanks to the technology currently available, many geological studies rely heavily on the mapping of geological lineaments, especially in structural geology. This has become more reliable with access to earth observation data using optical and radar sensors as well as the various remote sensing techniques. Therefore, the objective of this work is to determine the potential of Landsat 8, ASTER, Sentinel 2 and radar Sentinel 1 datasets using the automatic method to extract lineaments. Furthermore, this work focuses on quantitative lineament analysis to determine lineament trends and subsequently compare them with global and regional tectonic movement trends. The lineaments obtained through different satellite images were validated by including the shaded relief maps, the slope map, the correlation with the pre-existing faults in the geological maps as well as the field investigation. Comparison of these results indicates that Sentinel 1 imagery provides a better correlation between automated extraction lineaments and major fault zones. Thus, Sentinel 1 data is more effective in mapping geological lineaments. The final lineament map obtained from the VH and VV polarizations shows two major fault systems, mainly oriented NE-SW and NW-SE to NNW-SSE.

Analysis of past and future urban growth on a regional scale using remote sensing and machine learning

This research investigates Land Use and Land Cover (LULC) changes in the Porto Alegre Metropolitan Region (RMPA). A 30-year historical analysis using Landsat satellite imagery was made and used to develop LULC scenarios for the next 20 years using a Multilayer Perceptrons (MLP) model through an Artificial Neural Network (ANN). These maps analyze the urban area’s expansion over the years and project their potential development in the future. This research considered several critical factors influencing urban growth, including shaded relief, slope, distances from main roadways, railway stations, urban centers, and the state capital, Porto Alegre. These spatial variables were incorporated into the model’s learning processes to generate future urbanization scenarios. The LULC historical maps precision showed excellent performance with a Kappa index greater than 88% for the studied years. The results indicate that the urbanization class witnessed an increase of 236.78 km2 between 1990 and 2020. Additionally, it was observed that the primary concentration of urbanized areas since 1990 has predominantly occurred around Porto Alegre and Canoas. Lastly, the future forecasts for LULC changes in 2030 and 2040 indicate that the urban area of the RMPA is projected to reach 1,137.48 km2 and 1,283.62 km2, respectively. In conclusion, based on the observed urban perimeter in 2020, future projections indicate that urban areas are expected to increase by more than 443.29 km2 by 2040. The combination of remote sensing data and Geographic Information System (GIS) enables the monitoring and modeling the metropolitan area expansion. The findings provide valuable insights for policymakers to develop more informed and conscientious urban plans, as well as enhance management techniques for urban development.