Sense Of Information Research Articles

Recent innovations in land capability classification for sustainable development: a brief overview.

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

ARMNet: A Network for Image Dimensional Emotion Prediction Based on Affective Region Extraction and Multi-Channel Fusion

Compared with discrete emotion space, image emotion analysis based on dimensional emotion space can more accurately represent fine-grained emotion. Meanwhile, this high-precision representation of emotion requires dimensional emotion prediction methods to sense and capture emotional information in images as accurately and richly as possible. However, the existing methods mainly focus on emotion recognition by extracting the emotional regions where salient objects are located while ignoring the joint influence of objects and background on emotion. Furthermore, in the existing literature, when fusing multi-level features, no consideration has been given to the varying contributions of features from different levels to emotional analysis, which makes it difficult to distinguish valuable and useless features and cannot improve the utilization of effective features. This paper proposes an image emotion prediction network named ARMNet. In ARMNet, a unified affective region extraction method that integrates eye fixation detection and attention detection is proposed to enhance the combined influence of objects and backgrounds. Additionally, the multi-level features are fused with the consideration of their different contributions through an improved channel attention mechanism. In comparison to the existing methods, experiments conducted on the CGnA10766 dataset demonstrate that the performance of valence and arousal, as measured by Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), has improved by 4.74%, 3.53%, 3.62%, 1.93%, 6.29%, and 7.23%, respectively. Furthermore, the interpretability of the network is enhanced through the visualization of attention weights corresponding to emotional regions within the images.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Remote Sensing - Based Analysis of LST and NDVI Correlation for the Coolest and Hottest Month of 2023 in Khanaqen City

Urban land cover characteristics often lead to variations in surface temperature. Remote Sensing (RS) and Geographic Information Systems (GIS) are valuable tools for obtaining detailed information about surface indices. This study evaluates the temporal variation of Land Surface Temperature (LST) in Khanaqen city, located in South eastern Iraqi Kurdistan and Eastern Iraq. The research investigates the relationship between LST and the Normalized Difference Vegetation Index (NDVI) for the coolest and hottest month of 2023 using Landsat images from the USGS Earth Explorer. The objective is to explore how green spaces influence LST during the cool, humid winter and hot, dry summer. Two satellite images from Landsat 9 were used to retrieve LST and NDVI for the study periods. The results indicate that green spaces significantly reduce LST in July, so that the result of the correlation analysis of variables, significant inverse correlation between LST and NDVI (R = -0.53, R2 = 0.28 and P value = 0.00) highlights the effect of vegetation cooling. Conversely, January shows a modest correlation (R = -0.01, R2 = 0.00, and P value = 0.90), indicating a minimal effect of vegetation cover on LST.

APPLICATION OF GEO-INFORMATICS IN THE MAPPING OF AREAS VULNERABLE TO SURFACE WATER STRESS IN KADUNA STATE, NIGERIA

The main purpose of this paper is to identify water-stressed areas as a response to land use/land cover (LULC), climate variability, and increasing population in Kaduna State using Remote Sensing and Geographic Information systems. To achieve this aim, the paper set out to identify/map out areas vulnerable to water stress in the study area. The Meteorological data for the study were sourced from twelve remotely sensed stations around Kaduna State for thirty-seven years (1984 to 2021), land use data from remotely sensed satellite imageries that covered thirty-seven years, and projected population for thirty-seven years respectively. There are several indices used to compute water stress and the paper opted for indices that could monitor climate variability and surface water resources. Thereafter, the six indicators were standardized, weighted, and aggregated in Microsoft Excel, and imported into the ArcGIS 10.2.1 environment, and a weightier vulnerability model analysis was used to determine the weights of each variable’s contribution to water stress. The result achieved indicated that: 54.41% of the entire study area is vulnerable to water stress, 15.80% is moderately vulnerable, 26.23% is less vulnerable and 3.56% is not vulnerable to water stress within the period under study. Furthermore, Birnin Gwari, Chikun, Igabi, Ikara, and Kajuru Local Government Areas (LGAs) were the most vulnerable areas in the State while not vulnerable areas are; Kauru, Kagarko, and Jaba LGAs. The paper recommends the implementation of water conservation measures which include the creation of wetlands and shelter belts in areas vulnerable to water stress.

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

Role of Remote Sensing (RS) and Geographic Information Systems (GIS) in Water Resource Management in India

An approach to demarcate prospective areas for groundwater recharge a case study of Upper Thurinjalar Tamil Nadu, India

India, as the world's largest consumer of groundwater, faces an alarming water crisis, particularly in southern regions like Upper Thurinjalar, Tamil Nadu. The relentless extraction of groundwater has severely depleted reserves, making it increasingly difficult to meet the water demands of households, agriculture, and industry. This unsustainable usage endangers the region's water security, emphasizing the urgent need for sustainable management practices. Immediate action is essential to safeguard these vital resources and secure a sustainable water future for the region. The present study addresses the critical need to identify optimal groundwater recharge areas to mitigate the adverse effects of groundwater depletion. Utilizing Remote Sensing (RS) and Geographic Information Systems (GIS), this research focuses on delineating suitable recharge zones in Upper Thurinjalar, Tamil Nadu, to promote sustainable water resource management. Through geospatial analysis, thematic maps were generated, incorporating variables such as geomorphology, geology, subsurface lithology, lineaments, land use, drainage density, soil types, and slope. Utilizing satellite data within the GIS framework, groundwater recharge zones were classified into three categories: highly suitable (61.06 km2), moderately suitable (214.18 km2), and least suitable (48.07 km2). Additionally, prioritized interventions for 18 rural reservoirs within the same catchment area included sediment removal, depth enhancement, and infrastructure upgrades. The application of artificial groundwater recharge in these targeted areas is anticipated to significantly alleviate irrigation water deficits, thereby advancing sustainable development and rehabilitating degraded land. The use of a weighted overlay technique within Geographic Information Systems (GIS) has proven to be a highly effective method for optimizing water resource management, facilitating the sustainable development of groundwater resources. The results emphasize the importance of promptly undertaking focused interventions, such as prioritizing tank improvements and deploying artificial recharge procedures, in order to guarantee the long-term availability of water. Implementing these proactive measures is essential for reducing water scarcity and promoting the development of unused land in the designated recharge areas.

Integrated Morphometric and Hypsometric Analysis of Niragantipalli Micro Watershed Using Remote Sensing and Geographical Information System Techniques

Employing Remote sensing (RS) and geographic information system (GIS) for the morphometric parameters analysis are discovered to be tremendous usefulness in the prioritization of watersheds for soil, water conservation and natural resource management at micro watershed level. The analysis of morphometric parameters plays a crucial role for understanding and managing watersheds. In current study an, attempt was to determine the morphological parameters of Niragantipalli micro-watershed in Chikkaballapura District of Karnataka, India. For detail study, Google earth pro, GPS visualizer conversion toll and Arc GIS were used for preparation of DEM and delineation of the watershed boundary. GIS was used for evaluation of basic, linear, areal and relief aspects of morphometric parameters. The Niragantipalli micro-watershed occupies an area of 632 ha with third order stream (truck order) and dendritic drainage pattern. The mean bifurcation ratio of the micro-watershed is 2.5 it suggests that the drainage system formed on homogeneous rock when the influences of geologic structures on the network of streams were negligible and generate sharp peak flow. With a drainage density of 1.31 km/km2, the drainage network is extremely coarse. The watershed is elongated, as shown by the form factor of 0.27 and elongation ratio of 0.58. The results from the morphometric analysis of the watershed are very beneficial for developing and designing conservation structures of soil and watershed management measures. The hypsometric curves’ structure as well as estimated hypsometric integral results reflects the Niragantipalli Micro Watershed erosional stages. As a result, the study concludes that morphometric and hypsometric analysis findings may be useful to stakeholders participating in catchment development and management projects.

Analysis of the Morphometric Characteristics of Wadi Rajil in the Azraq Basin using Geographic Information Systems

The study aimed to Analyse the Hydrometric Characteristics of Wadi Rajil basin and assess the flood hazards in the basin using Remote Sensing and Geographic Information Systems Techniques. The study used Models of Digital Visualizations of the basin and processed them using ARC GIS 10.5 and ARC HYDRO TOOLS. The result was that the Hypsometric Coefficient reached (21.4%) of the valley area that was exposed to erosion and (78.6%) of it is still waiting for its turn to erosion, indicating that the valley has reached the stage of youth. The analysis of Hot Spots in the GIS program resulted in negative values for internal Drainage Basins greater than -0.5 extending to the northern and northwestern parts of the basin. It also showed that the flow coefficient exceeds the internal Drainage Coefficient or Leakage. As for the positive values, they are distributed in the southern and southeastern parts, where the (Hot) values prevail and where the leakage coefficient exceeds the Flow Coefficient. We find that the Hot Spots are concentrated in the southeastern parts, which are mostly extended. The Rift and thus the most suitable areas for Groundwater Charging through it.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Tuning Transduction from Hidden Observables to Optimize Information Harvesting.

Biological and living organisms sense and process information from their surroundings, typically having access only to a subset of external observables for a limited amount of time. In this Letter, we uncover how biological systems can exploit these accessible degrees of freedom to transduce information from the inaccessible ones with a limited energy budget. We find that optimal transduction strategies may boost information harvesting over the ideal case in which all degrees of freedom are known, even when only finite-time trajectories are observed, at the price of higher dissipation. We apply our results to red blood cells, inferring the implemented transduction strategy from membrane flickering data and shedding light on the connection between mechanical stress and transduction efficiency. Our framework offers novel insights into the adaptive strategies of biological systems under nonequilibrium conditions.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Identification of groundwater recharge potential zone using geospatial approaches and multi criteria decision models in Udham Singh Nagar district, Uttarakhand, India

Groundwater is a valuable natural resource, which is extensively used in agricultural, industrial as well as daily activities of human beings. It is an essential source of freshwater need of half of the world’s population. In this study Remote Sensing (RS) and Geographic Information Systems (GIS), along with Multi criteria decision models were used. The Analytical Hierarchy Process (AHP) was used to determine the weights of various themes and their classes in the study to identify the groundwater recharge zones in Udham Singh Nagar district. Nine thematic maps/layers were created using GIS methods and employed for the demarcation of groundwater potential zone (GWPZ). It was concluded that around 82% of the area is very suitable (very good and good) for ground water recharge, which suggests that there is a significant amount of potential for artificial recharge systems to be implemented. This can help to augment the natural recharge process, improve water quality, and reduce the strain on existing water sources. Whereas only 18% (very poor and poor) of the area has low potential to groundwater recharge is still important to consider, as it may require different approaches or strategies to improve its suitability. Validation shows that, wells data; (n=38) have water level <5 mbgl (83%), whereas only (n=8) have water less >5 mbgl (17%). This area could be prioritized for more extensive data collection or monitoring to better understand its hydrogeological characteristics. The findings will indeed be helpful for decision-makers in selecting suitable locations for groundwater recharge structures.

Endel Tulving. 26 May 1927 — 11 September 2023

Endel Tulving was an Estonian–Canadian cognitive psychologist and cognitive neuroscientist who transformed the study of human memory in the 50 years between 1960 and 2010. He was born in Estonia in 1927, and spent several years in post-war Europe before emigrating to Canada in the late 1940s. He obtained degrees at the University of Toronto and at Harvard University before returning to the University of Toronto as a faculty member in 1956. He was always an independent thinker, and rejected the dominant view of memory and learning in terms of associations in favour of a more cognitive approach in which the organization of remembered events played a major part. He also developed theories on how events are encoded and retrieved, first in terms of behaviour and then in terms of their underlying neural correlates in specific brain regions. His theoretical ideas on different memory systems and their relations to different types of consciousness, laid out in his 1983 book Elements of episodic memory (Oxford University Press), continue to play a major part in current work on human memory. As a person, he retained a somewhat European sense of propriety and formality, which often made him seem quite formidable to new acquaintances. Once someone's scientific seriousness was established, however, Endel was warmly supportive of his students and colleagues. He was much loved by his family, and held in high esteem by his fellow scientists.

GIS-based multi-criteria approach for ranking suitable areas for the implementation of PV-Powered MCDI desalination plants in the Rheris Watershed, Southeast Morocco

In Morocco's oases, which rely heavily on surface water from mountain sources, the threat of decreasing rainfall presents a significant challenge, leading to a decline in surface water availability. This study focuses on the Rheris Watershed in Southeast Morocco, where brackish water desalination emerges as a viable alternative to ensure sustainable water supply for agriculture. Prior to the deployment of a pilot plant integrating a desalination unit, selecting an optimal site for PV-powered MCDI desalination plants was critical. This selection was based on a range of criteria, including Groundwater Quantity, Global Horizontal Irradiation, Temperature, Evaporation, Water Salinity, Water Quantity, Well Density and Slope. These criteria were evaluated using a combination of the Analytical Hierarchy Process (AHP), and geospatial data obtained from Remote Sensing (RS) and Geographic Information System (GIS) techniques.This case study in the Rheris watershed identified potential sites for future Membrane Capacitive Deionization (MCDI) desalination projects, High suitability areas encompass 280.45 km² (30.91 %), located west of Tinejdad, Khettarat Moulay Hachem, north of Goulmima, and south of Tinghir. Low suitability areas cover 63.75 km² (7.02 % of the study area), including regions south of Tadighoust and Tinejdad. Moderate suitability areas span 562.86 km² (62.05 %), involving central regions of Goulmima, Tadighoust, Tinejdad, and Tinghir. Based on the results of the study, the recommendations include focusing on areas identified as highly suitable for MCDI technology for sustainable agriculture, refining selection criteria based on local data, exploring brine management strategies, and adopting integrated water management approaches to ensure sustainable resource use.

Impact of European Integration on Substantive and Formal Constitutional Amendments in Spain in the Light of the Spanish Constitutional Court’s Jurisprudence and Constitutional Practice

The purpose of the article is to answer the question of how the process of European integration has influenced the Spanish constitutional order. Taking the European clause set out in Article 93 of the Spanish Constitution as a starting point, it analyzes both impacts of a formal-legal nature, but also influences of a substantive nature affecting the normative content of the Spanish Constitution. The study demonstrates that EU law, based on the principle of “direct applicability” and “direct effect,” intensively influences Spanish constitutional law and has been the strongest factor influencing the Spanish legal order since the enactment of the Spanish Constitution in 1978. Thus, while the Spanish Constitution in the formal sense remains a rigid constitution, when viewed in the category of a substantive constitution, it has proved to be flexible and susceptible to external influence, despite the fact that this occurs de jure with the consent of Spain expressed in respect of the principle of state sovereignty, on the basis of the provisions contained in Article 93 of the Spanish Constitution.

Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS

The urban sprawl phenomenon refers to the expansion of urban areas driven by high population growth and migration. A spatio-temporal approach is indispensable in urban sprawl research. Monitoring and evaluating urban sprawl in a region is crucial for controlling drastic environmental changes. Integrated Remote Sensing (RS) and Geographic Information System (GIS) technologies can serve as essential tools for this purpose. The aim of this systematic literature review paper is to gather information on the latest data, methods, and findings to be considered in future urban sprawl research. The PRISMA method was employed, involving filtering from the Scopus database, resulting in 30 papers selected for an in-depth review to address the objectives of this paper. Landsat data remains the preferred choice for monitoring changes due to its extensive historical archive compared to other data sources. Landscape metrics represent a more advanced method com-pared to conventional change detection in quantifying urban sprawl. Other indices and quantifiers are also used to support the quantification of urban sprawl. Two perspectives exist in selecting the study's temporal intervals: consistent and inconsistent, which are adjusted based on the natural characteristics of "change," namely "abrupt" and "gradual." Suggestions for future research include using data with detailed spatial resolution and narrow study intervals while considering the patterns of urban sprawl formation.