Geographic Information System Techniques Research Articles

Spatiotemporal Variations in Urban Wetlands in Kazakhstan: A Case of the Taldykol Lake System in Astana City

Acquiring a comprehensive understanding of the spatiotemporal dynamics of urban wetlands in Kazakhstan is crucial for their effective preservation and sustainable urban development. Our findings identify past and present Land Use Land Cover (LULC) in the capital city, providing policymakers with scientific evidence for improved management. Using remote sensing and Geographic Information System (GIS) techniques, this study examines the spatiotemporal changes in the Taldykol catchment area during the rapid development of Astana. In 1992, over 90% of the catchment area was grassland and vegetation. By 2022, 30% of the area became barren land. Urban areas increased by 127%, and water areas decreased by 24%. The most significant changes occurred in lakes Taldykol and Kishi Taldykol, whose areas shrank by 91% and 54%, respectively. The near-disappearance of the Taldykol wetlands is likely to contribute to rising land surface temperatures (LST), decreased natural flood control capacity, reduced biodiversity, and diminished recreational opportunities. The fate of Taldykol lakes underscores the urgent need to raise public awareness about the role of wetlands in Kazakhstan’s ecosystems and take action to preserve urban wetlands.

Hydrochemical characterization and evaluation of irrigation water quality using indexing approaches, multivariate analysis, and GIS techniques in K'sob Valley, Algeria

Irrigation plays a vital role in addressing increasing need for food production and promoting economic advancement. To meet the demands for food supply and economic progress, it is essential to underscore the significance of assessing water quality in dry regions. The current study was carried out to evaluate and predict the suitability of water quality for agricultural use in the K'sob valley in the M'sila region (Northeast Algeria). A combination of irrigation water quality indices (IWQIs), Geographic Information System (GIS) analysis and multivariate statistical methods were used for this purpose. Several physicochemical parameters, such as temperature (T°), hydrogen ion concentration (pH), electrical conductivity (EC), total dissolved solids (TDS), turbidity (Turb), chemical oxygen demand (COD), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, NO2−, NH4+, PO4− and SiO22+ were all measured from 40 samples collected at ten surface water locations during four seasons. The concentrations of the main cation and anion were shown as follows: Na+>Ca2+> K+ > Mg2+, and SO42− > HCO3− > Cl− > NO3− indicating mixed Na-Cl-K or Na-SO4 water facies. Significant seasonal variation for each parameter (T, pH, Turbidity, Salinity, COD, NH4+, Cl−, SO4−, and NO2−) was reported (p < 0.05). Additionally, a significant spatial variation (p < 0.05) was observed among different stations for the parameters: TDS, EC, Ca2+, Na+, HCO3−, SO4−, NO3−, and PO43− (p < 0.05). The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), Kelly index (KI), and permeability index (PI) had values varying between 28.1 and 56.8, 5.65 and 12.45, 75 and 87, 2.61 and 6.54 and 83, and 97, respectively, and a significant seasonal effect was recorded. According to the Wilcox diagram, 70% of samples were unsuitable for irrigation, while 30% of samples were questionable. The IWQI map revealed that 50% of the samples fell within the very poor category for irrigation, while 20% and 30% of the samples were inside the poor and unsuitable categories, respectively. The principal component analysis (PCA) and hierarchical cluster analysis (HCA) of K'SobValley water revealed three different categories of water based on elemental composition and seasonal variations. The results obtained in this study can be valuable for surface water management. Furthermore, the developed methodology can serve as a useful tool for identifying critical hydrogeochemical components in arid and semi-arid environments related to surface water.

Urban greening in Dhaka: Assessing rooftop agriculture suitability using GIS and MCDM techniques

Dhaka ranks among the world's most densely populated cities, with built-up areas expanding to accommodate the demands of a growing population. The rapid urbanization has reduced green space and exacerbated urban heat and pollution in the city. In the quest for a greener and healthier urban environment, rooftop agriculture has emerged as a promising solution, offering opportunities for the restoration of the environment and safe food production. Despite its potential, limited studies have explored the viability of this alternative greening solution for Dhaka. Therefore, this study aims to assess the suitability of rooftops for agricultural activities employing Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) techniques. First, seven criteria were selected based on the literature, such as building age, height, rooftop size, building utility, property value, sunlight, and water availability. Second, an expert opinion survey was conducted using the Best Worst Method (BWM) to calculate the criteria's weights. Finally, the suitability map for Dhaka was derived by combining the criteria layers and was subsequently validated. Rooftop area and property value were identified as the most and least important criteria. Approximately 9% (6.27 km2), 68% (46.59 km2), 22% (15.15 km2), and a negligible portion (0.1 km2) of Dhaka city has been classified as highly suitable, suitable, moderately suitable, and not suitable, respectively, for rooftop agriculture. By identifying and promoting the most suitable locations for rooftop agriculture and highlighting existing opportunities, this research will help to initiate and expand sustainable agriculture practices that can contribute to climate change adaptation and urban resilience.

Spatial mapping of dengue fever prevalence and its association with geo-climatic factors in Lahore, Pakistan.

A vector-borne disease of concern for global public health, dengue fever has been spreading its endemicity and several cases in recent years, particularly in Lahore Pakistan. Dengue transmission is influenced by geo-climatic conditions. This study aimed to map the spatial prevalence of dengue fever in Lahore and its association with geo-climatic factors during the epidemic of the year 2021. In this study, geo-climatic factors that could potentially encourage the growth of the virus are chosen for this study, and their temporal and spatial changeability relate to dengue cases. The objective of this study is to use meteorological, satellite data and Geographic Information System (GIS) techniques to map dengue outbreaks and identify the risk-prone areas by relating geo-climatic factors with dengue outbreaks. The dengue patients and their locations data were collected from the Directorate General of Health Services (DGHS) Lahore. This study uses Google Earth and Landsat-8 OLI/TIRs images to extract geo-climatic and land use parameters. The dot density maps technique was used to represent the spatiotemporal distribution of dengue cases. The hotspot analysis was applied to show the hotspots of dengue cases in district Lahore at the Union Council (UC) level. The Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), built-up area, population density, precipitation, and Land Surface Temperature (LST) are the factors employed. In this study, correlation was performed to test the significance between precipitation and the prevalence of dengue fever in Lahore. The results show that the incidence and prevalence of dengue fever month-wise at the UC level in Lahore. The distribution pattern of dengue outbreaks in the Lahore area and its demographic factors were found to be associated. It concludes that the increase in the spread of dengue fever is associated with the monsoon rains. The prevalence of dengue is associated with water bodies and high land surface temperature, but it does not represent any significant relation with vegetation cover and land use in Lahore during the year 2021. The study pinpointed the locations that are most susceptible and require care to prevent such outbreaks in the future.

Spatio-temporal analysis of riverbank changes using remote sensing and geographic information system

Fluvial hazards cause severe damage to human lives and properties. The increased intrusion of anthropogenic activities into natural hydrological cycles has increased hazard severity and confounded river channel dynamics. The present article studies the epochal channel erosion, accretion, and unaltered sites along the 42 km stretch between the Polavaram project and Dowleshwaram Barrage on the Godavari River. Geographic Information System (GIS) and Remote Sensing (RS) techniques were used to identify changes in river morphology. A total of five decades of Landsat images were analyzed, classifying the land use with a supervised learning Support Vector Machine (SVM) algorithm and an ISO cluster unsupervised algorithm to delineate the rivers and study the influence of land use land cover change (LULCC) on river morphology. Accretion and erosion channel changes during the assessment period were measured at all reaches, and the possible driving mechanisms of morphological alterations were studied.The results indicate the susceptibility of human dwellings and agricultural lands to floods due to the shifting of the highly vibrant river shoreline. The study provides the most recent information on Godavari River dynamics. These aspects of the river's evolution will aid in planning and management in the lower portions, which have not been well understood. River channel alterations can be quantitatively and economically analyzed using Remote Sensing (RS) and Geographic Information System (GIS) data.

Modeling the spatial relationship between landscape services and vulnerability assessment.

Even though many studies have been thus far devoted to landscape services (LS) or vulnerability assessment (VA) alone, the relationship between these two concepts has been less investigated. The current study attempts to model the spatial relationship between LS and VA in the Tarhan area (west of Iran). For this purpose, a combination of the analytic hierarchy process (AHP), remote sensing (RS), and geographic information system (GIS) techniques are applied to assess vulnerability. Variogram models and geostatistical simulations are used for mapping and evaluating landscape services. Moreover, the geographically weighted regression (GWR) is operated to predict the relationship between LS and VA. The results indicate that landform and socioeconomic variables are also two main factors shaping variations in VA. As well, the levels of services provided in the possible landscape are affected by the vulnerability. The assessment accordingly establishes that natural and cultural values have significant effects on services, in terms of their spatial distribution and nature. Exploring the relationship between LS and VA correspondingly depicts that VA has a direct influence on the capacity and entity of LS provision (adjusted R2 = 0.67). The outcomes of the study provide a foundation for nature management and conservation, although it is less able to analyze the trade-off between ecosystem degradation and future development trends. The nexus of LS and future ecological processes in an integrated system can be thus the subject of further research.

The toxic legacy of artisanal and informal mining: XRF and gis analysis of soil contamination by Hg, Pb and As in the Secocha annex, Camaná, Peru

Hg, used as the main input in the gold amalgamation process in artisanal mining, along with lead Pb and As present in the toxic waste resulting from mining activity, cause serious effects on the environment and human health, as it occurs in the Secocha annex (Camaná, Peru). The objectives of the present study were to determine the concentrations of Hg, Pb, and As in the soil of the Secocha annex, evaluate the level of contamination of the site, determine the spatial distribution of contamination and the concentrations of the evaluated elements, and determine the origin of these contaminants. To achieve these objectives, satellite imagery, field sampling, X-ray fluorescence techniques, soil quality environmental regulations, geo-accumulation indices (Igeo), and geographic information system techniques were used. The results revealed maximum concentrations of 350 and 176.6 mg/kg for Hg and Pb, respectively. Of the 72 soil samples collected, the concentrations of Hg located within the urban/industrial area, where gold recovery activities are carried out, exceeded environmental regulations to a greater extent. In turn, the calculated Igeo revealed extreme soil contamination (5 &lt; Igeo) due to the presence of Hg in most of the study area (95%), followed by moderate to heavy contamination (2 &lt; Igeo &lt; 3) due to the presence of Pb and As (0.5 and 5%, respectively). To reduce contamination and the concentrations of the evaluated elements, it is recommended to carry out studies to reuse inputs, change gold processing methodologies, and/or relocate the generated waste.

Integrated assessment of geophysical and social vulnerability to natural hazards in North-East Region, Romania

The multidisciplinary approach outlined in this paper aims to assess the correlation between geophysical and social vulnerability for disaster risk reduction. To achieve this, two multi-criteria methods based on Geographic Information Systems (GIS) techniques have been integrated to assess the risk to natural hazards in the North-East Development Region. The Analytical Hierarchy Process (AHP) was utilized to conduct a comprehensive hazard analysis, including floods, landslides, and earthquakes. The Principal Component Analysis (PCA) method was applied to examine the spatial distribution of social vulnerability at the Local Administrative Unit (LAU level), utilizing 24 variables to generate 6 principal components. Based on these results a Regional Risk Index (RRI) was developed using the bivariate method, combining the multi-hazard distribution with social vulnerability. The results showed that the north-eastern part of the region presents the highest risk, encompassing 10.68% of administrative units. These results can contribute to the design of risk reduction programs and policies.

Decadal Changes (1980–2021) of Shoreline and Mangrove Cover in Sundarban Delta, India, Using Remote Sensing and GIS

Abstract Being situated in the estuary of the flood-dominated Hooghly River system, the macrotidal Indian Sundarban Delta (ISD) has become one of the most complex, dynamic, and rapidly changing landforms on Earth’s surface. To study the horizontal areal shifting of shoreline and its impact on mangrove cover in the region, the U.S. Geological Survey (USGS) satellite data of 1980, 1990, 2000, 2010, and 2021 were used. Remote sensing and geographic information system (GIS) techniques were employed in the investigation. Simultaneous prograding and retrograding shoreline shifting was distinguished almost in all the parts, although sediment-starved eastern and macrotidally more active southern lobes experienced dominantly retreating shift, and the sediment-engorged western lobe was demonstrated to be more dynamic. Net areal change over north–south tracks followed the trend of decreasing accretion to increasing erosion while going from west to east, whereas that over west–east tracks followed the trend of exponentially increasing erosion while going from north to south. Overall accretion of ∼91 km2 in the ISD accounted for the augmentation of sparse vegetation of ∼13 km2, whereas ∼243 km2 erosion called for the depletion of sparse and moderate vegetation of ∼18 and ∼174 km2, respectively, over the 41-yr period. Various oceanographic and riparian forces and actions, episodic natural events, etc., vis-a-vis several anthropogenic interventions—all together contributed to such changes. The findings may help the coastal environmentalists, professionals, planners, decision-makers, and implementers in formulating and taking up of suitable strategic measures for integrated and effective coastal zone management in this estuarine wetland forest.

Exploring Sleep Duration and Insomnia Among Prospective University Students: A Study with Geographical Data and Machine Learning Techniques.

Sleep disruptions among prospective university students are increasingly recognized for their potential ramifications on academic achievement and psychological well-being. But, information regarding sleep issues among students preparing for university entrance exams is unknown. Thus, this study aimed to investigate the prevalence and factors associated with sleep duration and insomnia among university entrance test-takers in Bangladesh, utilizing both traditional statistical analyses and advanced geographic information system and machine learning techniques for enhanced predictive capability. A cross-sectional study was conducted in June 2023 among 1496 entrance test-takers at Jahangirnagar University, Dhaka. Structured questionnaires collected data on demographics, academic information, and mental health assessments. Statistical analyses, including chi-square tests and logistic regression, were performed using SPSS, while machine learning models were applied using Python and Google Colab. Approximately 62.9% of participants reported abnormal sleep duration (<7 hours/night or >9 hours/night), with 25.5% experiencing insomnia. Females and those dissatisfied with mock tests were more likely to report abnormal sleep duration, while repeat test-takers, those with unsatisfactory mock test results, or anxiety symptoms had a higher risk of insomnia. Machine learning identified satisfaction with previous mock tests as the most significant predictor of sleep disturbances, while higher secondary school certificate GPA had the least influence. The CatBoost model achieved maximum accuracy rates of 61.27% and 73.46%, respectively, for predicting sleep duration and insomnia, with low log loss values indicating robust predictive performance. Geographic analysis revealed regional variations in sleep disturbances, with higher insomnia prevalence in some southern districts and abnormal sleep duration in northern and eastern districts. Sleep disturbances are prevalent among prospective university students and are associated with various factors including gender, test-taking status, mock test satisfaction, and anxiety. Targeted interventions, including sleep education and psychological support, hold promise in ameliorating sleep health and overall well-being among students, potentially enhancing entrance test performance.

Assessing the Regional Socio-Economic Disparities, Evidence from Jordan

Objectives: Regional disparity in development among regions in developing countries is a common issue. Despite being a small, emerging economy, Jordan suffers from this problem. This study aims to identify and explore regional disparities in Jordan between 2010 and 2018 by integrating social and economic factors. Methods: A mixed-methods approach combining statistical analysis and Geographic Information Systems (GIS) techniques was employed to uncover the economic and social disparities in Jordan, and classify the governorates accordingly. This study presents the results of the Lorenz curve and the site quotient and shows the direct interactions between social disparities on economic disparities in Jordan. Results: The results indicated significant differences between the twelve governorates in Jordan. The capital city, Amman, possesses the highest share of social and economic factors in Jordan and is the only governorate with the highest economic growth. The second and third-ranking governorates in terms of ratios are Irbid and Zarqa. Conclusions: The study recommends implementing balanced and obligatory regional development plans, utilizing an integrated team of experts, decision-makers, investors, and local representatives. Emphasizing the importance of decentralization and empowering local councils in the governorates is crucial.

Application of Geographic Information Systems (GIS) in the Study of Prostate Cancer Disparities: A Systematic Review.

Introduction: PCa is one of the cancers that exhibits the widest disparity gaps. Geographical place of residence has been shown to be associated with healthcare access/utilization and PCa outcomes. Geographical Information Systems (GIS) are widely being utilized for PCa disparities research, however, inconsistencies in their application exist. This systematic review will summarize GIS application within PCa disparities research, highlight gaps in the literature, and propose alternative approaches. Methods: This paper followed the methods of the Cochrane Collaboration and the criteria set of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in peer-reviewed journals were searched through the PubMed, Embase, and Web of Science databases until December 2022. The main inclusion criteria were employing a GIS approach and examining a relationship between geographical components and PCa disparities. The main exclusion criteria were studies conducted outside the US and those that were not published in English. Results: A total of 25 articles were included; 23 focused on PCa measures as outcomes: incidence, survival, and mortality, while only 2 examined PCa management. GIS application in PCa disparities research was grouped into three main categories: mapping, processing, and analysis. GIS mapping allowed for the visualization of quantitative, qualitative, and temporal trends of PCa factors. GIS processing was mainly used for geocoding and smoothing of PCa rates. GIS analysis mainly served to evaluate global spatial autocorrelation and distribution of PCa cases, while local cluster identification techniques were mainly employed to identify locations with poorer PCa outcomes, soliciting public health interventions. Discussion: Varied GIS applications and methodologies have been used in researching PCa disparities. Multiple geographical scales were adopted, leading to variations in associations and outcomes. Geocoding quality varied considerably, leading to less robust findings. Limitations in cluster-detection approaches were identified, especially when variations were captured using the Spatial Scan Statistic. GIS approaches utilized in other diseases might be applied within PCa disparities research for more accurate inferences. A novel approach for GIS research in PCa disparities could be focusing more on geospatial disparities in procedure utilization especially when it comes to PCa screening techniques. Conclusions: This systematic review summarized and described the current state and trend of GIS application in PCa disparities research. Although GIS is of crucial importance when it comes to PCa disparities research, future studies should rely on more robust GIS techniques, carefully select the geographical scale studied, and partner with GIS scientists for more accurate inferences. Such interdisciplinary approaches have the potential to bridge the gaps between GIS and cancer prevention and control to further advance cancer equity.

Mapping of the Seasonal Migration Routes of Cattle Pastoralists of the Deccan Plateau Region of India Using Ethnographic Geographic Information System Technique

Mapping of the Seasonal Migration Routes of Cattle Pastoralists of the Deccan Plateau Region of India Using Ethnographic Geographic Information System Technique

Landslide hazard zonation (LHZ) mapping of Doon Valley using multi-criteria analysis method based on remote sensing and GIS techniques

The occurrence of landslides is a costly and cataclysmic natural hazard that mainly occurs in hilly areas due to factors like earthquakes, cloud burst, extreme rainfall, human pressure, etc. leading to loss of biodiversity, property, and life. Effective and comprehensive landslide risk management is crucial to address landslide susceptibility. Utilizing remote sensing and Geographic Information System (GIS) techniques, this work focuses on the landslide hazard zonation (LHZ) mapping. The study is specifically conducted in the Doon Valley. These advanced technologies help in identifying and categorizing the areas prone to landslides. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data at 30 m resolution and Sentinel-2B data at 10 m resolution were used to perform the remote sensing and GIS operations in ArcGIS Software. Thematic layers such as Land Use/Land Cover (LULC), Slope, Geology, Soil Type, Elevation, Drainage Density, Vegetation, and Aspect were produced utilizing remote sensing and GIS data. The weighted overlay, a multi-criteria analysis method, was applied to assign attribute values to each thematic layer based on their importance, which was then combined to calculate the landslide hazard zone. The findings of this study demonstrate that landslides are more likely to occur near and above the Main Boundary Thrust/Fault (MBT). The high to very high hazard zone covers 16.64% of the total area, making landslides occurring more frequently. Additionally, the study found that the Doon Valley rivers’ upper segments are more susceptible to landslides than their lower segments. By integrating Remote Sensing and GIS techniques, it is possible to obtain extensive knowledge of regions prone to landslides. This information will be helpful for decision-makers and planners to reduce the impact of landslides in the near future.

Effective Monitoring of Noyyal River surface water quality using remote sensing and Machine learning and GIS techniques

This study utilizes Geographic Information System (GIS) and remote sensing techniques to predict water quality metrics in the Noyyal River. Satellite data is employed to construct statistical models, with preprocessing involving adjustments from Landsat 8 images. The hybrid LASSO model demonstrates superior performance in predicting water quality parameters, supported by key performance metrics such as RMSE, R-squared, and ANOVA results. The study focuses on assessing the suitability of the hybrid LASSO model for predicting the Water Quality Index (WQI) in the Noyyal region, highlighting its ability to handle high-dimensional data and provide interpretable results. Prediction models employing the LASSO approach yield promising results, with R-squared values exceeding 0.87 for temperature and pH. Incorporating spectral indices significantly enhances model performance, with an average R-squared of 0.8. These models offer cost-effective options for monitoring water quality, revealing poor conditions in the Noyyal River and projecting deterioration for 2023. WQI ratings indicate poor conditions, particularly during July and August 2022 and April 2023, providing valuable insights for local pollution regulation enforcement.

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

&lt;p&gt;Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.&lt;/p&gt;

Investigation of the Basin Characteristics through Morphometric Analysis of Hadejia River Sub-Basin: Implications for Groundwater Recharge

Understanding the geohydrological properties of a drainage basin in relation to the topographical feature and its flow patterns depends heavily on morphometric analysis. Estimating a watershed's frequency of infiltration and runoff as well as its other hydrological characteristics is also helpful. The study was conducted using Geographical Information System (GIS) techniques with the aim of establishing relationship between surface morphometry, underlying geology and groundwater recharge. For detailed measurement and analysis, Digital Elevation Model (DEM) and high resolution imageries were employed for basin delineation, slope characterization, channel network extraction and stream ordering in order to derive the linear, areal, and relief aspects of morphometric parameters. The findings showed that a total number of 116 streams joined the 4th order stream in which 83 streams were 1st order, 25 streams were 2nd order, 7 streams were 3rd order and the major trunk was 4th order stream, occupied an area of 1486.86km2. The stream network's drainage system exhibits dendritic design. The results further indicate that the values for stream frequency, infiltration number, drainage density, drainage texture, length of overland flow, elongation ratio and basin relief are 0.08, 0.032, 0.41km/km2, 0.023, 1.22km, 0.54 and 28.59m respectively. The observed values of both linear, areal and relief parameters were generally low. Low values for the areal and relief criteria indicates that the sub-basin is at its youthful stage of development and possesses very good permeable subsurface formation and prospect with the possibility of high potential groundwater resources. The result help us understand the connections between hydrological variables and geomorphological parameters as guidance and/or decision-making instruments for the authorities to develop decisions for the environmentally friendly growth of the basin, water supply planning, water budgeting, and disaster mitigation within the Hadejia river sub-basin.

Securing water for arid regions: Rainwater harvesting and sustainable groundwater management using remote sensing and GIS techniques

Arid regions experience climatic stress under climate change: increased drought frequency coupled with intensified storm events. This disruption and lack of precipitation patterns leads to water scarcity and hinders the achievement of sustainable development goals. Egypt drainage basin exhibiting the greatest suitability for the implementation of rainwater harvesting (RWH) strategies. To facilitate the development of sustainable water resource management practices in the region, this study uses a multi-criteria methodology to delineate optimal zones for RWH within the Wadi Safaga. Integration of radar and optical remote sensing data obtained from Sentinel-1&2, Landsat-8, ALOS/PALSAR, and Sentinel-1 Interferometric SAR with climatic Tropical Rainfall Measuring Mission (TRMM), hydrological, and geological datasets emphasizes the hydrologic characteristics of the catchments. Additionally, the analysis of rainfall intensity patterns within the basin was undertaken. Thirteen factors are used in the predicted model including elevation, slope, curvature, depression, lithology, radar, InSAR CCD, drainage density (Dd), distance to river (DR), vegetation, topographic wetness index (TWI), rainfall, and lineament density. A knowledge-driven Geographic Information System (GIS) methodology, including weighted factors based on the Analytical Hierarchy Process (AHP), was implemented to delineate plausible areas for RWH and groundwater potential zones (GWPZs). The resultant map categorized the basin into five GWPZ classes: very low (14%), low (28%), moderate (27%), high (21%), and very high (10%). Furthermore, the study identified optimal locations for constructing reservoirs to store harvested rainwater and provide protection for downstream mining, industrial, and tourism activities. In conclusion, the obtained information is crucial for planners and decision-makers to implement sustainable water resource management strategies within the Wadi Safaga basin.

Research on the Spatiotemporal Distribution Characteristics and Accessibility of Traditional Villages Based on Geographic Information Systems—A Case Study of Shandong Province, China

The traditional settlements are of paramount significance as indispensable elements of China’s cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China’s rural revitalization endeavor and Shandong Province’s proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from “southwest to northeast”; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent “four-core” spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility.

Geospatial assessment of built environment on land surface temperature in district Sheikhupura, Punjab Pakistan

The built environment is associated with higher air and surface temperatures in urban areas compared to rural counterparts. This study focuses on the Sheikhupura district in Punjab, Pakistan, where rapid urbanization and deforestation have significantly impacted land surface temperature (LST) from 2000 to 2020. Utilizing Geographic Information Systems (GIS) and Remote Sensing (RS) techniques, four Landsat images from the United States Geological Survey (USGS) were analyzed to assess spatiotemporal variations in LULC and LST. The single infrared channel approach was employed to calculate LST, and LULC maps were created using supervised classification for the years 2000, 2010, and 2020. The results indicate a substantial increase in built-up areas from 66.9708 square kilometers in 2000 to 259.8147 square kilometers in 2020, accompanied by a significant reduction in vegetation and agricultural land. Correspondingly, the maximum LST rose from 28.9354 °C in 2000 to 31.5173 °C in 2020, highlighting the urban heat island effect. Zonal statistics revealed a decrease in mean high temperatures from 24.45 to 20.3 °C over the same period, suggesting variability in temperature distribution due to changes in land cover types. These findings underscore the need for sustainable urban planning to mitigate the adverse effects of urbanization on local climates. Future work should focus on developing strategies to increase green spaces and reforestation efforts to counteract rising temperatures. Additionally, further research is recommended to explore the long-term impacts of these changes on local biodiversity and ecosystem services, providing a holistic approach to urban sustainability.