Geographic Information System Platform Research Articles

A digital twin prototype to visualize heterogeneous seismic damage simulation results on web-GIS

ABSTRACT Access to a wide range of information tailored to specific purposes, including extensive damage projections and statistics for target areas and the potential for individual building and dwelling damage predicted through simulations, enhances cities’ resilience against large-scale earthquakes. Recognizing the significance of this, we have developed a prototype of a digital twin capable of comprehensively visualizing various simulations on a web-based geographic information system (GIS) platform. This prototype employs three seismic damage simulators: the Integrated Earthquake Simulation for area damage, the wallstat for wooden houses, and the E-Simulator for a reinforced concrete building. A trial of earthquake damage simulations was conducted to assess this prototype’s efficacy, focusing on the damage incurred in Mashiki Town during the 2016 Kumamoto earthquake. The trial successfully verified the capability of the system to visualize and integrate various types of simulation data alongside records obtained from actual earthquake disasters. This paper provides an overview of the digital twin prototype. It reports on the trial’s results, focusing on the visualization and integration achieved through this trial.

Using public participation gis to understand texas coastal communities’ perceptions and preferences for urban green space development in connection to their perceptions of flood risk

Urban green spaces, a form of green infrastructure, provide cities with a number of opportunities to build urban resilience to withstand a wide range of urban challenges, such as land-use change, climate change, biodiversity loss, and physical and mental health. Despite their benefits to communities, urban green spaces are among the most valuable and attractive areas for development and are most susceptible to urban development. Therefore, careful planning of the green space network is needed,requiring diverse voices from not only city policymakers and planning experts but also local residents. This study investigated residents’ perceptions and preferences for green space development and its association with perceived flood risk. Data were collected from residents residing in two Texas Gulf Coast cities via a Public Participation Geographic Information System (PPGIS) platform. Our analyses revealed that Texas Gulf Coast residents assigned a full spectrum of landscape values to their existing urban green spaces with aesthetic and recreation values being most frequently identified. Notably, across the city landscape, urban green spaces that were most favored by residents tended to be green spaces with water bodies, large in scale and offered a broader array of facilities/amenities. These spaces were also identified as flood-prone areas, presenting opportunities for green space improvement, flood protection, and preservation. In addition, we observed a significant association between residents’ risk perception and the number of locations across the city landscape they perceived as posing the greatest flooding threat. Our findings illustrate a need to promote multiple landscape values of urban green spaces, especially in its ability to protect the city from future flooding. This research highlights how PPGIS can support green infrastructure planning as well as enhance public participation in green infrastructure planning processes.

Geoinformatics and RUSLE model-based soil erosion assessment in a tropical mountainous area of Chite watershed, Mizoram, India

Soil erosion remains a persistent menace to the sustainability of agriculture and the environment in tropical mountainous regions. Soil erosion assessment is therefore necessary to identify degraded land areas for implementing effective conservation and management strategies. Hence, this study focuses on estimating potential soil erosion and analyzing their spatial patterns in the Chite watershed, situated in the Eastern Himalayas, India, using the Revised Universal Soil Loss Equation (RUSLE) model in Geographic Information System (GIS) platform. Various datasets encompassing remote sensing, ground observations, and laboratory analysis were employed to prepare the model’s input factors. The estimated mean erosion rate of the study area is 6.10 t ha-1 year-1, which produces a total soil loss of about 357580.90 t year-1. Spatial analysis reveals that about 5.79% of the watershed is under a relatively severe erosion category, contributing 70.13% of the total soil loss. Soil erosion appraisal with respect to the land use/ land cover (LULC) indicates a considerable consequence of various anthropogenic activities in the watershed. Higher rates of soil erosion are mainly observed on the bare land, cropland, and settlement areas which are characterized by steep and continuous slopes. The present findings were also validated with previous work undertaken in some comparable regions. This research can serve as a reliable tool towards the development of successful soil conservation measures and for promoting sustainable land use planning in this ecologically sensitive tropical mountainous region.

Hydrogeophysical characterization of the Bir Guendouz basin (SW Morocco): Insights into its relations to the Senegal-Mauritanian basin

The Senegal-Mauritanian Basin onshore is the largest among the northwest African Atlantic margin basins, covering approximately 350,000 km2 and extending from Morocco to Guinea Bissau. Although valuable information is provided on the Senegalese part, data on other parts of the basin are scarce. The study aims to visualise the subsurface structure and describe the distribution patterns of Meso-Cenozoic deposits in the Moroccan portion of the Senegalo-Mauritania basin. The dataset integrates information from 750 vertical electrical soundings, hydrogeological drilling data, and wireline logging into a Geographic Information System (GIS) platform and 3D modelling software. A new three-dimensional model has been created to represent the topmost layer of the Palaeozoic substratum. Additionally, thickness maps and geo-electric profiles have been developed for distinct geo-electric intervals, including the lower, middle, upper, and surface layers. The study presents the geometric properties of two aquifers, namely, the ''upper and lower'': the Continental Terminal sandy and sandstones, dating back to the upper Miocene to Pliocene period, and the Paleocene sand to shaly sand. The upper aquifer has a thickness of 20 m in the eastern region, whereas it thickens to nearly 100 m towards the west, approaching the coastline. Similarly, the lower aquifer's thickness ranges from 100 m to 400 m in the same direction. The resistivity values range from 5 to 240 Ω m, providing an average of 120 Ω m. Four discontinuities have been identified in the sections, which may indicate lateral facies modifications or synsedimentary faults, correlating with Atlantic margin structuration and Meso-Cenozoic sea-level variations. The parameters' range for various mappings, models, and quantitative descriptions of reservoir properties. The new structural and hydro-stratigraphic model provides novel insights for researching this critical area.

Integration of Remote Sensing and Aerogeophysical Data Applied in the Geological Characterization of the Ipueira-Medrado Segment, Andorinhas/BA

The segment of the Ipueira-Medrado mines constitutes two of the main chromium mines operated by Companhia Ferro-ligas da Bahia (Ferbasa). The research area is located in the municipality of Andorinhas, in the north center of the state of Bahia, in the Piemonte Norte Do Itapicuru region. Therefore, the main objective of the present work is the use of Remote Sensing and Aerogeophysics data to elaborate the geological map of the study area and highlight new target areas for mineral research. The Digital Image Processing (PDI) stage was carried out in an environment in the ENVI® 5.3 software for the scenes of the optical Alos and radar images, Palsar. The treatment of magnetic and radiometric data was carried out in the environment of the software Oasis Montaj 9.10 by Geosoft®. In the Geographic Information System (GIS) platform of ArcGis 10.7.1®, multisource data was integrated. This integration supported the screening of information from local lithologies and the identification of four lithological units: Sienito Itiúba, Santa Luz Unit, São Bento Unit and Jacurici Valley Complex. In this last unit, a combined analysis between Map Algebra and Fuzzy logic was applied to all input raster data, in order to mark out a zoning of potential areas for the occurrence of mafic-ultramafic rocks. In the end, a good relationship was noted between the results obtained with models that already exist in the bibliography. In view of this, the application of the methodology developed in this research requires more robustness for the local geological knowledge, as it will also help in the development of new mineral research endeavors.

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

An innovative approach for equitable urban green space allocation through population demand and accessibility modeling

Urban green space (UGS) plays a pivotal role in enhancing citizens' living standards, well-being, and overall physical and mental health. As urbanization continues to progress, ensuring equitable access to UGS becomes increasingly vital in addressing the social resource disparities. This paper proposes an innovative approach to inform UGS planning and updates, placing a strong emphasis on equitable UGS distribution. In pursuit of a more balanced UGS layout, we present a novel recreational demand model that can evaluate the citizen's recreational preferences by taking into account the population activity levels and the agglomeration effect. Leveraging point of interest (POI) data within the geographic information system (GIS) platform, we quantitatively analyze these factors. Additionally, we propose a refined resistance model, enhancing the accuracy of UGS update cost estimation. Applying these models, we undertake a case study in Changsha City to advance UGS update strategies. Our findings reveal disparities not only across different city sectors but also within individual spaces, underscoring the unequal distribution of diverse UGS resources. The proposed models can effectively guide the incorporation of new UGS points in urban planning, mitigating the challenge of inequitable UGS resource allocation. The study provides a new approach to more comprehensively evaluate the UGS quality and offers additional valuable insights for improved UGS planning, and therefore, contributes to fostering a more just, inclusive and sustainable urban environment.

Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information

River water is one of the most important natural resources for economic development and environmental sustainability. However, river water systems are vulnerable in some of the densely populated regions across the globe. Intense sand mining and waste disposal can lead to river changes, loose foundations, and biodiversity loss. This study aims to develop a web-based geographic information system (GIS) platform to monitor river channel changes and their impacts on river environments due to sand mining and waste burial. The system integrates open-source software, Windows Server 2012, a web server, and PostgreSQL with PostGIS plugins for efficient mapping and storage of geospatial data and volunteered reporting of location events. Interferometric methods, including SNAP2STAMPS Automated Algorithm, persistent scatterer interferometry (PSI), small baseline subset (SBAS), and Snap software, were used to analyze spatial changes of subsidence from Sentinel-1 satellite data from 2021 to 2023 in the Gadar River in Oshnavieh, Iran. The results showed that the maximum subsidence at the riverbank was −10.1 cm due to sand mining, and the maximum uplift was 8.2 cm due to waste landfilling. The average subsidence was reported to be −5.1 cm. The results emphasize spatial analysis, showcasing material mining’s impact on subsidence trends and underscoring the significance of public participation in monitoring river health. Three parameters—completeness, correctness, and quality—were used to validate the system. Validation results showed completeness, correctness, and quality rates of 94.15%, 92.48%, and 86.63%, respectively.

Building Information Modeling and Geographic Information System: Integrated Framework in Support of Facility Management (FM)

Currently, the management of public real estate stocks is one of the most challenging activities. In this case, facility management emerges as a strategic but complex discipline that requires a great amount of integrated and structured data and information of heterogeneous origin and nature. Starting from this premise, this paper focuses on how FM discipline can be supported by Building Information Modeling (BIM) and Geographic Information System (GIS) integrated methodologies and tools achieving high levels of effectiveness and efficiency in public building stock management. This led to the definition and development of a framework that mainly takes advantage of open-source tools (e.g., Quantum Geographic Information System—QGIS, and Dynamo) and non-proprietary data exchange formats like the COBie (Construction Operations Building Information Exchange) international standard. This methodological framework is capable of integrating different but strongly complementary tools, data, information, and expert knowledge for a multiscale approach. The methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform but leads instead to an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. The findings of experimental applications of the methodological approach to the former Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are presented and discussed. Finally, the analysis about potentials and criticalities of the developed methodological framework aims to trigger the widest future development and dissemination of this research.

Multivariate Statistical Analysis and Its Significance In Drainage Morphometry of Santanavardhani Watershed, Tamil Nadu, India

The present study aims to examine the significance of multivariate statistics in studying morphometric behaviour and its relationship in 13 sub- watersheds of Santanavardhani Watershed, Tamil Nadu. Geographic Information System (GIS) based drainage morphometric analysis has been carried out to decipher the geomorphic behaviour of watershed. Different parameters of Linear, Areal and Relief aspects are considered and quantify with the help of Survey of India (SOI) topographic maps and Shuttle Radar Topographic Mission (SRTM) based Digital Elevation Model (DEM) in GIS platform. Correlation Analysis is carried out for twelve morphometric parameters to measure the linear relationship between the variables using Pearson Product Moment Correlation (PPMC) method. Most of the variables show a strong correlation and represent similar geomorphic characteristics. The correlated data was further used to group the variables into different components using Factor Analysis (FA) and it gives 3 different components like Factor of steepness, Factor of magnitude and Factor of geometry. Hierarchical Clustering method (Ward's method) is implemented to group different sub-watersheds based on its geomorphic behaviour. Cluster-1 consists of sub-watersheds S1, S2, S13, S4, S11, S5, S9, S10 shows homogeneous nature and Cluster-2 and Cluster-3 comprises S6, S12, S3 and S7, S8, respectively show heterogeneity of the sub-watersheds. Keywords: Quantitative Drainage Morphometric Analysis, Correlation Analysis, Factor Analysis, Cluster Analysis, GIS

Automated mapping process of frontal area and thermal potential indexes: GIS algorithm development and implementation

The impacts of urban areas on microclimate conditions are well-known and highlight the importance of climatological guided urban planning. The growth and evolution of Geographic Information Systems (GIS) have set a backdrop for implementing strategies to apply urban climatological knowledge in planning daily practice. This research brings algorithms to automate the Frontal Area Index (FAI) and Thermal Potential Index (TPI) mapping process. This type of algorithms type facilitates the extraction of detailed spatial information for the decision-making process, making it highly relevant for urban planning and management. Their calculation method is implemented as ESRI © ArcGIS Pro embedded Python Stand Alone Script Tools, using the Python ArcPy library to access the ArcGIS geoprocessing functions. The algorithms are described in detail, allowing their implementation in other GIS platforms. The output maps allow the urban thermal conditions and morphology assessment. The findings from this research may substantially contribute both to the advance in the urban climatology scientific field and to guide urban planners' and managers' practical decision making.

A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

A Web-Based Geodesign Tool for Evaluating the Integration of Transport Infrastructure, Public Spaces, and Human Activities

The need for addressing the adverse impacts of transport infrastructure on public spaces and human activities (TSH) emphasizes the importance of designing integrated TSH system, thereby necessitating tailored planning support systems (PSS). This study begins by assessing the demand for PSS using surveys and interviews to uncover the need for robust analysis and evaluation support, particularly through the use of geographical information systems (GIS). On this basis, a prototype GIS platform is proposed for analyzing and evaluating the integration of the TSH system at the block scale. This user-friendly geodesign tool encompasses a customizable evaluation index (includes seven KPAs and KPIs), allowing for combined quantitative and qualitative assessments. Notably, it introduces a buffer effect index to quantify transport–space interaction. The proposed tool serves as a dedicated platform for evaluating TSH systems, offering 2D/3D visualization capabilities and two analysis units and facilitating cross-platform collaboration. Applied to a case study in Nanjing, China, it effectively assessed the interdependence among different TSH system components and block integration around expressways, railways, and main roads. This tool holds promise in offering invaluable insights into urban planning and (re)development, thereby enhancing the integration of transport infrastructure and public spaces.

Geospatial delineation of flood susceptible zones using analytical hierarchy process

Demarcation of flood-prone areas based on risk level plays a key role in flood management systems. Geospatial approach has gained much significance among the researcher working in flood risk management. This work incorporated an analytic hierarchy process (AHP) and geographic information system (GIS) to delineate the flood susceptible zones of the lower Damodar River basin. Various influencing factors were initially assigned with weights with respect to their relative importance towards flood susceptibility and integrated into GIS platform by weighted overlay approach. Based on the current study area, the flood zone map was sub-classified into four zones, namely critical, high and low, and safe zone, respectively. Moreover, sensitivity analysis was also performed to evaluate the most significant factors in flood susceptibility using the exclusion of factors from the estimation of flood zone index. Sensitivity analysis showed that River distance and elevation are the most sensitive factors for evaluating flood zonation.

Delineation of Groundwater Potential Zones Utilising Geospatial Multi Criteria Technique in Jakholi Block of a Himalayan District, Rudraprayag, India

Groundwater mapping is critical for satisfying people’s water needs. An attempt has been made to delineate the groundwater potential zone for Jakholi block in the Rudraprayag district of Uttarakhand, India. An innovation in groundwater research that aids in evaluating, observing, and sustaining groundwater resources is the coalscence of remote sensing data with the Geographical Information System (GIS) in order to groundwater resources. Several groundwater potential zones for assessing groundwater availability in Jakholi block have been identified in this article utilizing remote sensing and GIS methodologies. Throughout the study, nine thematic layers were evaluated for designating groundwater potential zones: geomorphology, geology, lineament density, drainage density, slope, soil texture, precipitation, relief and land use/land cover. Thematic layers were then transformed to raster using a GIS platform. After giving weights and score to individual layer, overlay analysis has been practised to identify five zones: very good, good, moderate, poor, and very poor. The majority of the territory is in a zone with poor groundwater prospect. The outcome illustrates groundwater potential zones in the research region and has been shown to be useful in improving ground water resource planning and management.

Model-based prediction of a vacant summer niche in a subarctic urbanscape: A multi-year open access data analysis of a ‘niche swap’ by short-billed Gulls

Gulls belong to the seabird group, and are widely considered to be useful ecological indicators. Increasing urbanization throughout the Anthropocene has led to the rise of the urban landscape (‘urbanscape’) as a globally dominant habitat. Though historically less developed, similar urbanization patterns are emerging in the boreal forest, the world's largest forest ecoregion. Here we evaluate the ecological position of migratory Short-billed Gulls (Larus canus) in a subarctic urbanscape. We attempt to investigate a summer niche swap theory, in which gulls annually fill a vacant niche otherwise occupied by common ravens in winter. For that investigation we conducted seasonal plot surveys for Short-billed Gull presence from 2013 to 2016 in the Fairbanks, AK municipality. All data were made publicly available. We compiled them in a open source and ESRI geographic information system (GIS) platform and then added 68 open-access predictor layers, including socio-economic U.S. Census data. We trained, tested, and evaluated the performance of an ensemble of machine learning models, resulting in predictions of gull-abundance hotspots and coldspots, at 100-m resolution for inference. We find that Short-billed Gulls prefer the synergy of industrial areas near man-made water bodies, impervious surfaces, gravel pits, strip malls, transfer sites (garbage dumps) and some young forest vegetation. This study is a first-known attempt to utilize a blended ‘Big Data’ approach, in combination with traditional multi-year field-based data collection and alternative model assessments, in order to characterize an urban seabird niche. Our findings, and the digital infrastructure herein, provide an interdisciplinary baseline for potential applications in urban planning and monitoring the spread of disease reservoirs.

A national scale web mapping platform for mainstreaming ecosystem services in Greece

This study outlines the conceptualisation and development of a public participation geographic information system (ppGIS) and web GIS (webGIS) platform for hosting, visualizing, and updating ecosystem services (ES) related data at national scale through participatory mapping. The ppGIS/webGIS is developed within the framework of the LIFE-IP 4 Natura, a project has received the most substantial nature-focused funding scheme for Greece approved by the European Commission. One of the project's central goals is the mapping and assessment of ecosystems and their services (MAES) in Greece. This initiative contributes to the implementation of Action 5 of Target 2 of the European Union (EU) Biodiversity Strategy to 2020, following the standard EU MAES guidelines. The LIFE-IP 4 Natura online mapping infrastructure hosts and visualizes spatially explicit maps and information produced earlier in the project at a national level. It also provides a mechanism for updating this baseline information via public participation, thereby fostering increased public awareness of the necessity for ecosystem protection, conservation, and management. Following a multi-stakeholder user requirement analysis, the herein presented ppGIS/webGIS platform is designed and developed. The platform incorporates various geospatial data explorers (GDE), ppGIS services for local and national public participation, geotools and data, visual content representation, and an introductory-level knowledge repository for ES (ES-pedia). An, overview of the platform's preliminary usage is presented, and its potential in promoting the uptake and mainstreaming of the ES concept is discussed.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method.

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km2), moderate (76.44%; 2274.4 km2), and high (11.267%; 345.943 km2), in sequential sequence. These categories define the model's output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study's findings to better inform future regulatory initiatives.

Using web GIS to promote stakeholder understanding of scientific results in sustainable urban development: A case study in Bergen, Norway

AbstractThis paper highlights the importance of improving stakeholder understanding of environmental factors such as air quality and the urban heat island effect to achieve sustainable development goals. While accurate spatial and temporal environmental data are critical for sustainable solutions, the challenge lies in translating complex environmental information for non‐specialists. To solve this problem, we propose using web GIS storytelling as a powerful tool for communicating and disseminating information about the environment. This paper aims to show example of implementation of a web‐based geographic information system (GIS) on our case study in Bergen, Norway. This platform “Web GIS Bergen, air quality and thermal comfort” was created as an open portal. This document explains the components of the platform, their rationale, and implementation, which includes important steps. They involve the collection of relevant environmental data, including modeling results and remote sensing data. Futhermore, the design and interface of the platform has been carefully considered to provide a user‐friendly experience based on interactive maps and storytelling. Combining data visualization and storytelling, the platform promotes a comprehensive understanding of complex environmental data, including “invisible killers” such as air pollution and the urban heat island effect, which significantly affect sustainable development. The strategic integration of this web‐based GIS platform in Bergen can serve as an attractive model for other urban areas looking for practical tools for sustainable development, engaging stakeholders for a healthier and more sustainable urban future.

Analysis of Çanakkale City Center Active Green Area Potential in GIS Platform

While cities are facing challenges such as uncontrolled growth, population growth, and climate change, environment and human-friendly solutions are sought to create more livable urban areas. Green areas in cities play an important role in improving the quality of life quality of life for residents and reducing the risks of the disasters. Urban green area planning is a holistic and strategic process in which qualitative and quantitative factors should be considered in detail. This study focuses on determining the qualities and potentials of urban green areas in the city center of Çanakkale province in western Turkey and determining the amount of green area per capita, which is one of the quantitative factors. In addition, the study also aims to draw conclusions about the distribution and accessibility of urban green areas. Determining the amount, accesibility and distribution of active green areas, which are intensively used by urban dwellers for recreational purposes and are the determinants of the presence of green area in the city, is important in terms of forming the basis for strategies that improve urban quality of life. In this study, the active green areas in various urban landscapes were determined by utilizing high spatial resolution satellite remote sensing images and the spatial distribution and accessibility analysis were performed in a geographic information systems (GIS) platform. The findings of this study provide a basis for the development of local green area strategies and contribute to the current discourse on livable and sustainable urban development.