GIS Odyssey Journal

ASSESSMENT OF LANDSCAPE VEGETATION COVER IN THE CLIMATE CHANGE ERA USING GEOSPATIAL DATA AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF KHINIS, KURDISTAN REGION, IRAQ

Diman Zuhair Jacksi — 2026-05-23

The spatiotemporal changes of vegetation in the Khinis area, Kurdistan Region of Iraq, were investigated using satellite images (Landsat and Sentinel) between 1977 and 2021. Based on its historical and ecological significance, this study investigates land cover and natural resources condition changes. The Normalized Difference Vegetation Index (NDVI) was calculated using remote sensing and GIS, and the statistics were evaluated in the Statgraphics Centurion. The result showed a decrease in vegetation cover. Increased vegetation was seen during wet years, while decreased vegetation was observed for sparse vegetation (desert) during dry years. Moreover, seasonally, spring had a moderate vegetation increase while winter and summer exhibited minimum and lower values respectively based on Sentinel data. The analysis of vegetation indices suggests that the vegetation in the study area is declining as a result of climate and anthropogenic factors, which implies that effective conservation and sustainable development management are needed to prevent and reduce land degradation of the Khinis area, which is part of its rich natural and cultural heritage.

MANAGEMENT BY … (MANY): EVOLUTION OF THE WEG MODEL FROM DRUCKER TO THE SCALABLE 21ST CENTURY

Tihomir Luković — 2026-05-23

This study examines the evolution of Management by Objectives (MBO) from Drucker (1954) through Deyhle’s WEG model (1980) to the concept of Management by… (many) as a scalable approach for VUCA environments. The research focuses on extending Deyhle’s five mechanisms (Objectives, Participation, Delegation, Exception, Results) into ten modules (including Time, Risk, Sustainability, Customer), illustrated through a case study of mangulica pig production. The limitation of the original WEG model lies in its rigidity when applied to complex value chains. The case highlights operational challenges such as yield efficiency (80%), low mortality (<2%), time constraints (Christmas 2027), and sustainability targets (CO₂ <2 t per pig). Traditional reactive controlling is insufficient, while modern approaches require adaptive, situation-specific management (“each situation has its own Management by…”). Methodologically, the study combines desk research with a case study, expanding the WEG cycle (Goals→Paths→Design→Results) into ten mechanisms. Results confirm strong performance (profit margin >20%, NPS >85, blockchain traceability for EU PDO certification). The main contribution is the formulation of Management by… (many) as a dynamic extension of the WEG model, relevant for SMEs. The hypothesis is confirmed: WEG evolves with context. Future research includes pilot validation (2027) and software development.

INTEGRATING GIS AND CYBERSECURITY: SPATIAL INTELLIGENCE FOR CRITICAL INFRASTRUCTURE RISK AND RESILIENCE

Gokhan Balik — 2026-05-23

As digital networks become increasingly intertwined with physical infrastructure, cybersecurity must account for location, interdependence, and operational context. Geographic Information Systems (GIS) provide a valuable framework by integrating spatial, attribute, and temporal data within a single analytical environment. This article presents a structured literature review of GIS-enabled cybersecurity research, focusing on critical infrastructure, smart cities, and resilience planning. The review synthesizes peer-reviewed studies, technical standards, and selected institutional materials to examine how GIS supports threat visualization, vulnerability assessment, dependency mapping, situational awareness, governance, and risk communication. The review suggests that GIS is most valuable where cyber risk has clear spatial, infrastructural, and operational dimensions, particularly in critical infrastructure protection and urban cyber-physical systems. However, the evidence base remains uneven. Visualization and situational awareness applications are relatively mature, while ontology-based modeling, blockchain-based trust, and some AI-driven functions remain emerging and require further operational validation. The article also argues that GIS platforms should be treated as strategic digital assets requiring dedicated cybersecurity protection and concludes with implications for secure GIS governance and future research on geospatially informed resilience frameworks.

GIS-BASED ASSESSMENT OF THE PHYSICAL EROSION SUSCEPTIBILITY USING RAINFALL EROSIVITY (R) AND TOPOGRAPHIC FACTOR (LS): CASE OF THE SILIANA RIVER WATERSHED, NORTH-WESTERN TUNISIA

Arbi Chafai — 2026-05-25

This study analyzes the spatial variability of rainfall erosivity (R factor), the topographic factor (LS), and their interaction in the Siliana river watershed, located in northwestern Tunisia, using a GIS-based integrated approach. The R factor was estimated from multi-year rainfall data covering the period 1990–2022, applying Arnoldus’ index and spatial interpolation via kriging. The LS factor was derived from a 30 m resolution SRTM digital elevation model, incorporating slope and slope length. The spatial combination of R and LS factors allowed for the assessment of water erosion potential at the watershed scale. Results show that annual precipitation ranges from 417 to 523 mm, with higher values in the mountainous areas of Bargou and Makthar. Rainfall erosivity exhibits moderate spatial variability, dominated by low to medium classes over most of the watershed. In contrast, the LS factor displays strong spatial heterogeneity, controlled by the relief morphology, with high values concentrated in the mountainous sectors of Kesra, Bargou and Makthar. The LS × R map reveals a highly contrasted erosion potential, with values ranging from 0 to 2069. High to very high risks dominate the upstream areas, whereas agricultural plains show generally low risk. These results highlight the amplifying role of topography and provide a valuable basis for soil conservation planning and sustainable watershed management.

ASSESSING THE EFFECTS OF TEMPERATURE CHANGE ON HUMAN THERMAL COMFORT UNDER INTENSIFYING LAND COVER CHANGE IN ETHIOPIA (1984–2023)

Ayehu Fekadu — 2026-05-26

Rising temperatures have aggravated heat stress in many tropical countries, driven by the combined effects of land cover dynamics and climate variability. However, the impacts of land use and land cover change (LULC) on thermal comfort have not been sufficiently evaluated, predominantly in many African countries. Thus, this study assesses heat stress in Ethiopia (1984–2023) using Adjusted Wet-Bulb Globe Temperature (WBGT) and Discomfort Index (DI) assessment indicators, via satellite-derived land cover datasets and Land Surface Temperature (LST). The results revealed that mean annual temperature and thermal stress in Ethiopia had steadily increased over the last four decades. The DI value (> 32) indicates very high heat stress areas across the rift valley, particularly along the Danakil Depression. The area extent raised in 2023 sevenfold compared to 1984, while the extent of comfortable zones decreased by 25%. DI and WBGT values are higher in urbanized and forest-depleted areas (Southeast) and lowland regions, this was driven by climate change and induced LULC. The warming trend is currently increasing in highland areas that were formerly cooler. The WBGT results support DI outcomes, showing an increase in heat stress, expansion of high-risk areas (≥27.7 °C), and a decrease in cold stress regions. The expansion of heat stress zones is due to the compound effects of climatic warming, natural vegetation reduction and urban expansion. The findings show that heat stress is spreading across Ethiopia, increasing health risks, limiting everyday activities, and affecting socioeconomic resilience. As a result, these findings emphasise the need for climate-sensitive design, heat adaptation measures, and public health involvement in decreasing rising heat dangers.

SPATIOTEMPORAL DYNAMICS OF URBAN BUILT-UP EXPANSION AND LAND SURFACE TEMPERATURE IN GUWAHATI MUNICIPAL CORPORATION, ASSAM

Nilakshi Mazumdar — 2026-05-26

In Guwahati, urban growth has increased over the past ten years, and the thermal imprint within the Guwahati Municipal Corporation (GMC) has evolved over the decade. The change in land surface temperature (LST) regime of GMC between 2013 and 2024 is reshaped by the growth of impervious surfaces. Late post-monsoon window Landsat 8 OLI/TIRS Image Collection 2 Level-2 imagery for the years 2013, 2018, and 2024 was processed in Google Earth Engine, with cloud-shadow masking, radiometric scaling, and emissivity-corrected LST retrieval using NDVI-derived vegetation fraction. The Built-up Index (NDBI) was used to map built-up intensity as both a continuous measure and a binary (classifier of urban extent) measure. LST was compared using zonal statistics and Pearson’s correlation of pixels at annual snapshots and inter-period change. The built-up area increased from 24.33 sq. km in 2013 to 40.12 sq. km in 2024, representing about a 65% expansion. The built-up zones were consistently warmer than the non-built-up areas, with the mean LST differences of approximately 1.5-1.98 °C. It shows that there is a consistent urban thermal penalty. The strong and significant within-year NDBI-LST correlations (r = 0.68-0.74) demonstrated that denser impervious cover is a reliable predictor of high surface temperatures. Conversely, relationships between the variations in NDBI and the variations in LST were weak to moderate (r = 0.26-0.31). It also points out the other contribution of interannual atmospheric variability, complicated topography, and mixed land cover at the urban fringe. The results indicate that a thermal difference exists between non-urban and urban surfaces in Guwahati. Although the areal coverage of the thermally stressed built-up land is still growing with the ongoing urbanisation.

CLOUD-FREE FLOOD INUNDATION FREQUENCY MAPPING OF MAJULI ISLAND, ASSAM USING GOOGLE EARTH ENGINE AND SENTINEL-1 SAR TIME-SERIES (2017-2025)

Manoj Sarmah — 2026-05-27

Majuli Island, located within the braided channel of the Brahmaputra in upper Assam, India. The island is widely documented as the world's largest inhabited river island and one of the most flood-prone landmasses in South Asia. Repeated monsoon flooding has stripped the island of more than half its mapped area since the late nineteenth century, yet no spatially explicit or decadal-scale record of where flooding actually recurs has been produced. Cloud-free flood inundation frequency mapping for Majuli Island exercise using dual Sentinel-1 SAR Ground Range Detected (GRD) workflows executed within Google Earth Engine. One pipeline, built on VH cross-polarisation descending-orbit imagery to extracted annual monsoon-season flood extents across 2017 to 2025 through bi-temporal backscatter change detection. A second pipeline, using VV co-polarisation from 2017 to 2025, stacked nine annual binary flood masks into a pixel-level Flood Frequency Index. The results show that roughly 43.51% of the island was flooded at least once during the observation period. Persistent inundation was recorded in almost every season, with concentrations along the southwestern channel margin and in low-lying wetland basins. The annual flooded area ranged from 70 sq. km to 294 sq. km, with 2019 recording the largest extent. Validation against Sentinel-2 MNDWI reference imagery yielded a mean overall accuracy of 63.44% and a mean Kappa coefficient of 0.27. The mean spatial overlap with JRC Global Surface Water seasonal zones reached 88.57%. Over 61% of the island's agricultural land fell within the occasional-to-persistent flood frequency categories. The flood frequency dataset produced here is intended as a direct operational input to embankment prioritisation, early warning planning, and agricultural risk assessment in Majuli.

BEYOND MANAGEMENT BY OBJECTIVES: HOW THE WEG CONTROLLING MODEL SECURED MARINA RESILIENCE DURING COVID-19

Ante Rončević — 2026-07-01

Strategic management faces challenges of digital transformation, algorithmic competition, unstable supply chains, and integration of artificial intelligence into decision-making, particularly in transitional economies such as Croatia. Classical frameworks, such as Management by Objectives (MBO), introduced by Peter Drucker in 1954, remain a foundation of participative goal-setting but exhibit limitations in real time control and monitoring as well as scalability under the dynamic conditions of modern business. This paper examines the evolution of MBO towards Albrecht Deyhle’s WEG model (Wege, Ergebnisse, Gestaltung – Paths, Results, Design), which integrates controlling as a predictive, cascading, and iterative methodology for goal attainment. The Croatian marina industry serves as a case study (Marina Frapa Rogoznica), demonstrating the application of the MBO-WEG framework in setting goals for capacity, supply, sustainability, and financial efficiency during the COVID-19 crisis. The findings indicate that this approach significantly improves decision-making in the marina business sector, where tourism, environmental, and economic imperatives intersect, rendering it scalable for coastal development of small municipalities and national macro-strategies.

PREDICTING FLOOD RISK WITH MACHINE LEARNING AND GIS: INSIGHTS FROM THE SAKTOLA RIVER, ASSAM, INDIA

Nilotpal Kalita — 2026-07-01

In Assam, India, flooding is among the most frequent and devastating natural disasters, bringing about significant social and economic disturbances in the Brahmaputra Valley every year. Though floods occur quite frequently, a systematic assessment of flood susceptibility has not been extensively carried out in many secondary river basins, including the Saktola River Basin. This paper discusses the basin's first spatially defined flood susceptibility assessment using the Maximum Entropy (MaxEnt) machine learning method to facilitate informed management of flood risks. Field validation were combined with seven environmental variables that condition floods in order to model patterns of flood susceptibility. The produced susceptibility map classifies the basin into zones with low, moderate, and high susceptibility to floods; high-risk areas mostly lie along river channels and near floodplains. Validation of models through Area Under the Receiver Operating Characteristic Curve (ROC) reflects high predictive accuracy, which will confirm the MaxEnt algorithm's efficiency in flood susceptibility modeling. The study revealed the applicability of machine learning-based flood susceptibility assessment for data-scarce river basins in Northeast India and provided valuable insights for management of hazard like flood and associated problems in the state of Assam.

THE ROLE OF NATURAL RESOURCE POTENTIAL OF TERETORRY IN SHAPING TAX REVENUES OF TERRITORIAL COMMUNITIES

Petro Pyvovar — 2026-07-01

This study assesses the natural resource potential (NRP) of 66 territorial communities in Zhytomyr Oblast (urban, settlement-type, and rural) and examines its association with local tax revenues. An integrated database of land cover, climate, relief, water resources, soils, vegetation, and ecological pressure was derived from open geospatial products (Sentinel-2, Sentinel-5P/TROPOMI, ERA5-Land, Dynamic World, OpenLandMap) and aggregated to community boundaries, then linked to key taxes (land fees, resource rents, single tax Group IV, environmental tax, PIT, and single tax Groups II–III) for 2021–2025 (deflated and averaged). Results reveal a near balance of forests (~47%) and agricultural land (~42%) in rural communities and a strong north–south natural gradient. Correlation patterns differ by community type, with built-up area dominating urban fiscal capacity, mixed resource channels in settlement-type communities, and stronger NRP–revenue links in rural areas.

COMPARISON OF COMMUNITY SCIENCE RAIN GAUGE MEASUREMENTS WITH SATELLITE PRECIPITATION DATA IN TUCSON, ARIZONA, USA

Theresa Foley — 2026-07-01

The Sonora Environmental Research Institute, Inc. (SERI) is a non-profit organization in Tucson, Arizona, USA. With the help of community scientists, SERI installed 168 rain gauges for a precipitation study in central Tucson. One hundred twelve or 67% of the participants met the U.S. Department of Housing and Urban Development (HUD) criteria for being low income. Forty-three percent of the participants spoke Spanish as their primary language, and 60% of participants identified as Hispanic. We compared the rain gauge data to NASA's Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for Global Precipitation (IMERG). The overall chi-square is 7.0 mm, indicating there is significant disagreement between the GPM and rain gauge measurements. We calculated an overall bias of 6 mm and an RMSE of 13 mm. We found that Tucson precipitation is variable in intensity and through space and time. A significant portion of this variability is below the resolution of the 9x11 kilometer GPM IMERG pixel.

MODELING THE SECURITY AND RESILIENCE OF GIS SYSTEMS USING ARTIFICIAL INTELLIGENCE

Maciej Kiedrowicz — 2026-07-01

Geographic information systems (GIS) play a critical role in the functioning of modern states and economies by supporting crisis management, critical infrastructure protection, and the delivery of public services. The increasing integration of GIS with cloud computing environments, Internet of Things (IoT) networks, and real time analytical mechanisms significantly enhances their operational capabilities, but at the same time increases system complexity and exposure to cyber threats, technical failures, and operational disturbances. This paper proposes an integrated approach to modeling the security and resilience of GIS systems using artificial intelligence techniques, including machine learning, neural networks, and probabilistic modeling. The proposed framework supports automated anomaly detection, vulnerability forecasting, and assessment of the impact of disruptions on the operational continuity of geoinformatics components across data, analytical, and service layers. The model explicitly accounts for data quality, AI model stability, and API based service behavior within a unified resilience assessment structure. Experimental results demonstrate that AI based solutions enable earlier identification of incidents and more effective analysis of GIS resilience compared to conventional methods. In particular, the proposed approach improves detection of subtle anomalies and supports systematic evaluation of cascading effects under compound disturbance scenarios. The findings confirm the potential of artificial intelligence to enhance the automation and adaptability of GIS security processes and provide a foundation for further research on intelligent, adaptive mechanisms for spatial data protection and resilient geoinformatics infrastructures.

JUST ENERGY TRANSITION IN COAL-DEPENDENT REGIONS – A CASE STUDY OF THE SILESIA REGION, POLAND

Dorota Benduch — 2026-07-01

The Upper Silesia region, historically shaped by hard coal mining and related heavy industry, is currently in a critical phase of just energy transition driven by the European Union’s goal of achieving climate neutrality by 2050. This article aims to assess the feasibility of this process in the coal-dependent subregions of the Silesian Voivodeship, with particular attention to institutional, legal, and spatial planning conditions. The study uses an interdisciplinary desk-based research approach based on the analysis of normative acts, strategic and operational documents, statistical data, official regional sources, and selected comparative literature on post-mining transformation and energy justice. The findings indicate that the principal challenge no longer lies in the absence of strategies or financing instruments but in a weak integration of sectoral restructuring, post-mining land governance, spatial planning, and labor market policy. The 2025 amendment to the Act on the Functioning of Hard Coal Mining improved the operational framework for mine closure and enabled asset transfers to local governments, yet it did not eliminate inequalities in access to social protection or remove the procedural bottlenecks affecting the redevelopment of post-mining land. Although the regional governance architecture has been strengthened by the Territorial Just Transition Plan (TJTP), the Regional Council for Just Transition (RCJT), and the Regional Observatory of the Transformation Process 2.0 (ROPT 2.0), the implementation gap remains substantial. The article concludes with legislative recommendations aimed at improving the implementation of a just transition in coal-dependent regions.

A METHODOLOGICAL FRAMEWORK FOR ASSESSING THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE CYBERSECURITY OF MODERN GIS SYSTEMS

Stefan Rozmus — 2026-07-01

This paper proposes an integrated methodological framework for assessing the role of artificial intelligence in the cybersecurity of contemporary geographic information systems (GIS). The framework is structured around three interrelated dimensions: spatial data quality, the resilience of anomaly detection models to geolocation manipulation, and the security architecture of geospatial service environments. The study aims to provide a coherent, simulation-based foundation for systematically examining the relationships among these dimensions in a controlled and reproducible research setting. The framework is demonstrated using synthetic yet operationally realistic data representing an urban public transport environment. The methodological design combines: (i) formal assessment of spatial data quality in accordance with ISO 19157-1:2023 and ISO 19115-1:2014; (ii) comparative evaluation of anomaly detection models implemented in single-channel and multimodal configurations; and (iii) assessment of OGC API–based geospatial services under conventional trust-based and Zero Trust security architectures. The simulation environment includes controlled data quality degradation, GNSS spoofing scenarios, and selected API abuse patterns. The results indicate that the proposed framework provides a coherent basis for analysing the effects of data quality on anomaly detection, the response of multimodal AI models to geolocation manipulation. Under simulated disruption conditions, multimodal analytical configurations and Zero Trust architectures showed greater robustness than conventional approaches. The framework offers a methodological foundation for future research on AI-supported GIS cybersecurity.

ARTIFICIAL INTELLIGENCE IN PHOTOGRAMMETRIC MEASURING AND MODELLING OF LANDSLIDE EVENTS

Dubravko Gajski — 2026-07-01

Landslides are among the most devastating natural hazards worldwide, causing significant loss of life, infrastructure damage, and economic disruption. Accurate detection, mapping, and modeling of landslides are essential components of disaster risk reduction and sustainable land management. In the past, finding and studying landslides depended on field mapping and subjective visual interpretation of aerial photography. While these methods established a theoretical foundation for understanding slope dynamics, they were often constrained by limited spatial resolution, high labor costs, and an inability to provide timely insights in rapidly changing landscapes. The contemporary transition toward automated, data-driven methods is characterized by the connecting of high-resolution remote sensing platforms—such as Unmanned Aerial Vehicles (UAVs) and sophisticated satellite constellations—with advanced machine learning (ML) and deep learning (DL) algorithms. This change has made it possible to measure movements of the ground and identify areas at risk for landslides more accurately than ever before. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, have revolutionized the processing and interpretation of photogrammetric data. This synergy facilitates improved landslide monitoring, early warning, and risk assessment, contributing to more effective emergency reaction. This paper aims to emphasize both the advantages and limitations of these technologies in advancing landslide science and disaster management.