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

2026-05-23T09:21:19+00:00

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

2026-05-23T09:29:10+00:00

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

2026-05-23T09:35:38+00:00

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

2026-04-08T19:05:59+00:00

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)

2026-06-17T19:58:30+00:00

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

2026-05-09T18:55:19+00:00

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)

2026-05-15T19:56:14+00:00

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.