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3 November 2025

Application of Remote Sensing and GIS for Promoting Sustainable Geoenvironment

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Department of Geology, University of Patras, 26504 Patras, Greece
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Sustainability2025, 17(21), 9789;https://doi.org/10.3390/su17219789
This article belongs to the Special Issue Application of Remote Sensing and GIS for Promoting Sustainable Geoenvironment
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The continuous growth of Earth’s population poses increasingly complex challenges to the physical environment. Over recent decades, urban areas have undergone accelerated growth, leading to significant landscape alterations, heightened demand for natural resources, and increased stress on ecological systems []. According to projections by the United Nations, the proportion of the world’s population residing in urban areas, which currently exceeds 50%, is expected to rise to nearly 70% by 2050, with the majority of this growth occurring in developing regions of Asia and Africa []. These demographic and spatial dynamics are expected to promote continued urban sprawl.
Urban Expansion and Geoenvironmental Consequences
Urban expansion has profoundly transformed the geoenvironment and has been consistently linked to a wide range of detrimental consequences. These include the intensification of energy demand, increased levels of environmental pollution, air, water, and soil degradation, traffic congestion, natural resource depletion, water and soil salinization, biodiversity loss, waste, desertification fires, deforestation, and climate change [,]. Over the past decades, the rapid expansion of urban areas has led to new environmental challenges including the development of urban heat islands, increased greenhouse gas emissions from transportation and buildings, and greater pressure on water supply, sewage, and waste management systems [].
The expansion growth of urban areas has triggered significant modifications in land use practices and coastal morphology []. Land use changes can exert significant influences on Earth. Urban sprawl not only has substantial impacts on the climate, but also contributes to ecosystem disruption and environmental degradation []. The conversion of naturally vegetated land, such as forests, grasslands, or shrublands, into impervious surfaces intensifies surface runoff and increases the likelihood of flooding []. Moreover, coastal regions are projected to experience a 160% increase in urban extent between 2000 and 2030 []. In Mediterranean coastal areas, such as Italy, each year, more than 10 km2 of natural and agricultural land types are converted into anthropogenic surfaces []. In Greece, the coastal areas adjacent to the Athens Metropolitan Area demonstrate that over the past 76 years, artificial land cover has increased by 40% []. The loss of natural land due to urbanization is a global phenomenon, potentially exposing many cities to various natural hazards.
Furthermore, it should be noted that urban sprawl has been associated with the amplification of natural hazard occurrences and the exacerbation of disaster impacts on a global scale []. The unregulated expansion of built-up areas into flood prone zones has heightened both human and infrastructural vulnerability [,]. A global assessment revealed that urban exposure to flooding increased more than fourfold between 1985 and 2018, with a significant proportion of new urban development taking place within floodplain areas []. Urban expansion also alters topography and increases susceptibility to landslides. In hilly or sloped areas, construction activities frequently destabilize slopes, thereby elevating the likelihood of landslide events [,,,].
Overall, while urbanization continues to serve as a catalyst for economic development and social progress, its uncontrolled expansion presents significant challenges to environmental sustainability and disaster resilience. Consequently, the integration of spatial planning, ecosystem-based management, and climate adaptation policies is imperative to mitigate the adverse geoenvironmental impacts associated with urban growth.
Environmental System Pressures and the Necessity of Continuous Monitoring
Pressures on environmental systems are continuously intensifying as population growth drives extensive land-use and land-cover transformations. These transformations frequently result in irreversible consequences. As human activities reshape the Earth’s surface at unprecedented rates, understanding these processes necessitates comprehensive baseline data, continuous environmental monitoring, and the systematic evaluation of spatial and temporal dynamics [].
Regional-scale assessments are particularly vital, as they enable the identification of localized environmental stresses that may remain undetected at the global scale. Through systematic observation and the use of long-term datasets, researchers can quantify the cumulative impacts of anthropogenic activities on geomorphological processes, hydrological dynamics, and climatic variables [].
Furthermore, the continuous monitoring of environmental parameters provides critical feedback to policymakers and stakeholders, facilitating the development of adaptive, evidence-based management strategies. Such approaches form the cornerstone of sustainable geoenvironmental management, ensuring that development policies respect ecological thresholds and promote long-term environmental resilience []. Ultimately, the systematic observation and analysis of environmental data are essential for maintaining a balance between human development and the sustainability of the geoenvironment.
Remote Sensing and GIS in Geoenvironmental Research
Over the past several decades, the proliferation of advanced remote sensing (RS) technologies has facilitated the acquisition of high-resolution, multi-sensor, and multi-temporal geospatial datasets. Simultaneously, geographical information systems (GIS) have evolved into sophisticated computational platforms for the integration, management, and quantitative analysis of heterogeneous geospatial data. The synergistic fusion of RS and GIS enables the generation of multi-dimensional, dynamic datasets that are critical for systematic environmental monitoring, change detection, and predictive modeling [,,].
The advancement of modern RS technologies has allowed researchers to obtain highly detailed data regarding land cover characteristics, vegetation dynamics, hydrological patterns, urban expansion, and environmental degradation at unprecedented spatial and temporal scales [,,,]. The use of high-resolution satellite imagery, synthetic aperture radar (SAR), and LiDAR technologies now permits the identification of subtle environmental changes that were previously undetectable through conventional field-based techniques.
Simultaneously, geographical information systems (GIS) have evolved into sophisticated computational platforms capable of integrating, managing, and quantitatively analyzing heterogeneous geospatial datasets. Modern GIS supports advanced modeling, statistical analysis, and scenario-based simulations of environmental processes, enhancing visualization and spatial querying capabilities [,].
The integration of remote sensing (RS) and geographic information systems (GIS) significantly improves the capacity to analyze spatial and temporal variations in environmental systems. This combined approach offers essential insights for policymakers, resource managers, and stakeholders, facilitating data-driven decision-making in sustainable land-use planning, environmental protection, and geoenvironmental management.
The application of RS and GIS spans a wide spectrum of geoenvironmental studies. These include but are not limited to:
  • Precise geological and geomorphological mapping for understanding terrain evolution [,];
  • Quantitative assessment of natural resources such as soil, water, vegetation, and mineral deposits [,,];
  • Evaluation of contaminant dispersal and environmental hazard assessment [,];
  • Ecosystem and ecological vulnerability assessment [,];
  • Modeling of natural hazard susceptibility and identification of vulnerable communities [,,,,,,];
  • Urbanization and climate changes [,];
  • Land-use planning and resource management for sustainable development [,,].
RS and GIS allow for the integration of remotely acquired data with in situ measurements, supporting predictive modeling and decision-making in both applied and theoretical geosciences. These technologies enhance the precision and efficiency of environmental assessments and provide the foundation for adaptive, data-driven strategies in sustainable geoenvironmental management. Consequently, such capabilities ensure that geoenvironmental management remains responsive and evidence-based, effectively addressing the complex challenges arising from rapid environmental change and urbanization.
Consequently, RS and GIS constitute indispensable methodological tools in contemporary geoenvironmental research. They underpin the quantitative analysis of human–environment interactions and support the sustainable management of geological, ecological, and socio-environmental systems [,,,,,,]. The continuous advancement of sensor technologies, data analytics, and spatial modeling algorithms further underscores their critical role in addressing complex environmental challenges in an era of rapid global change [,,,,]. By integrating high-resolution geospatial data, real-time monitoring, and predictive modeling, RS and GIS provide essential tools for mitigating environmental degradation, reducing disaster risk, and promoting resilient and sustainable geoenvironmental management.
The main aim of this Special Issue is to emphasize the wide-ranging applications of RS and GIS in advancing sustainable geoenvironmental research. It encompasses systematic monitoring, evaluation, and the spatiotemporal analysis of environmental conditions. The Issue also addresses strategies for enhancing the long-term sustainability of geoenvironmental systems, reducing anthropogenic impacts, and supporting the preservation of high-quality human life. Additionally, natural hazard assessment and prevention are presented.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science 2008, 319, 756–760. [Google Scholar] [CrossRef] [PubMed]
  2. DESA, U. World Urbanization Prospects: The 2018 Revision; United Nations: New York, NY, USA, 2020; Available online: https://population.un.org/wup/ (accessed on 22 September 2025).
  3. Bathrellos, G.D.; Skilodimou, H.D. Estimation of sand and gravel extraction sites. Z. Geomorphol. 2022, 63, 313–328. [Google Scholar] [CrossRef]
  4. Semenov, S.M. Intergovernmental Panel on Climate Change: Results, Problems, and Prospects. Izv. Atmos. Ocean. Phys. 2024, 60 (Suppl. 3), S323–S330. [Google Scholar] [CrossRef]
  5. Li, X.; Zhou, Y.; Ouyang, Z. Ecological and environmental impacts of urban expansion: A review. Land Use Policy 2020, 91, 104380. [Google Scholar] [CrossRef]
  6. Bathrellos, G.D.; Skilodimou, H.D. Land use planning for natural hazards. Land 2019, 8, 128. [Google Scholar] [CrossRef]
  7. Hong, C.; Burney, J.A.; Pongratz, J.; Nabel, J.E.; Mueller, N.D.; Jackson, R.B.; Davis, S.J. Global and regional drivers of land-use emissions in 1961–2017. Nature 2021, 589, 554–561. [Google Scholar] [CrossRef]
  8. Skilodimou, H.D.; Bathrellos, G.D.; Alexakis, D.E. Flood Hazard Assessment Mapping in Burned and Urban Areas. Sustainability 2021, 13, 4455. [Google Scholar] [CrossRef]
  9. Seto, K.; Fragkias, M.; Güneralp, B.; Reilly, M. A Meta-Analysis of Global Urban Land Expansion. PLoS ONE 2011, 6, e23777. [Google Scholar] [CrossRef]
  10. Smiraglia, D.; Cavalli, A.; Giuliani, C.; Assennato, F. The increasing coastal urbanization in the Mediterranean environment: The state of the art in Italy. Land 2023, 12, 1017. [Google Scholar] [CrossRef]
  11. Skilodimou, H.D.; Antoniou, V.; Bathrellos, G.D.; Tsami, E. Mapping of coastline changes in Athens Riviera over the past 76 year’s measurements. Water 2021, 13, 2135. [Google Scholar] [CrossRef]
  12. Huppert, H.E.; Sparks, R.S.J. Extreme natural hazards: Population growth, globalization and environmental change. Phil. Trans. R. Soc. A 2006, 364, 1875–1888. [Google Scholar] [CrossRef]
  13. Hemmati, M.; Ellingwood, B.R.; Mahmoud, H.N. The role of urban growth in resilience of communities under flood risk. Earth’s Future 2020, 8, e2019EF001382. [Google Scholar] [CrossRef]
  14. Maranzoni, A.; D’Oria, M.; Rizzo, C. Quantitative flood hazard assessment methods: A review. J. Flood Risk Manag. 2023, 16, e12855. [Google Scholar] [CrossRef]
  15. Tellman, B.; Sullivan, J.A.; Kuhn, C.; Kettner, A.J.; Doyle, C.S.; Brakenridge, G.R.; Erickson, T.A.; Slayback, D.A. Satellite imaging reveals increased proportion of population exposed to floods. Nature 2021, 596, 80–86. [Google Scholar] [CrossRef] [PubMed]
  16. Alcántara-Ayala, I. Landslides in a changing world. Landslides 2025, 22, 2851–2865. [Google Scholar] [CrossRef]
  17. Bathrellos, G.D.; Koukouvelas, I.K.; Skilodimou, H.D.; Nikolakopoulos, K.G.; Vgenopoulos, A.-L. Landslide causative factors evaluation using GIS in the tectonically active Glafkos River area, northwestern Peloponnese, Greece. Geomorphology 2024, 461, 109285. [Google Scholar] [CrossRef]
  18. Bathrellos, G.D.; Kalivas, D.P.; Skilodimou, H.D. Landslide Susceptibility Assessment Mapping: A Case Study in Central Greece. In Remote Sensing of Hydrometeorological Hazards; Petropoulos, G.P., Islam, T., Eds.; CRC Press, Taylor & Francis Group: London, UK, 2017; pp. 493–512. ISBN -13: 978-1498777582. [Google Scholar] [CrossRef]
  19. Kechebour, B.E. Relation between stability of slope and the urban density: Case study. Procedia Eng. 2015, 114, 824–831. [Google Scholar] [CrossRef]
  20. Turner, B.L.; Lambin, E.F.; Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. USA 2007, 104, 20666–20671. [Google Scholar] [CrossRef]
  21. Pettorelli, N.; Laurance, W.F.; O’Brien, T.G.; Wegmann, M.; Nagendra, H.; Turner, W. Satellite remote sensing for applied ecologists: Opportunities and challenges. J. Appl. Ecol. 2014, 51, 839–848. [Google Scholar] [CrossRef]
  22. Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F.S., III; Lambin, E.F.; Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J.; et al. A safe operating space for humanity. Nature 2009, 461, 472–475. [Google Scholar] [CrossRef]
  23. Manapragad, N.V.S.K.; Mandelmilch, M.; Roitberg, E.; Kizel, F.; Natanian, J. Remote Sensing for Environmentally Responsive Urban Built Environment: A Review of Tools, Methods and Gaps. Remote Sens. Appl. 2025, 38, 101529. [Google Scholar] [CrossRef]
  24. Ahmad, M.N.; Almutlaq, F.; Huq, M.E.; Islam, F.; Javed, A.; Skilodimou, H.D.; Bathrellos, G.D. Enhanced urban impervious surface land use mapping using a novel multi-sensor feature fusion method and remote sensing data. Environ. Earth Sci. 2025, 84, 228. [Google Scholar] [CrossRef]
  25. Foody, G.M. Remote sensing in landscape ecology. Landsc. Ecol. 2023, 38, 2711–2716. [Google Scholar] [CrossRef]
  26. Ahmad, M.N.; Shao, Z.; Javed, A.; Israr, A.; Islam, F.; Skilodimou, H.D.; Bathrellos, G.D. Optical-SAR data fusion based on Simple Layer Stacking and XGBoost Algorithm to extract urban impervious surface in diverse Urban Environments. Remote Sens. 2024, 16, 873. [Google Scholar] [CrossRef]
  27. Goodchild, M.F. Scale in GIS: An overview. Geomorphology 2011, 130, 5–9. [Google Scholar] [CrossRef]
  28. Nezhad, M.M.; Moradian, S.; Guezgouz, M.; Shi, X.; Avelin, A.; Wallin, F. A GIS-portal platform from the data perspective to energy hub digitalization solutions-A review and a case study. Renew. Sustain. Energ. Rev. 2025, 223, 116019. [Google Scholar] [CrossRef]
  29. Skilodimou, H.D.; Bathrellos, G.D.; Maroukian, H.; Gaki-Papanastassiou, K. Late Quaternary evolution of the lower reaches of Ziliana stream in south Mt. Olympus, Greece. Geogr. Fis. Din. Quat. 2014, 37, 43–50. [Google Scholar] [CrossRef]
  30. Kokinou, E.; Skilodimou, H.D.; Bathrellos, G.D.; Antonarakou, A.; Kamberis, E. Morphotectonic analysis, structural evolution/pattern of a contractional ridge: Giouchtas Mt., Central Crete, Greece. J. Earth Syst. Sci. 2015, 124, 587–602. [Google Scholar] [CrossRef]
  31. Huang, X.; Li, Z.; Liu, X. Urban expansion and its impacts on surface temperature in metropolitan regions: A multi-sensor analysis. Remote Sens. Environ. 2019, 233, 111374. [Google Scholar] [CrossRef]
  32. Islam, F.; Ahmad, M.N.; Janjuhah, H.T.; Ullah, M.; Islam, I.U.; Kontakiotis, G.; Skilodimou, H.D.; Bathrellos, G.D. Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS Based Model. Appl. Sci. 2022, 12, 12211. [Google Scholar] [CrossRef]
  33. Gandhi, G.M.; Parthiban, S.; Thummalu, N.; Christy, A. Ndvi: Vegetation change detection using remote sensing and gis—A case study of Vellore District. Procedia Comput. Sci. 2015, 57, 1199–1210. [Google Scholar] [CrossRef]
  34. Makri, P.; Stathopoulou, E.; Hermides, D.; Kontakiotis, G.; Zarkogiannis, S.D.; Skilodimou, H.D.; Bathrellos, G.D.; Antonarakou, A.; Scoullos, M. The Environmental Impact of a Complex Hydrogeological System on Hydrocarbon-Pollutants’ Natural Attenuation: The Case of the Coastal Aquifers in Eleusis, West Attica, Greece. J. Mar. Sci. Eng. 2020, 8, 1018. [Google Scholar] [CrossRef]
  35. Bathrellos, G.D.; Skilodimou, H.D.; Gamvroula, D.E.; Alexakis, D.E. Evaluate the spatial distribution of trace elements in soil of a karst terrain. Carbonate Evaporite 2024, 39, 41. [Google Scholar] [CrossRef]
  36. Ding, Q.; Wang, L.; Fu, M.; Huang, N. An integrated system for rapid assessment of ecological quality based on remote sensing data. Environ. Sci. Pollut. Res. 2020, 27, 32779–32795. [Google Scholar] [CrossRef]
  37. Kamran, M.; Yamamoto, K. Evolution and use of remote sensing in ecological vulnerability assessment: A review. Ecol. Indic. 2023, 148, 110099. [Google Scholar] [CrossRef]
  38. Lu, Y.; Wang, J.; Xie, Y. Urbanization impacts on coastal morphology and sustainability: A global perspective. Sustainability 2021, 13, 6071. [Google Scholar] [CrossRef]
  39. Humbal, A.; Chaudhary, N.; Pathak, B. Urbanization Trends, Climate Change, and Environmental Sustainability. In Climate Change and Urban Environment Sustainability; Pathak, B., Dubey, R.S., Eds.; Disaster Resilience and Green Growth; Springer: Singapore, 2017; pp. 151–166. [Google Scholar] [CrossRef]
  40. Karpouza, M.; Skilodimou, H.D.; Kaviris, G.; Zymvragakis, A.; Antonarakou, A.; Bathrellos, G.D. Escape routes and safe points in natural hazards. A case study for soil. Eng. Geol. 2024, 340, 107683. [Google Scholar] [CrossRef]
  41. Chen, S.; Guo, Q.; Li, L. Sustainable land use dynamic planning based on GIS and symmetric algorithm. Adv. Civ. Eng. 2022, 2022, 4087230. [Google Scholar] [CrossRef]
  42. Kingra, P.K.; Majumder, D.; Singh, S.P. Application of Remote Sensing and Gis in Agriculture and Natural Resource Management Under Changing Climatic Conditions. Agric. Res. J. 2016, 53, 295–302. [Google Scholar] [CrossRef]
  43. Bareth, G. GIS-and RS-based spatial decision support: Structure of a spatial environmental information system (SEIS). Int. J. Digit. Earth 2009, 2, 134–154. [Google Scholar] [CrossRef]
  44. Paul, P.; Aithal, P.S.; Bhimali, A.; Kalishankar, T.; Saavedra, R.; Aremu, P.S.B. Geo information systems and remote sensing: Applications in environmental systems and management. IJMTS 2020, 5, 11–18. [Google Scholar] [CrossRef]
  45. Ahmadi, H.; Pekkan, E. Fault-based geological lineaments extraction using remote sensing and GIS—A review. Geosciences 2021, 11, 183. [Google Scholar] [CrossRef]
  46. Chaminé, H.I.; Pereira, A.J.; Teodoro, A.C.; Teixeira, J. Remote sensing and GIS applications in earth and environmental systems sciences. SN Appl. Sci. 2021, 3, 870. [Google Scholar] [CrossRef]
  47. Rosa, A.G.F.; Silva, W.D.O.; Fontana, M.E.; Levino, N.; Guarnieri, P. A GIS-based multi-criteria approach for identifying areas vulnerable to subsidence in the world’s largest ongoing urban socio-environmental mining disaster. Extr. Ind. Soc. 2024, 19, 101500. [Google Scholar] [CrossRef]
  48. Goodchild, M.F.; Haining, R.P. GIS and spatial data analysis: Converging perspectives. Pap. Reg. Sci. 2004, 83, 363–385. [Google Scholar] [CrossRef]
  49. Kumar, S.S.; Arivazhagan, S.; Rangarajan, N. Remote sensing and GIS applications in Environmental Sciences—A review. J. Environ. Nanotechnol. 2013, 2, 92–101. [Google Scholar] [CrossRef]
  50. Hogland, J.; Anderson, N. Function modeling improves the efficiency of spatial modeling using big data from remote sensing. Big Data Cogn. Comput. 2017, 1, 3. [Google Scholar] [CrossRef]
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