FAQ
T_LOGIN Log in

Don't have an account on our website?

T_REGISTER Register
Polish Academy of Arts and Sciences logo

Emerging trends in GIS and remote sensing technologies for environmental monitoring: innovations, applications, and future directions

Publication date: 05.12.2025

Geoinformatica Polonica, 2025, Vol. 24 (2025), pp. 25-41

https://doi.org/10.4467/21995923GP.25.002.22857

Authors

,
Anna Szafarczyk
AGH University of Science and Technology in Kraków
, Poland
https://orcid.org/0000-0001-6130-2529 Orcid
Contact with author
All publications →
Okechukwu Ebuka Agbasi
Department of Physics, College of Physical Science, Michael Okpara University of Agriculture
, Nigeria
https://orcid.org/0000-0001-5649-0107 Orcid
Contact with author
All publications →

Titles

Emerging trends in GIS and remote sensing technologies for environmental monitoring: innovations, applications, and future directions

Abstract

The escalating challenges of climate change, biodiversity loss, land degradation, and urban expansion have amplified the need for reliable, high-resolution, and timely environmental data. Geographic Information Systems (GIS) and Remote Sensing (RS) technologies have become indispensable tools for environmental monitoring, enabling the systematic collection, analysis, and visualization of spatial data across diverse ecosystems. This review synthesizes recent innovations in GIS and RS that are transforming environmental surveillance and decision-making. Key developments include the integration of artificial intelligence (AI) and machine learning (ML) for enhanced image classification, cloud-based platforms like Google Earth Engine (GEE) for scalable analysis, and the increasing use of Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors for high-resolution monitoring. Furthermore, the convergence of geospatial analytics with big data, the Internet of Things (IoT), and participatory approaches such as citizen science is expanding the accessibility and impact of environmental data. Case studies from Africa, Asia, and global initiatives highlight practical applications in land use change detection, water resource assessment, hazard risk mapping, urban heat island analysis, and biodiversity conservation. While the potential of these tools is vast, persistent challenges include data interoperability, technical capacity gaps, policy integration barriers, and ethical concerns related to surveillance and data equity. This review calls for greater investment in open-source tools, interdisciplinary collaboration, and inclusive data governance to realize the full potential of GIS and RS in achieving environmental resilience and sustainability. Future directions emphasise real-time monitoring, ethical frameworks, and the democratisation of spatial intelligence.

References

Download references

1. Ibebuchi, C.C., Abu, I.O. Interpolation of environmental data using deep learning and model inference. Machine Learning Science and Technology, 2024; volume 5, no. 2:025046 (https://doi.org/10.1088/2632-2153/ad4b94).

CrossRef Next

2. Mitchell, L.J., Williamson, B.J., Masden, E.A. Methods for highlighting ecological monitoring needs in data-sparse regions: a case study of impact assessment for multi-component infrastructure installations. Environmental Impact Assessment Review, 2024; volume 30, no. 105:107433 (https://doi.org/10.1016/j.eiar.2024.107433).

CrossRef Next

3. Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., Zhang, L. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 2020; volume 27, no. 241:111716 (https://doi.org/10.1016/j.rse.2020.111716).

CrossRef Next

4. Sani, S.A., Ibrahim, A., Musa, A.A., Dahiru, M., Baballe, M.A. Drawbacks of traditional environmental monitoring systems. Computer and Information Science, 2023; volume 30, no. 16(3):30 (https://doi.org/10.5539/cis.v16n3p30).

CrossRef Next

5. Zhu, Y., Geiß, C., So, E. Image super-resolution with dense-sampling residual channel-spatial attention networks formulti-temporal remote sensing image classification. International Journal of Applied Earth Observation and Geoinformation, 2021; volume 20, no. 104:102543 (https://doi.org/10.1016/j.jag.2021.102543).

CrossRef Next

6. Liu, D., Zheng, X., Wang, H. Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 2020; volume 7, no. 417:108924 (https://doi.org/10.1016/j.ecolmodel.2019.108924).

CrossRef Next

7. Zhang, P., Wu, W., Xue, C., Shi, S., Su, F. A New Framework for Integrating DNN-Based Geographic Simulation Models within GISystems. ISPRS International Journal of Geo-Information, 2024; volume 14, no. 13(10):361 (https://doi.org/10.3390/ijgi13100361).

CrossRef Next

8. Akiang, F.B., Nnaji, V.N., Opara, A.I., Agoha, C.C., Agbasi, O.E., Ulem, E.B., Njoku, J.O.. Geospatial and geo-electrical assessment of groundwater vulnerability and potential in parts of Cross River, Southern Nigeria. HydroResearch, 2024; volume 1 (https://doi.org/10.1016/j.hydres.2024.09.007).

CrossRef Next

9. Okoli, E., Akaolisa, C.C.Z., Ubechu, B.O., Agbasi, O.E., Szafarczyk, A. Using VES and GIS-Based DRASTIC analysis to evaluate groundwater aquifer contamination vulnerability in Owerri, southeastern Nigeria. Ecological Questions, 2024; volume 18, no. 35(3), pp. 1–27 (https://doi.org/10.12775/eq.2024.031).

CrossRef Next

10. Rowland, A., Ebuka, A.O. Assessing the Impact of Land Cover and Land Use Change on Urban Infrastructure Resilience in Abuja, Nigeria: A Case Study From 2017 To 2022. Structure and Environment, 2024; volume 29, no. 16(1), pp. 6–17 (https://doi.org/10.30540/sae-2024-002).

CrossRef Next

11. Aziz, N.A., Alwan, I.A., Agbasi, O.E. Integrating remote sensing and GIS techniques for effective watershed management: a case study of Wadi Al-Naft Basins in Diyala Governorate, Iraq, using ALOS PALSAR digital elevation model. Applied Geomatics, 2023; volume 2, no. 16(1), pp. 67–76 (https://doi.org/10.1007/s12518-023-00540-9).

CrossRef Next

12. Akaolisa, C.C., Agbasi, O.E., Etuk, S.E., Adewumi, R., Okoli, E.A. Evaluating the effects of real estate development in Owerri, Imo State, Nigeria: Emphasizing changes in Land Use/Land Cover (LULC). Journal of Landscape Ecology, 2023; volume 1, no. 16(2), pp. 98–113 (https://doi.org/10.2478/jlecol-2023-0012).

CrossRef Next

13. Himeur, Y., Rimal, B., Tiwary, A., Amira, A. Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Information Fusion, 2022; volume 25, no. 86–87, pp. 44–75 (https://doi.org/10.1016/j.inffus.2022.06.003)

CrossRef Next

14. Alotaibi, E., Nassif, N. Artificial intelligence in environmental monitoring: in-depth analysis. Discover Artificial Intelligence, 2024; volume 18, no. 4(1) (https://doi.org/10.1007/s44163-024-00198-1).

CrossRef Next

15. Gessa, A., Sancha, P. Environmental open data in Urban Platforms: An approach to the big data Life cycle. Journal of Urban Technology, 2019; volume 26, no. 27(1), pp. 27–45 (https://doi.org/10.1080/10630732.2019.1656934).

CrossRef Next

16. Quamar, M.M., Al-Ramadan, B., Khan, K., Shafiullah, M., Ferik, S.E. Advancements and Applications of Drone-Integrated Geographic Information System Technology —A Review. Remote Sensing, 2023; volume 20, no. 15(20):5039 (https://doi.org/10.3390/rs15205039).

CrossRef Next

17. Anderson, K., Griffiths, D., DeBell, L., Hancock, S., Duffy, J.P., Shutler, J.D., et al. A grassroots remote sensing toolkit using live coding, smartphones, kites and lightweight drones. PLoS ONE, 2016; volume 4, no. 11(5):e0151564 (https://doi.org/10.1371/journal.pone.0151564).

CrossRef Next

18. Vasiliades, M.A., Hadjichambis, A.Ch., Paraskeva-Hadjichambi, D., Adamou, A., Georgiou, Y. A systematic literaturę review on the participation aspects of Environmental and Nature-Based Citizen Science initiatives. Sustainability, 2021; volume 3, no. 13(13):7457 (https://doi.org/10.3390/su13137457).

CrossRef Next

19. Lary, D.J., Alavi, A.H., Gandomi, A.H., Walker, A.L. Machine learning in geosciences and remote sensing. Geoscience Frontiers, 2015; volume 12, no. 7(1), pp. 3–10. (https://doi.org/10.1016/j.gsf.2015.07.003).

CrossRef Next

20. Chen, H., Yang, L., Wu, Q. Enhancing land cover Mapping and Monitoring: an interactive and explainable machine learning approach using Google Earth engine. Remote Sensing, 2023; volume 18, no. 15(18):4585 (https://doi.org/10.3390/rs15184585).

CrossRef Next

21. Janga, B., Asamani, G., Sun, Z., Cristea, N. A review of practical AI for remote sensing in Earth Sciences. Remote Sensing, 2023; volume 21, no. 15(16):4112 (https://doi.org/10.3390/rs15164112).

CrossRef Next

22. Varma, B., Naik, N., Chandrasekaran, K., Venkatesan, M., Rajan, J. Forecasting Land-Use and Land-Cover change using hybrid CNN–LSTM model. IEEE Geoscience and Remote Sensing Letters, 2024; volume 1, no. 21, pp. 1–5 (https://doi.org/10.1109/lgrs.2024.3389671).

CrossRef Next

23. Prokop, K., Połap, D., Włodarczyk-Sielicka, M., Połap, K., Jaszcz, A., Stateczny, A. Automated shoreline extraction process for unmanned vehicles via U-net with heuristic algorithm. Alexandria Engineering Journal, 2024; volume 5, no. 102, pp. 108–18 (https://doi.org/10.1016/j.aej.2024.05.104).

CrossRef Next

24. Takhtkeshha, N., Mohammadzadeh, A., Salehi, B. A rapid Self-Supervised Deep-Learning-Based method for Post-Earthquake damage detection using UAV data (Case study: Sarpol-e Zahab, Iran). Remote Sensing, 2022; volume 26, no. 15(1), pp. 123 (https://doi.org/10.3390/rs15010123).

CrossRef Next

25. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 2020; volume 7, no. 164, pp. 152–70 (https://doi.org/10.1016/j.isprsjprs.2020.04.001).

CrossRef Next

26. Amani, M., Ghorbanian, A., Ahmadi, S.A., Kakooei, M., Moghimi, A., Mirmazloumi, S.M. Google Earth Engine Cloud computing platform for remote sensing big data Applications: A Comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020; volume 1, no. 13, pp. 5326–5350 (https://doi.org/10.1109/jstars.2020.3021052).

CrossRef Next

27. Gu, Q., Michanowicz, D.R., Jia, C. Developing a modular Unmanned aerial Vehicle (UAV) platform for air pollution profiling. Sensors, 2018; volume 10, no. 18(12):4363 (https://doi.org/10.3390/s18124363).

CrossRef Next

28. Rahman, M.M., Siddique, S., Kamal, M., Rifat, R.H., Gupta, K.D. UAV (Unmanned Aerial Vehicle): diverse applications of UAV datasets in segmentation, classification, detection, and tracking. Algorithms, 2024; volume 23, no. 7(12), pp. 594 (https://doi.org/10.3390/a17120594).

CrossRef Next

29. Han, S., Han, D. Enhancing Direct Georeferencing Using Real-Time Kinematic UAVs and Structure from Motion-Based Photogrammetry for Large-Scale Infrastructure. Drones, 2024; volume 5, no. 8(12) (https://doi.org/10.3390/drones8120736).

CrossRef Next

30. Feng, X., He, L., Cheng, Q., Long, X., Yuan, Y. Hyperspectral and multispectral remote sensing image fusion based on endmember spatial information. Remote Sensing, 2020; volume 21, no. 12(6):1009 (https://doi.org/10.3390/rs12061009).

CrossRef Next

31. Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R. Sousa, J.J. Hyperspectral Imaging: A review on UAVBased sensors, data processing and applications for agriculture and forestry. Remote Sensing, 2017, volume 30, no. 9(11):1110 (https://doi.org/10.3390/rs9111110).

CrossRef Next

32. Sekertekin, A., Arslan, N. Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery – A case study at Tuzla geothermal region. Geothermics, 2018; volume 29, no. 78, pp. 243–254 (https://doi.org/10.1016/j.geothermics.2018.12.014).

CrossRef Next

33. Chen, L., Mao, Y., Zhao, R. GIS application in environmental monitoring and risk assessment. 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS), 2022; volume 22, pp. 908–917 (https://doi.org/10.1109/icgmrs55602.2022.9849269).

CrossRef Next

34. Paul, K., Chatterjee, S.S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., Dasgupta, S. Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 2022; volume 1, no. 198:107096 (https://doi.org/10.1016/j.compag.2022.107096).

CrossRef Next

35. Govea; J., Gaibor-Naranjo, W., Sanchez-Viteri, S., Villegas-Ch, W. Integration of data and predictive models for the evaluation of air quality and noise in urban environments. Sensors, 2024; volume 5, no. 24(2):311 (https://doi.org/10.3390/s24020311).

CrossRef Next

36. Rossetto, R., De Filippis, G., Borsi, I., Foglia, L., Cannata, M., Criollo, R., Vázquez-Suñé, E. Integrating free and open source tools and distributed modelling codes in GIS environment for data-based groundwater management. Environmental Modelling & Software, 2018; volume 22, no. 107, pp. 210–230 (https://doi.org/10.1016/j.envsoft.2018.06.007).

CrossRef Next

37. Castell, N., Kobernus, M., Liu, H.Y., Schneider, P., Lahoz, W., Berre, A.J., Noll, J. Mobile technologies and services for environmental monitoring: The Citi-Sense-MOB approach. Urban Climate, 2014; volume 12, no. 14, pp. 70–82 (https://doi.org/10.1016/j.uclim.2014.08.002).

CrossRef Next

38. Amitrano, D., Di Martino, G., Guida, R., Iervolino, P., Iodice, A., Papa, M.N., Riccio, D., Ruelo, G. Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of selected applications. Remote Sensing, 2021; volume 8, no. 13(4):604 (https://doi.org/10.3390/rs13040604).

CrossRef Next

39. Santoro, M., Cartus, O. Research Pathways of Forest Above-Ground Biomass estimation based on SAR backscatter and interferometric SAR observations. Remote Sensing, 2018; volume 14, no. 10(4):608 (https://doi.org/10.3390/rs10040608).

CrossRef Next

40. Park, S.E., Jung, Y.T., Kim, H.C. Monitoring permafrost changes in central Yakutia using optical and polarimetric SAR data. Remote Sensing of Environment, 2022; volume 28, no. 74:112989 (https://doi.org/10.1016/j.rse.2022.112989).

CrossRef Next

41. Asori, M., Dogbey, E., Morgan, A.K., Ampofo, S.T., Mpobi, R.K.J., Katey, D. Application of GIS-based multi-criteria decision making analysis (GIS-MCDA) in selecting locations most suitable for siting engineered landfills – the case of Ashanti Region, Ghana. Management of Environmental Quality an International, 2022; volume 31, no. 33(3), pp. 800–826 (https://doi.org/10.1108/meq-07-2021-0159).

CrossRef Next

42. Sánchez-Lozano, J.M., Bernal-Conesa, J.A. Environmental management of Natura 2000 network areas through the combination of Geographic Information Systems (GIS) with Multi-Criteria Decision Making (MCDM) methods. Case study in south-eastern Spain. Land Use Policy, 2017; volume 30, no. 63, pp. 86–97 (https://doi.org/10.1016/j.landusepol.2017.01.021).

CrossRef Next

43. De Miguel González, R., Mar-Beguería, J., López, M.S., Kratochvíl, O. GIS-Based Dashboards as advanced geospatial applications for climate change education and teaching the future. ISPRS International Journal of Geo-Information, 2025; volume 18, no. 14(2):89 (https://doi.org/10.3390/ijgi14020089).

CrossRef Next

44. Yan, J., Wang, L., Song, W., Chen, Y., Chen, X., Deng, Z. A time-series classification approach based on change detection for rapid land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 2019; volume 7, no. 158, pp. 249–262 (https://doi.org/10.1016/j.isprsjprs.2019.10.003).

CrossRef Next

45. Li, Z., Weng, Q., Zhou, Y., Dou, P., Ding, X. Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta. Remote Sensing of Environment, 2024; volume 4, no. 308:114190 (https://doi.org/10.1016/j.rse.2024.114190).

CrossRef Next

46. Pavelka, K., Landa, M. Using Virtual and Augmented Reality with GIS Data. ISPRS International Journal of Geo-Information, 2024; volume 5, no. 13(7):241 (https://doi.org/10.3390/ijgi13070241).

CrossRef Next

47. Fridhi, A., Frihida, A. GIS 3D and Science of Augmented Reality: Modeling a 3D geospatial environment. DOAJ (DOAJ: Directory of Open Access Journals), 2019; volume 1, (https://doaj.org/article/f677385247c74868b739ef341f67196a).

DOAJ Next

48. Lin, M.H., Lin, Y.T., Tsai, M.L., Chen, Y.Y., Chen, Y.C., Wang, H.C., Wang, C.K. Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data. Environmental Monitoring and Assessment, 2024; volume 8, no. 96(3) (https://doi.org/10.1007/s10661-024-12424-5).

CrossRef Next

49. Harish, B., Manjulavani, K., Shantosh, M., Supriya, V.M. Change detection of land use and land cover using remote sensing techniques. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017; volume 1, no. 64, pp. 2806–2810 (https://doi.org/10.1109/icpcsi.2017.8392231).

CrossRef Next

50. Su, T., Zhang, S., Liu, T. Multi-Spectral image classification based on an Object-Based Active Learning approach. Remote Sensing, 2020; volume 4, no. 12(3), pp. 504 (https://doi.org/10.3390/rs12030504).

CrossRef Next

51. Abera, T., Pellikka, P., Johansson, T., Mwamodenyi, J., Heiskanen, J. Towards tree-based systems disturbance monitoring of tropical mosaic landscape using a time series ensemble learning approach. Remote Sensing of Environment, 2023; volume 27, no. 299:113876 (https://doi.org/10.1016/j.rse.2023.113876).

CrossRef Next

52. Mohamed, A., Worku, H. Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 2019; volume 3, no. 31:100545 (https://doi.org/10.1016/j.uclim.2019.100545).

CrossRef Next

53. Rugel, G.M.V., Ochoa, D., Menendez, J.M., Van Coillie, F. Evaluating the applicability of global LULC products and an Author-Generated Phenology-Based Map for regional analysis: a case study in Ecuador’s ecoregions. Land, 2023: volume, 22, no. 12(5), pp. 1112 (https://doi.org/10.3390/land12051112).

CrossRef Next

54. Vallet, A., Dupuy, S. Verlynde, M., Gaetano, R. Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning. Scientific Data, 2024; volume 23, no. 11(1) (https://doi.org/10.1038/s41597-024-03750-x).

CrossRef Next

55. Lippe, M., Hilger, T., Sudchalee, S., Wechpibal, N., Jintrawet, A., Cadisch, G. Simulating Stakeholder-Based Land-Use change scenarios and their implication on Above-Ground carbon and Environmental Management in Northern Thailand. Land, 2017; volume 3, no. 6(4), pp. 85 (https://doi.org/10.3390/land6040085).

CrossRef Next

56. Ross, M.R.V., Topp, S.N., Appling, A.P., Yang, X., Kuhn, C., Butman, D., Simard, M., Pavelsky, T.M. AquASAT: a data set to enable remote sensing of water quality for inland waters. Water Resources Research, 2019; volume 11, no. 55(11), pp. 10012–10025 (https://doi.org/10.1029/2019wr024883).

CrossRef Next

57. Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., Chen, F. A review of Remote sensing for water quality Retrieval: Progress and challenges. Remote Sensing, 2022; volume 7, no. 14(8), pp. 1770 (https://doi.org/10.3390/rs14081770).

CrossRef Next

58. Yan, T., Shen, S.L., Zhou, A. Indices and models of Surface water quality assessment: Review and perspectives. Environmental Pollution, 2022; volume 15, no. 308, pp. 119611 (https://doi.org/10.1016/j.envpol.2022.119611).

CrossRef Next

59. Verma, R., Sharif, M., Husain, A. Application of HEC-HMS for hydrological modeling of Upper Sabarmati River Basin, Gujarat, India. Modeling Earth Systems and Environment, 2022; volume 25, no. 8(4), pp. 5585–5593 (https://doi.org/10.1007/s40808-022-01411-9).

CrossRef Next

60. Operational overview of the application of remote sensing and hydrography for coastal zone management. Journal of Earth and Environmental Sciences, 2020; volume 1 (https://doi.org/10.29011/2577-0640.100191).

CrossRef Next

61. Sulaiman, N., Abdullah, N.M., Nazir, U., Ismail, M., Latib, S.K.K.A., Mahmud, N.P.N. Geographical Information System (GIS) and Remote sensing (RS) applications in Disaster Risk Reduction (DRR) in Malaysia. International Journal of Integrated Engineering, 2022; volume 6, no. 14(5) (https://doi.org/10.30880/ijie.2022.14.05.003).

CrossRef Next

62. Nabukonde, A., Barakagira, A., Akwango, D. The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review. Archives of Current Research International, 2023; volume 8, no. 23(2), pp. 43–49 (https://doi.org/10.9734/acri/2023/v23i2558).

CrossRef Next

63. Joiner, J., Yoshida, Y., Anderson, M., Holmes, T., Hain, C., Reichle, R., koster, R., Middleton, E., Zeng, F.W. Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. Remote Sensing of Environment, 2018; volume 25, no. 219, pp. 339–352 (https://doi.org/10.1016/j.rse.2018.10.020).

CrossRef Next

64. Chaabane, F.Z., Lamine, S., Guettouche, M.S., Bachari, N.E.I., Hallal, N. Landslide Risk Assessments through Multicriteria Analysis. ISPRS International Journal of Geo-Information, 2024; volume 25, no. 13(9), pp. 303 (https://doi.org/10.3390/ijgi13090303).

CrossRef Next

65. Whitehurst, D., Joshi, K., Kochersberger, K., Weeks, J. Post-Flood analysis for damage and restoration assessment usingdrone imagery. Remote Sensing, 2022; volume 4, no. 14(19), pp. 4952 (https://doi.org/10.3390/rs14194952).

CrossRef Next

66. Xia, H., Chen, Y., Song, C., Li, J., Quan, J., Zhou, G. Analysis of surface urban heat islands based on local climate zones via spatiotemporally enhanced land surface temperature. Remote Sensing of Environment, 2022; volume 8, no. 273:112972 (https://doi.org/10.1016/j.rse.2022.112972).

CrossRef Next

67. De Almeida, C.R., Teodoro, A.C., Gonçalves, A. Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review. Environments, 2021; volume 9, no. 8(10), pp. 105 (https://doi.org/10.3390/environments8100105).

CrossRef Next

68. Leitão, P.J., Santos, M.J. Improving models of species Ecological niches: A remote sensing overview. Frontiers in Ecology and Evolution, 2019; volume 29, no. 7 (https://doi.org/10.3389/fevo.2019.00009).

CrossRef Next

69. Zhang, X., Zhou, Y., Peng, P., Wang, G. A novel multimodal species distribution model fusing remote sensing images and environmental features. Sustainability, 2022; volume 28, no. 14(21):14034 (https://doi.org/10.3390/su142114034).

CrossRef Next

70. Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Odongo-Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters, 2021; volume 19, no. 6(2):024005 (https://doi.org/10.1088/1748-9326/abd0a8).

CrossRef Next

71. Adams, C.E., Garcia-Carreras, L. Detection of Land-Use Change and Rapid Recovery of Vegetation after Deforestation in the Congo Basin. Earth Interactions, 2023; volume 1, no. 27(1) (https://doi.org/10.1175/ei-d-22-0020.1).

CrossRef Next

72. Kowler, L.F., Pratihast, A.K., Del Arco, A.P.O., Larson, A.M., Braun, C., Herold, M. Aiming for sustainability and scalability: community engagement in forest payment schemes. Forests, 2020; volume 15, no. 11(4), pp. 444 (https://doi.org/10.3390/f11040444).

CrossRef Next

73. Abdourahamane, Z.S., Garba, I., Boukary, A.G., Mirzabaev, A. Spatiotemporal characterization of agricultural drought in the Sahel region using a composite drought index. Journal of Arid Environments, 2022; volume 16, no. 204:104789 (https://doi.org/10.1016/j.jaridenv.2022.104789).

CrossRef Next

74. Rembold, F., Meroni, M., Urbano, F., Csak, G., Kerdiles, H., Perez-Hoyos, A., Lemoine, G., Leo, O., Negre, T. ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis. Agricultural Systems, 2018; volume 15, no. 168, pp. 247–257 (https://doi.org/10.1016/j.agsy.2018.07.002).

CrossRef Next

75. Uddin, K., Matin, M.A., Meyer, F.J. Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sensing, 2019; volume 3, no. 11(13), pp. 1581 (https://doi.org/10.3390/rs11131581).

CrossRef Next

76. Rehman, A.U., Lyche, T., Awuah-Offei, K., Nadendla, V.S.S. Effect of text message alerts on miners evacuation decisions. Safety Science, 2020; volume 17, no. 130:104875 (https://doi.org/10.1016/j.ssci.2020.104875).

CrossRef Next

77. Polo, M.J. Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region: A Decade of Experience from SERVIR. Edited by Birendra Bajracharya, Rajesh Bahadur Thapa, and Mir A. Matin. Mountain Research and Development, 2022; volume 17, no. 42(2) (https://doi.org/10.1659/mrd.mm273.1).

CrossRef Next

78. Meyer, R., Davies, N., Pitz, K.J., Meyer, C., Samuel, R., Anderson, J., Appeltans, W., Barker, K., Chavez, F.P., Duffy, J.E., Goodwin, K.D., Hudson, M., Hunter, M.E., Karstensen, J., Laney, C.M., Leinen, M., Mabee, P., Macklin, J.A., Muller-Karger, F., Pade, N., Pearlman, J., Phillips, L., Provoost, P., Santi, I., Schigel, D., Schriml, L.M., Soccodato, A., Suominen, S., Thibault, K.M., Ung, V., de Kamp, J., Wallis, E., Walls, R., Buttigieg, P.L. The founding charter of the Omic Biodiversity Observation Network (Omic BON). GigaScience, 2022; volume 28, no. 12 (https://doi.org/10.1093/gigascience/giad068).

CrossRef Next

79. Himeur, Y., Rimal, B., Tiwary, A., Amira, A. Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Information Fusion, 2022; volume 25, no. 86–87, pp. 44–75 (https://doi.org/10.1016/j.inffus.2022.06.003).

CrossRef Next

80. Safonova, A., Ghazaryan, G., Stiller, S., Main-Knorn, M., Nendel, C., Ryo, M. Ten deep learning techniques to address small data problems with remote sensing. International Journal of Applied Earth Observation and Geoinformation, 2023; volume 18, no. 125:103569 (https://doi.org/10.1016/j.jag.2023.103569).

CrossRef Next

81. Zhang, Z., Zhang, Q., Hu, X., Zhang, M., Zhu, D. On the automatic quality assessment of annotated sample data for object extraction from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2023; volume 1, no. 201, pp. 153–73 (https://doi.org/10.1016/j.isprsjprs. 2023.05.026).

CrossRef Next

82. Downes, J., Bruce, D., Da Silva, G.M., Hesp, P.A. Optimising Satellite-Derived Bathymetry Using Optical Imagery over the Adelaide Metropolitan Coast. Remote Sensing, 2025; volume 28, no. 17(5), pp. 849 (https://doi.org/10.3390/rs17050849).

CrossRef Next

83. Lagunes-Gómez, I., Hernández-Orduña, M., Murrieta-Galindo, R., Hernández-Pitalua, D., Mayorga-Cruz, D. Spatial Analysis of the Empirical Behavior of Municipal Institutional Capacity for the Formulation of Sustainable Growth Management Strategies with a Regional Focus: State of Veracruz, Mexico. Sustainability, 2022; volume 10, no. 4(4), pp. 2000 (https://doi.org/10.3390/su14042000).

CrossRef Next

84. Pandey, V., Kipf, A., Neumann, T., Kemper, A. How good are modern spatial analytics systems? Proceedings of the VLDB Endowment, 2018; volume 1, no. 11(11), pp. 1661–1673 (https://doi.org/10.14778/3236187.3236213).

CrossRef Next

85. Minde, J.M., Gerlak, A.K., Colella, T., Murveit, A.M. Re-examining geospatial online participatory Tools for environmental Planning. Environmental Management, 2024; volume 15, no. 73(6), pp. 1276–1292 (https://doi.org/10.1007/s00267-024-01973-7).

CrossRef Next

86. Scott, G., Rajabifard, A. Sustainable development and geospatial information: a strategic framework for integrating a global policy agenda into national geospatial capabilities. Geo-spatial Information Science, 2017; volume 3, no. 20(2), pp. 59–76 (https://doi.org/10.1080/10095020.2017.1325594).

CrossRef Next

87. Qwaider, S., Al-Ramadan, B., Shafiullah, M., Islam, A., Worku, M.Y. GIS-Based Progress Monitoring of SDGs towards Achieving Saudi Vision 2030. Remote Sensing, 2023; volume 17, no. 15(24), pp. 5770 (https://doi.org/10.3390/rs15245770).

CrossRef Next

88. Akanbi, A., Masinde, M. A distributed stream processing middleware framework for Real-Time analysis of heterogeneous data on big data platform: case of environmental monitoring. Sensors, 2020; volume 3, no. 20(11), pp. 3166 (https://doi.org/10.3390/s20113166).

CrossRef Next

89. Chandra, G.R., Bhatia, S. Issues and concerns in the field of remote sensing. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020; volume 1, pp. 654–658 (https://doi.org/10.1109/icrito48877.2020.9197901).

CrossRef Next

90. Lombardo, L., Corbellini, S., Parvis, M., Elsayed, A., Angelini, E., Grassini, S. Wireless sensor network for distributed environmental monitoring. IEEE Transactions on Instrumentation and Measurement, 2017; volume 11, no. 67(5), pp. 1214–1222 (https://doi.org/10.1109/tim.2017.2771979).

CrossRef Next

91. Fraisl, D., Hager, G., Bedessem, B., Gold, M., Hsing, P.Y., Danielsen, F., Hitchcock, C.B., Hulbert, J.M., Piera, J., Spiers, H., Thiela, M., Hakla, M. Citizen science in environmental and ecological sciences. Nature Reviews Methods Primers, 2022; volume 25, no. 2(1) (https://doi.org/10.1038/s43586-022-00144-4).

CrossRef Next

92. Spaans, R.H., Drumond, B., Van Daalen, K.R., Vitor, A.C.R., Derbyshire, A., Da Silva, A., Lana, R.M., Vega, M.S., Carrasco-Escobar, G., Escada, M.I.S., Codeço, C., Lowe, R. Ethical considerations related to drone use for environment and health research: A scoping review protocol. PLoS ONE, 2024; volume 31, no. 19(1):e0287270 (https://doi.org/10.1371/journal.pone.0287270).

CrossRef Next

Information

Information: Geoinformatica Polonica, 2025, Vol. 24 (2025), pp. 25-41

Article type: Original scientific article

Titles:

English:
Emerging trends in GIS and remote sensing technologies for environmental monitoring: innovations, applications, and future directions
Polish: Nowe trendy w technologiach GIS i teledetekcji w zakresie monitorowania środowiska: innowacje, zastosowania i przyszłe kierunki rozwoju

Authors

https://orcid.org/0000-0001-6130-2529

Anna Szafarczyk
AGH University of Science and Technology in Kraków
, Poland
https://orcid.org/0000-0001-6130-2529 Orcid
Contact with author
All publications →

AGH University of Science and Technology in Kraków
Poland
ROR ID:

 Research Organization Registry icon 00bas1c41

https://orcid.org/0000-0001-5649-0107

Okechukwu Ebuka Agbasi
Department of Physics, College of Physical Science, Michael Okpara University of Agriculture
, Nigeria
https://orcid.org/0000-0001-5649-0107 Orcid
Contact with author
All publications →

Department of Physics, College of Physical Science, Michael Okpara University of Agriculture
Nigeria
ROR ID:

 Research Organization Registry icon 050850526

Published at: 05.12.2025

Article status: Open

Licence: CC BY 4.0  licence icon

Percentage share of authors:

Anna Szafarczyk (Author) - 50%
Okechukwu Ebuka Agbasi (Author) - 50%

Article corrections:

-

Publication languages:

English

Emerging trends in gis and remote sensing technologies for environmental monitoring: innovations, applications, and future directions

quote

download files

RIS BIB ENDNOTE