Welcome to Bilingual Publishing Group

Next-Generation Engineering Surveying: Integrating GNSS, UAV Photogrammetry, and LiDAR for Environmental Monitoring

Authors

  • Xiaoxiang Chen ORCID PowerChina Qinghai Electric Power Design Institute Co.,Ltd.,Xining 810008,China
  • Yuping Liu ORCID PowerChina Qinghai Electric Power Design Institute Co.,Ltd.,Xining 810008,China

DOI:

https://doi.org/10.30564/jees.v8i9.117334

Received:21 August 2026 |Revised:23 September 2026 |Accepted:29 September 2026 |Published Online:30 September 2026

Abstract

To monitor the environment, geospatial information that is accurate, repeatable, and high-resolution is becoming a necessity for identifying landscape change, determining hazards, and promoting sustainable management. New opportunities of the next-generation engineering surveying are associated with Global Navigation Satellite Systems (GNSS), unmanned aerial vehicle (UAV) photogrammetry, and Light Detection and Ranging (LiDAR). This review discusses the technological basis, integration processes, and environmental uses of these three complementary approaches. GNSS can be used to provide accurate positioning and time-stability for multi-period surveys; UAV photogrammetry can be used to provide high-resolution imagery and surface models in a flexible and cost-effective way; and LiDAR can be used to provide dense three-dimensional measurements, especially in complex terrain and vegetated settings. What is novel about this review is that these technologies are regarded as a cohesive surveying system rather than distinct mapping tools. The most important topics are platform choice, georeferencing, sensor calibration, data fusion, quality evaluation, uncertainty propagation, and automatic change detection. Applications are covered with reference to landform evolution, erosion, landslides, floodplains, coastal, wetlands, forest structure, mining, infrastructure impacts, and post-disaster assessment. The review notes that, to conduct reliable environmental monitoring, it is necessary not only to have high-quality sensors but also to have standardized work processes and uncertainty-aware interpretation. Artificial intelligence, cloud and edge computing, digital twins, and real-time monitoring systems are also areas of future focus. The use of integrated GNSS-UAV-LiDAR surveying is likely to promote environmental intelligence and resilient decision-making.

Keywords:

GNSS,UAV Photogrammetry,LiDAR,Engineering Surveying,Environmental Monitoring

References

[1]Dritsas, E., Trigka, M., 2025. Remote sensing and geospatial analysis in the big data era: A survey. Remote Sensing. 17(3), 550.

[2]Tsatsaris, A., Kalogeropoulos, K., Stathopoulos, N., et al., 2021. Geoinformation technologies in support of environmental hazards monitoring under climate change: An extensive review. ISPRS International Journal of Geo-Information. 10(2), 94.

[3]Panda, S.S., Rao, M., Thenjabail, P., et al., 2024. Remote sensing systems—Platforms and sensors: Aerial, satellite, UAV, optical, radar, and LiDAR. In: Thenkabail, P. (Ed.). Remote Sensing Handbook, Volume I: Remotely Sensed Data Characterization, Classification, and Accuracies. CRC Press: Boca Raton, FL, USA. pp. 3–60.

[4]Nadarajah, N., Paffenholz, J.-A., Teunissen, P.J., 2014. Integrated GNSS attitude determination and positioning for direct geo-referencing. Sensors. 14(7), 12715–12734.

[5]Jiang, S., Jiang, W., Wang, L., 2021. Unmanned Aerial Vehicle-Based Photogrammetric 3D Mapping: A survey of techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine. 10(2), 135–171.

[6]Irwin, L.A., Coops, N.C., Anders, K., et al., 2025. Light detection and ranging of natural systems. Nature Reviews Methods Primers. 5(1), 75.

[7]Huang, G., Du, S., Wang, D., 2023. GNSS techniques for real-time monitoring of landslides: A review. Satellite Navigation. 4(1), 5.

[8]Ayşe, K., Demircan, N., Özgeriş, M., et al., 2025. Integration of drones in landscape research: Technological approaches and applications. Drones. 9(9), 603.

[9]Mazlan, S.M., Wan Mohd Jaafar, W.S., Muhmad Kamarulzaman, A.M., et al., 2023. A Review on the Use of LiDAR remote sensing for forest landscape restoration. In: Suratman, M.N. (Ed.). Concepts and Applications of Remote Sensing in Forestry. Springer: Singapore. pp. 49–74.

[10]Owusu-Awuah, J., 2024. Delivering Sustainable Surveying Engineering Business in the 21st century-The Entrepreneurial Approach. In Proceedings of the FIG Working Week 2024, Accra, Ghana, 19–24 May 2024.

[11]Liu, M., Li, W., Ye, Y., et al., 2025. Identification and Assessment of Geological Hazards in Highly Vegetated Areas Based on Multi-Source Radar Remote Sensing Data: Supporting Sustainable Disaster Risk Management. Sustainability. 17(17), 8070.

[12]Kovanič, Ľ., Topitzer, B., Peťovský, P., et al., 2023. Review of photogrammetric and lidar applications of UAV. Applied Sciences. 13(11), 6732.

[13]Tmušić, G., Manfreda, S., Aasen, H., et al., 2020. Current practices in UAS-based environmental monitoring. Remote Sensing. 12(6), 1001.

[14]Aplin, P., 2006. On scales and dynamics in observing the environment. International Journal of Remote Sensing. 27(11), 2123–2140.

[15]Schofield, W., Schofield, W., Breach, M., 2007. Engineering Surveying. CRC Press: Boca Raton, FL, USA.

[16]Kulicki, M., Cabo, C., Trzciński, T., et al., 2024. Artificial intelligence and terrestrial point clouds for forest monitoring. Current Forestry Reports. 11(1), 5.

[17]Fascista, A., 2022. Toward integrated large-scale environmental monitoring using WSN/UAV/Crowdsensing: A review of applications, signal processing, and future perspectives. Sensors. 22(5), 1824.

[18]Pepple, G.T., Iwueze, S.N., 2025. Emerging Technologies and their integration in Surveying Profession. Coordinates. 7, 23–27.

[19]Rydlund Jr., P.H., Densmore, B.K., 2012. Methods of Practice And Guidelines for Using Survey-Grade Global Navigation Satellite Systems (GNSS) to Establish Vertical Datum in the United States Geological Survey. US Geological Survey: Reston, VA, USA.

[20]Grinter, T., 2018. Precise Point Positioning—The Evolution of an Alternative GNSS Positioning Solution [Master’s Thesis]. UNSW Sydney: Sydney, Australia.

[21]Dąbrowski, P.S., Specht, C., Specht, M., et al., 2021. Integration of multi-source geospatial data from GNSS receivers, terrestrial laser scanners, and unmanned aerial vehicles. Canadian Journal of Remote Sensing. 47(4), 621–634.

[22]Ok, E., 2025. Traditional Surveying vs. UAV Drone-Based Structure-from-Motion Advancements in Topographical Mapping Accuracy and Efficiency. ResearchGate GmbH: Berlin, Germany.

[23]Gracchi, T., Rossi, G., Tacconi Stefanelli, C., et al., 2021. Tracking the evolution of riverbed morphology on the basis of UAV photogrammetry. Remote Sensing. 13(4), 829.

[24]Burdziakowski, P., Bobkowska, K., 2021. UAV photogrammetry under poor lighting conditions—Accuracy considerations. Sensors. 21(10), 3531.

[25]Fernández‐Hernandez, J., González-Aguilera, D., Rodríguez-Gonzálvez, P., et al., 2015. Image‐based modelling from unmanned aerial vehicle (UAV) photogrammetry: An effective, low‐cost tool for archaeological applications. Archaeometry. 57(1), 128–145.

[26]Remondino, F., Nocerino, E., Toschi, I., et al., 2017. A critical review of automated photogrammetric processing of large datasets. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 42, 591–599.

[27]Lefsky, M.A., Warren, B.C., Geoffrey, G.P., et al., 2002. Lidar remote sensing for ecosystem studies: LiDAR, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience. 52(1), 19–30.

[28]Istenič, K., Gracias, N., Arnaubec, A., et al., 2019. Scale accuracy evaluation of image-based 3D reconstruction strategies using laser photogrammetry. Remote Sensing. 11(18), 2093.

[29]Sharma, V.B., 2023. Automatic Identification of Deformations in Buildings and Pipelines Using 3D Point Cloud Data for Structural Health Monitoring with LiDAR Technology [PhD Thesis]. Rajiv Gandhi Institute of Petroleum Technology: Bahadurpur, India.

[30]Zhang, J., Lin, X., 2017. Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing. International Journal of Image and Data Fusion. 8(1), 1–31.

[31]Zhou, T., Hasheminasab, S.M., Habib, A., 2021. Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems. ISPRS Journal of Photogrammetry and Remote Sensing. 180, 336–356.

[32]Karaim, M., Elsheikh, M., Noureldin, A., 2018. GNSS error sources. In: Rustamov, R.B., Hashimov, A.M. (Eds). Multifunctional Operation and Application of GPS. IntechOpen: London, UK.

[33]Glennie, C., 2007. Rigorous 3D error analysis of kinematic scanning LIDAR systems. Journal of Applied Geodesy. 1(3), 147.

[34]Raufu, I.O., Tata, H., Adewole, E.B., 2025. Integration of UAV-derived digital elevation model and GNSS for orthometric height determination in urban mapping. Nova Geodesia. 5(3), 418.

[35]Christofi, D., Mettas, C., Evagorou, E., et al., 2025. A review of open remote sensing data with GIS, AI, and UAV support for shoreline detection and coastal erosion monitoring. Applied Sciences. 15(9), 4771.

[36]Barbarella, M., Fiani, M., 2013. Monitoring of large landslides by Terrestrial Laser Scanning techniques: Field data collection and processing. European Journal of Remote Sensing. 46(1), 126–151.

[37]Williams, K., Olsen, M., Roe, G., et al., 2013. Synthesis of transportation applications of mobile LiDAR. Remote Sensing. 5(9), 4652–4692.

[38]Gawronek, P., Klapa, P., Sochacki, D., et al., 2024. Multi-Platform Collaboration in Integrated Surveying: Ensuring Completeness and Reliability of Geospatial Data—A Case Study. Remote Sensing. 16(23), 4499.

[39]Yan, W., 2017. Development of an Algorithm for a GNSS/MEMS-IMU/Imaging Coupled System for Direct Georeferencing [PhD Thesis]. Universidade do Porto: Porto, Portugal.

[40]Zeybek, M., 2021. Accuracy assessment of direct georeferencing UAV images with onboard global navigation satellite system and comparison of CORS/RTK surveying methods. Measurement Science and Technology. 32(6), 065402.

[41]Zhang, Z., 2021. Camera calibration. In: Ikeuchi, K. (Ed.). Computer Vision: A Reference Guide. Springer: Cham, Switzerland. pp. 130–131.

[42]Borre, A., 2025. Modelling the Dynamic Impacts of Consecutive Natural Hazards: A Framework for Time-Evolving Vulnerability and Recovery Analysis [PhD Thesis]. Università degli Studi di Genova: Genova, Italy.

[43]Toth, C.K., Koppanyi, Z., 2018. Strip adjustment. In Topographic Laser Ranging and Scanning. CRC Press: Boca Raton, FL, USA. pp. 259–289.

[44]Lacoste, A., Lehmann, N., Rodriguez, P., et al., 2023. Geo-bench: Toward foundation models for earth monitoring. Advances in Neural Information Processing Systems. 36, 51080–51093.

[45]Ogundare, J.O., 2015. Precision Surveying: The Principles and Geomatics Practice. John Wiley & Sons: Hoboken, NJ, USA.

[46]Ogaja, C.A., 2022. GNSS data processing. In Introduction to GNSS Geodesy: Foundations of Precise Positioning Using Global Navigation Satellite Systems. Springer: Cham, Switzerland. pp. 119–134.

[47]Wu, Q., Wang, M., 2007. A framework for risk assessment on soil erosion by water using an integrated and systematic approach. Journal of Hydrology. 337(1–2), 11–21.

[48]Lahat, D., Adali, T., Jutten, C., 2015. Multimodal data fusion: An overview of methods, challenges, and prospects. Proceedings of the IEEE. 103(9), 1449–1477.

[49]Xin, Q., 2025. Uncertainty-Aware Late Fusion for 3D Perception (Confidence Calibration+ Fusion Rule Learning). Journal of Technology Informatics and Engineering. 4(1), 215–238.

[50]Khalil, R., 2013. The accuracy of GIS tools for transforming assumed total station surveys to real world coordinates. Journal of Geographic Information System. 5(5), 486.

[51]Ellingsen, K.E., Yoccoz, N.G., Tveraa, T., et al., 2017. Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions. Environmental Monitoring and Assessment. 189(11), 595.

[52]Pang, C., Wang, J., Liu, Z., et al., 2025. Robust LiDAR-IMU-GNSS fusion pose estimation with the point cloud registration uncertainty modeling. Measurement. 258, 119424.

[53]Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L., et al., 2007. Uncertainty in the environmental modelling process—A framework and guidance. Environmental Modelling & Software. 22(11), 1543–1556.

[54]Bello, S.A., Yu, S., Wang, C., et al., 2020. Deep learning on 3D point clouds. Remote Sensing. 12(11), 1729.

[55]Liu, D., Yan, Z., Ding, W., et al., 2019. A survey on secure data analytics in edge computing. IEEE Internet of Things Journal. 6(3), 4946–4967.

[56]Elamin, A., Abdelaziz, N., El-Rabbany, A., 2022. A GNSS/INS/LiDAR integration scheme for UAV-based navigation in GNSS-challenging environments. Sensors. 22(24), 9908.

[57]Bruma, S., Sabou, C.-S., Pop, I., et al. (Eds.), Integration of UAV-Based LiDAR, Photogrammetry, and Slam Technologies for the Complete above and below Ground Mapping of Mountainous Hydro-Technical Infrastructure. In Scientific Papers Series E: Land Reclamation, Earth Observation & Surveying, Environmental Engineering, Vol. XIV. University of Agronomic Sciences and Veterinary Medicine: București, Romania.

[58]Cao, X., Liu, Z., Hu, C., et al., 2024. Three-dimensional geological modelling in earth science research: An in-depth review and perspective analysis. Minerals. 14(7), 686.

[59]Tomás, R., Li, Z., 2017. Earth observations for geohazards: Present and future challenges. Remote Sensing. 9(3), 194.

[60]Sepuru, T.K., Dube, T., 2018. An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. Remote Sensing Applications: Society and Environment. 9, 1–9.

[61]Flener, C., Vaaja, M., Jaakkola, A., et al., 2013. Seamless mapping of river channels at high resolution using mobile LiDAR and UAV-photography. Remote Sensing. 5(12), 6382–6407.

[62]Li, R., Splinter, K.D., Felder, S., 2021. LiDAR scanning as an advanced technology in physical hydraulic modelling: The stilling basin example. Remote Sensing. 13(18), 3599.

[63]Gesch, D.B., 2009. Analysis of LiDAR elevation data for improved identification and delineation of lands vulnerable to sea-level rise. Journal of Coastal Research. 2009(10053), 49–58.

[64]Nghiem, S.V., Zuffada, C., Shah, R., et al., 2017. Wetland monitoring with global navigation satellite system reflectometry. Earth and Space Science. 4(1), 16–39.

[65]Mîzgaciu, L.,Tudoran, G.M., Ciocan, A.E., et al., 2025. A Comparative Analysis of UAV LiDAR and Mobile Laser Scanning for Tree Height and DBH Estimation in a Structurally Complex, Mixed-Species Natural Forest. Forests. 16(9), 1481.

[66]Koukoulas, S., Blackburn, G.A., 2004. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS. International Journal of Remote Sensing. 25(15), 3049–3072.

[67]Maeda, E.E., Brede, B., Calders, K., et al., 2025. Expanding forest research with terrestrial LiDAR technology. Nature Communications. 16(1), 8853.

[68]Singhroy, V., Fobert, M.A., Li, J., et al., 2020. Advanced radar images for monitoring transportation, energy, mining and coastal infrastructure. In: Singhroy, V. (Ed.). Advances in Remote Sensing for Infrastructure Monitoring. Springer: Cham, Switzerland. pp. 3–40.

[69]Tsachouridis, S., Pavloudakis, F., Sachpazis, C., et al., 2025. Monitoring Slope Stability: A Comprehensive Review of UAV Applications in Open-Pit Mining. Land. 14(6), 1193.

[70]Gaspari, F., Ioli, F., Barbieri, F., et al., 2022. Integration of UAV-lidar and UAV-photogrammetry for infrastructure monitoring and bridge assessment. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Nice, France, 6–11 June 2022; pp. 995–1002.

[71]Duo, E., Trembanis, A.C., Dohner, S., et al., 2018. Local-scale post-event assessments with GPS and UAV-based quick-response surveys: A pilot case from the Emilia–Romagna (Italy) coast. Natural Hazards and Earth System Sciences. 18(11), 2969–2989.

[72]Chen, Z., 2024. Application of UAV remote sensing in natural disaster monitoring and early warning: An example of flood and mudslide and earthquake disasters. Highlights in Science, Engineering and Technology. 85, 924–933.

[73]Li, X., Ge, M., Dai, X., et al., 2015. Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. Journal of Geodesy. 89(6), 607–635.

[74]Shi, W., Zhang, M., Zhang, R., et al., 2020. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing. 12(10), 1688.

Downloads

Issue

Article Type

Review