Bridging the skies and space: A comparative analysis of satellite and aerial data for urban waterlogging assessment – A case study of Sector 28 corridor between Dwarka and Khaira, New Delhi, India
Article Main
Abstract
Urban waterlogging is a serious problem in fast urbanizing regions, exacerbated by climatic variations and poor drainage facilities. This paper situates Unmanned Aerial Vehicle (UAV) and Light Detection and Ranging (LiDAR) remote sensing methods as viable tools for assessing waterlogging in the Dwarka-Khaira corridor, New Delhi, India. Conventional terrestrial field monitoring is restricted by resource constraints and scale, whereas high-resolution aerial methods provide detailed hydrological information. UAV and LiDAR deliver excellent spatial resolution and vertical accuracy necessary for the identification of microtopographic features critical for detecting hydrological restrictions. The RMSE of the UAV-derived DEMs was highly reduced to 4.6 m through post processing, with an increased accuracy of 83.66% and was conducive for a good performance in hydrological modeling. UAV data, which had ground sample distance less than 5 cm, facilitated urban feature classification with an accuracy of 98%) and helped to mitigate spectral confusion and misclassifi cation observed from Landsat (30 m) data. High-resolution aerial data therefore minimized false-positives and enhanced network extraction quality as compared to their satellite based counterparts. But while satellite imagery is not well matched for development level analysis, it continues to offer useful potential for regional assessment of vulnerability. Combining data from both sources is consistent with evidence from similar research, in support of models based on Sustainable Development Goal resilience targets. The application of these enhanced methods ed to a 30-40% decrease in identified waterlogged areas. So, the study offers a scientific basis for urban water management policies and planning.
Article Details
Article Details
Keywords
Resolution, Unmanned aerial vehicle , Vulnerability, Waterlogging
References
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Guan, Z., Chen, Y., Zhao, Y., Zhang, S., Jin, H., Yang, L., Yan, W., Zheng, S. & Lu, P., Key laboratory of VGE of ministry of education, Jiangsu center for collaborative innovation in geographical information resource development and application, & school of geography science and geomatics engineering. (2024). STFS-urban: Spatio-temporal flood simulation model for urban areas. In Journal of Environmental Management, ( 349, p. 119289) [Research article]. https://doi.org/10.1016/j.jenvman.2023.119289
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Keshava, N. & Mustard, J. (2002). Spectral unmixing. IEEE signal processing magazine, 19(1), 44–57. https://doi.org/10.1109/79.974727
Kim, E. & Kim, S. (2022). Global navigation satellite system real-time kinematic positioning framework for precise operation of a swarm of moving vehicles. Sensors, 22(20), 7939. https://doi.org/10.3390/s22207939
Kumari, A. & Karthikeyan, S. (2023). Sentinel-2 data for land use/land cover mapping: A meta-analysis and review. SN Computer Science, 4(6). https://doi.org/10.1007/s42979-023-02214-0
Laroche-Pinel, E., Cianciola, V., Singh, K., Vivaldi, G. A. & Brillante, L. (2024). Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation. Agricultural Water Management, 306, 109163. https://doi.org/10.1016/j.agwat.2024.109163
Liu, N. X. (2008). Airborne LiDAR for DEM generation: some critical issues. Progress in physical geography earth and environment, 32(1), 31–49. https://doi.org/10.1177/0309133308089496
Lei, Y., Treuhaft, R., Keller, M., Dos-Santos, M., Gonçalves, F. & Neumann, M. (2018). Quantification of selective logging in tropical forest with spaceborne SAR interferometry. Remote Sensing of Environment, 211, 167–183. https://doi.org/10.1016/j.rse.2018.04.009
Mashala, M. J., Dube, T., Mudereri, B. T., Ayisi, K. K. & Ramudzuli, M. R. (2023). A systematic review on advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi-arid tropical environments. Remote Sensing, 15(16), 3926. https://doi.org/10.3390/rs15163926
Matese, A., Toscano, P., Di Gennaro, S., Genesio, L., Vaccari, F., Primicerio, J., Belli, C., Zaldei, A., Bianconi, R. & Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing, 7(3), 2971–2990. https://doi.org/10.3390/rs70302971
Mouzakidou, K., Brun, A., Cucci, D. A. & Skaloud, J. (2024). Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment. ISPRS Open Journal of Photogrammetry and Remote Sensing, 12, 100057. https://doi.org/10.1016/j.ophoto.2023.100057
Nagel, G. W., Darby, S. E., Leyland, J. & The Authors. (2023). The use of satellite remote sensing for exploring river meander migration [Journal-article]. Earth-Science Reviews, 247, 104607. https://doi.org/10.1016/j.earscirev.2023.104607
Ngo, D., Lee, S. & Kang, B. (2020). Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sensing, 12(14), 2233. https://doi.org/10.3390/rs12142233
Ogato, G. S., Bantider, A. & Geneletti, D. (2021). Dynamics of land use and land cover changes in Huluka watershed of Oromia Regional State, Ethiopia. Environmental Systems Research, 10(1). https://doi.org/10.1186/s40068-021-00218-4
Okolie, C. J., a,b & Smit, J. L., University of cape town, & university of Lagos. (2022). A systematic review and meta-analysis of digital elevation model (DEM) fusion: preprocessing, methods and applications [Journal-article]. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 1–29. https://doi.org/10.1016/j.isprsjprs.2022.03.016
Pahari, S. & Tribhuvan University. (2023). Processing drone image using agisoft metashape and comparative analysis of different digital elevation model (DEM). In technical report [Report]. https://doi.org/10.13140/RG.2.2.16060.86405
Prajapati, G. S., Rai, P. K., Mishra, V. N., Singh, P. & Shahi, A. P. (2021). Remote sensing-based assessment of waterlogging and soil salinity: A case study from Kerala, India. Results in Geophysical Sciences, 7, 100024. https://doi.org/10.1016/j.ringps.2021.100024
Roberts, K. C., Lindsay, J. B., Berg, A. A. & Department of geography, Environment & Geomatics, The university of Guelph. (2019). An analysis of ground-point classifiers for terrestrial LiDAR [Journal-article]. Remote Sens., 11(1915). https://www.mdpi.com/journal/remotesensing
Rodriguez, D. J. C., Barresi, A. A. & Demichela, M. (2025). Multi-scale characterization of industrial infrastructure vulnerability to multiple hazards in their territories. Journal of Safety Science and Resilience. https://doi.org/10.1016/j.jnlssr.2024.11.004
Romero-Chambi, E., Villarroel-Quezada, S., Atencio, E. & Felipe Muñoz-La Rivera. (2020). Analysis of optimal flight parameters of unmanned aerial vehicles (UAVs) for detecting potholes in pavements. In Applied Sciences 10, 4157. https://doi.org/10.3390/app10124157
Rossi, M. J. & Vervoort, R. W. (2025). Enhancing inundation mapping with geomorphological segmentation: Filling in gaps in spectral observations. The Science of the Total Environment, 997, 180180. https://doi.org/10.1016/j.scitotenv.2025.180180
Roy, S., Bose, A., Singha, N., Basak, D. & Chowdhury, I. R. (2021). Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach. Environmental Challenges, 4, 100194. https://doi.org/10.1016/j.envc.2021.100194
Sar, N., Chatterjee, S. & Adhikari, M. D. (2015). Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India. Modeling Earth Systems and Environment, 1(4). https://doi.org/10.1007/s40808-015-0039-9
Seenipandi, K., Ramachandran, K. K., Ghadei, P., & Shekhar, S. (2021). Seasonal variability of sea surface temperature in Southern Indian coastal water using Landsat 8 OLI/TIRS images. In Remote Sensing of Ocean and Coastal Environments (pp. 277-295). Elsevier. https://doi.org/10.1016/b978-0-12-819604-5.00016-0
Shahbazi, M., Sohn, G., Théau, J. & Menard, P. (2015). Development and evaluation of a UAV-Photogrammetry system for precise 3D environmental modeling. Sensors, 15(11), 27493–27524. https://doi.org/10.3390/s151127493
Shahfahad, N., Talukdar, S., Naikoo, M. W., Rahman, A., Gagnon, A. S., Islam, A. R. M. T.& Mosavi, A. (2022). Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2022.2152496
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Bridging the skies and space: A comparative analysis of satellite and aerial data for urban waterlogging assessment – A case study of Sector 28 corridor between Dwarka and Khaira, New Delhi, India. (2025). Journal of Applied and Natural Science, 17(4), 1837-1855. https://doi.org/10.31018/jans.v17i4.6972
