LiDAR UAV Surveying for Flood Modeling and River Basin Mapping
Solution Overview
The Tanzania Urban Resilience Program deployed an innovative combination of Light Detection and Range (LiDAR) UAV surveying and bathometric surveying to support flood modelling within the flood-prone Msimbazi river basin.[1] This World Bank funded survey project served two purposes: generation of reliable, accurate and fit for purpose bathymetric and topographic data of the Msimbazi River and its floodplanes for riverine flooding related detailed studies, and rigorous comparison of Digital Terrain Model (DTM) generation by Unmanned Aerial Vehicle Photogrammetry and UAV LiDAR.[2] The outputs were used to guide river upgrades that improved Dar es Salaam's resilience to flooding.[1]
Technical Components
The LiDAR system used for this comparison weighed 3.5kg and was deployed with a multi-rotor drone (DJI M600pro), bringing the total take-off weight to just under 15 kg.[4] For this project, 16 out of 32 lasers were used to construct the point cloud using the Velodyne HDL 32e laser scanner, which provided 2 returns per measurement.[4] Because all components that determined the accuracy of the final point cloud were attached to the drone, there was no need for ground control points, saving considerable time.[4] A very positive aspect about the LiDAR survey acquisition was real time control on the measurement of the area.[4] Because it was a direct measurement technique, the results could be monitored in real time via WiFi/3G/4G connection with the system on a field laptop or even from a computer in an office using 3G/4G only.[4]
Implementation Details
CDR International in association with Shore Monitoring and Research defined an integral full survey campaign for the lower basin of the Msimbazi River, which was executed in February 2019.[2] Shore Monitoring and Research BV and CDR International BV conducted bathymetric and topographic surveying of flood-prone rivers in Dar es Salaam for The World Bank, with a final report dated August 7, 2019.[3] The entire survey campaign for the Msimbazi River area took place from 14th until 20th of February 2019.[4] The LiDAR survey was conducted from 7 different home locations throughout the Msimbazi Valley area in Dar es Salaam, which all needed to be assessed on appropriateness, security and possibility to get permission from local leaders of the area.[4] The multi-rotor with the LiDAR systems could perform flights of 13-15 minutes, after which batteries needed to be changed and therefore the drone had to come home and land in a controlled way regularly.[4]
Benefits and Impacts
UAV LiDAR potentially provided significant cost savings compared to both traditional LiDAR and field surveying methods.[2] The elimination of ground control points through the integrated system design saved substantial time during survey operations.[4] The real-time monitoring capability enabled immediate quality control and verification of measurement coverage during field operations.[4]
Climate Adaptation Relevance
This approach directly addressed flooding hazards within the flood-prone Msimbazi river basin by generating reliable, accurate bathymetric and topographic data for riverine flooding related detailed studies.[1][2] The survey outputs were used to guide river upgrades that improved Dar es Salaam's resilience to flooding.[1]
Business Analysis
Preliminary insights of the analysis were shared and presented at a master class at the Land and Poverty Conference 2019 on March 25-29, 2019 in Washington, DC and at a workshop on DTM drone applications for Dar es Salaam stakeholders at World Bank Office in Dar es Salaam on April 11th, 2019.[2] The World Bank funded this survey project as part of the Tanzania Urban Resilience Program.[2][3] The technology potentially provided significant cost savings compared to both traditional LiDAR and field surveying methods, making it an economically viable approach for flood modelling applications.[2]
Sources
- [1]: worldbank.org
- [2]: cdr-international.nl
- [3]: documents.worldbank.org
- [4]: s3.amazonaws.com