Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina

Among the different types of natural disasters, floods are the most devastating, widespread, and frequent. Floods account for approximately 30% of the total loss caused by natural disasters. Accurate flood-risk mapping is critical in reducing such damages by correctly predicting the extent of a flood when coupled with rain and stage gage data, supporting emergency-response planning, developing land use plans and regulations with regard to the construction of structures and infrastructures, and providing damage assessment in both spatial and temporal measurements. The reliability and accuracy of such flood assessment maps is dependent on the quality of the digital elevation model (DEM) in flood conditions. This study investigates the quality of an Unmanned Aerial Vehicle (UAV)-based DEM for spatial flood assessment mapping and evaluating the extent of a flood event in Princeville, North Carolina during Hurricane Matthew. The challenges and problems of on-demand DEM production during a flooding event were discussed. An accuracy analysis was performed by comparing the water surface extracted from the UAV-derived DEM with the water surface/stage obtained using the nearby US Geologic Survey (USGS) stream gauge station and LiDAR data.

[1]  Santiago Zazo,et al.  Analysis of flood modeling through innovative geomatic methods , 2015 .

[2]  Ľudovít Kovanič,et al.  Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study , 2016 .

[3]  Gustav Tolt,et al.  Accuracy evaluation of 3D lidar data from small UAV , 2015, SPIE Security + Defence.

[4]  Oluibukun Gbenga Ajayi,et al.  Generation of accurate digital elevation models from UAV acquired low percentage overlapping images , 2017 .

[5]  Christoph Strecha,et al.  Classification of Aerial Photogrammetric 3D Point Clouds , 2017, ArXiv.

[6]  Andreas Scheidegger,et al.  Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas , 2015 .

[7]  S. Coveney,et al.  Lightweight UAV digital elevation models and orthoimagery for environmental applications: data accuracy evaluation and potential for river flood risk modelling , 2017 .

[8]  Eija Honkavaara,et al.  Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera , 2012, Sensors.

[9]  A. Viguria,et al.  Evaluating the accuracy of DEM generation algorithms from UAV imagery , 2013 .

[10]  B. Sanders.  Evaluation of on-line DEMs for flood inundation modeling , 2007 .

[11]  D. Petrea,et al.  Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: case study in the north-east of Apuseni Mountains, Transylvania , 2016, Natural Hazards.

[12]  A. Tamminga,et al.  Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management , 2015 .

[13]  F. Nex,et al.  Quality assessment of combined IMU/GNSS data for direct georeferencing in the context of UAV-based mapping , 2017 .

[14]  A. Lechner,et al.  CHARACTERISING UPLAND SWAMPS USING OBJECT-BASED CLASSIFICATION METHODS AND HYPER-SPATIAL RESOLUTION IMAGERY DERIVED FROM AN UNMANNED AERIAL VEHICLE , 2012 .

[15]  P. Barry,et al.  FIELD ACCURACY TEST OF RPAS PHOTOGRAMMETRY , 2013 .

[16]  Ben Jarihani,et al.  Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment , 2017, ISPRS Int. J. Geo Inf..

[17]  J. Gonçalves,et al.  UAV photogrammetry for topographic monitoring of coastal areas , 2015 .

[18]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[19]  Lars T. Waser,et al.  High‐quality image matching and automated generation of 3D tree models , 2008 .

[20]  Taejung Kim,et al.  DSM Generation and Accuracy Analysis from UAV Images on River-side Facilities , 2015 .

[21]  V. Klemas,et al.  Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview , 2015 .

[22]  A. S. Toprak,et al.  DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill , 2015 .

[23]  Heiko Apel,et al.  Flood risk analyses—how detailed do we need to be? , 2009 .

[24]  Anders la Cour-Harbo,et al.  Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry , 2018 .

[25]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[26]  Anuar Ahmad,et al.  Assessment of Photogrammetric Mapping Accuracy Based on Variation Flying Altitude Using Unmanned Aerial Vehicle , 2014 .

[27]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[28]  Ayman Habib,et al.  ERROR ANALYSIS FOR THE AIRBORNE DIRECT GEOREFERINCING TECHNIQUE , 2016 .

[29]  T. I. Eldho,et al.  A web GIS based integrated flood assessment modeling tool for coastal urban watersheds , 2014, Comput. Geosci..

[30]  E. Gülch,et al.  Advanced Matching Techniques for High Precision Surface and Terrain Models , 2009 .

[31]  Loretta Ichim,et al.  Flood areas detection based on UAV surveillance system , 2015, 2015 19th International Conference on System Theory, Control and Computing (ICSTCC).

[32]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[33]  T. Udelhoven,et al.  SENSITIVITY ANALYSIS OF UAV-PHOTOGRAMMETRY FOR CREATING DIGITAL ELEVATION MODELS (DEM) , 2012 .

[34]  C. Strecha,et al.  The Accuracy of Automatic Photogrammetric Techniques on Ultra-light UAV Imagery , 2012 .