Advantages of LiDAR in Flood Modelling And Important Parameters for LiDARMukesh Vyas GIS Engineer Modelling Department RMSI Private Limited mukesh.vyas@rmsi.com
Abstract:
LIDAR is a relatively new technological tool that can be used to accurately georeference terrain features. LIDAR is an acronym for LIght Detection And Ranging and in some literature it is referred to as Laser Altimetry. Recently emerged technique of airborne altimetry, provides accurate topographic data at high speed. Higher density, higher accuracy, less time for data collection and processing, mostly automatic system, weather and light independence, minimum ground control required, and data being available in digital format right at beginning, are several advantages offered by this technology, over the other methods of topographic data collection. Features like buildings, dykes, constructed river banks or roads have great effect on flow dynamics and flood propagation and as such must be accounted for in the model set-up. Only high resolution input data can solve the purpose that relates to the systems topography as well as to the identified features. Frequent urban flooding is observed in many parts of the world over the past decades, an urgent need is identified to improve and increase our modeling efforts and to address more explicitly the effect model input data has on the simulation results. Even difference of few meters can means a lot in loss calculations in urban areas. Society demands accurate and detailed information on magnitude and likeliness of hazardous flood events for design of flood mitigation measures. RMSI took initiative to carry R&D by using high resolution DEM derived by today’s latest technology of LiDAR and to identify its advantages by finding important parameters that direct the accuracy of derived DEM which is ultimately used for Flood Modeling purpose. Acknowledgement and Project Area: The project area consists of Tenmile and Fifteenmile Creeks, Idaho, US. Thanks to “Bureau of Reclamation, Boise, Idaho, US” for providing High Resolution DEM (2m) generated from High Accuracy LiDAR data through its website. The project emcompassed an area of approximately 150 square miles. LiDAR data was collected with 2.0 to 2.2 meter nominal post spacing, 25 percent field of view and a 30 percent overlap. Ground control survey was conducted and LiDAR data processing included processing of the raw LiDAR data through a minimum block algorithm to classify points as bare-earth or non-bare earth. The city is situated on the river bottom, which slopes gentle from the foothills of the Boise Mountains to the Boise River. Most of the gulches opening from the hills adjacent to the city are dry, except when the snow is melting or when unusual precipitation occurs. Hulls Gulch naturally carried a small flow of water, but most of this has been diverted to contribute to the water supply of the city and of the military post. The water coming down this gu1ch originally found its way to the river, over the surface, by a rather poorly defined channel, the course of which changed frequently, owing to the heavy deposit of sand carried. Data Information, Processes and Results: DEM from LiDAR data having 2m cell size is having less than +/- 3 cm in the horizontal and less than +/- 5 cm in the vertical accuracy. Data were projected to Grid Coordinate System of “Universal Transverse Mercator (UTM), Zone Number 11”, and having Datum as “North American Datum (NAD) 1983”. ArcGIS 9x software was used for the processing of data. The processes include:
 Fig. 1 Level of Information Content in High Resolution Data and its Accuracy `` Catchments were derived for both data and using in-house customized tool for generating “Maximum Flood Zone (MFZ)” grids for a particular flood depth were generated. The tool is customized using “Cost Allocation” algorithm. This tool works on principle that “it calculates for each cell its nearest source based on the least accumulative cost over a cost surface”. The inputs required for generating MFZ grids are as under:
 Fig. 2 Maximum Flood Zone (MFZ) generated using LiDAR data compared with 100 yr RP FEMAFlood Extents FEMA’s FIRM (Flood Insurance Rate Map) gives mere indication of Flood extents but does not provide any information about the losses. As can be seen in above image, the flood extent generated by high resolution data is almost matching with the FEMA flood extent. Now once the flood extents are known the loss calculations can be computed by using calibrated Vulnerability functions against claims data from multiple insurers for a variety of occupancies, lines of business and coverages. LiDAR Technology, Important Parameters for LiDAR Data and DEM Generation: Till now we discussed about deriving flood extents which was found accurate enough, but for such a accurate flood extents, there are parameters of LiDAR which should be properly and accurately calibrated and after accurate filtration of LiDAR Data DEM generation also needs much care. Direct measurement of all important parameters such as X, Y, Z, Omega, Phi and Kappa is now-a-days possible which is known as Direct Geo-referencing. By combining GPS and INertial System (INS) or Inertial Measurement Unit (IMU), these parameters can be accurately measured and by mounting precise camera, images can also be captured simultaneously.  Fig. 3 LiDAR Work Flow Data Capturing: The LiDAR system is fully integrated with airborne inertial navigation system to provide directly observed exterior orientation parameters for each frame of imagery. The camera can be rigidly with LiDAR unit and can be boresighted with the same high-precision IMU. Each exposure location is time-tagged and recorded as a GPS event. So during post-processing of differential GPS and IMU data, it yields position and orientation data of high accuracy. The imagery taken on-the-fly of LiDAR data capture can be monitored by operator to ensure optimal exposure settings and to check area of interest coverage as well as overlaps. On-the-fly, image header information will be generated containing exposure number, position (x/y/z), and orientation (omega/phi/kappa). Flight reports are recorded during image acquisition. Beginning and ending frame numbers are marked for each flight line and GPS time tags are recorded as part of the image metadata for each frame. During post-mission processing the GPS time is combined with the precise inertial flight navigation data and the X, Y, Z, kappa, phi, and omega values are then computed for each frame. Flight Planning: LiDAR data acquisition mission is first planned over Area of Interest, in which usually, block is designed. Once block is designed, depending on the shape of block, numbers of parallel strips of flight and numbers of crossing strips of flight are planned based on swath of LiDAR system keeping required overlap. Control Points should be accurately measured and fixed, so as to cover all control points by crossing flight strips. Redundancy and Confidence of later adjustment is increased by such kind of adjustments. Longer data strips should be crossed by more than one crossing strips. This reduces errors introduced by GPS accuracy variations usually occurring during long strips. If the block consists of a series of different regular sub-blocks, isolated sub-blocks should be fixed by an additional control point. Once, the data for area of interest is captured, post processing of raw LiDAR data starts. In post processing of data, there are mainly two steps; 1) Calibration and Orientation of captured data for six important parameters (X, Y, Z, Phi, Omega and Kappa) and, 2) Filtration of processed data (Automatically by algorithm or Manually by software) for generating Bare Earth DEM. 1) Calibration and Orientation of captured data for six important parameters (X, Y, Z, Phi, Omega and Kappa): All LiDAR system should be calibrated, but that is not enough to achieve accurate positioning. The calibration parameters need to be checked for each flight session. Calibration is based on comparing the laser data produced by different flight passes which overlap each other. The calibration is normally based on surface-to-surface matching of the different flight passes. As preparation step, one has to classify ground in each flight passes separately to remove the noise that vegetation would bring into the comparison. The most common, basic matching steps are:
2) Filtration of processed data (Automatically by algorithm or Manually by software) for generating Bare Earth DEM: Selection of appropriate algorithm for filtration of Bare Earth points and Non-Bare Earth points is also important. Blindly selecting an algorithm may result in filtration of bare earth points into non-bare earth points. Selection process of appropriate automated algorithm should consider following factors for proper and accurate filtration/classification of bare earth points and non-bare earth points.
On the other hand, the DEM resolution has significant effect on simulation results. Innundation extent, flow velocity, flow depth as well as flow pattern are some of the affected Flood simulation characteristics. Carefully, appropriate re-sampling technique should be applied and cross-validated for accuracy to have accurate results. Conclusions: LiDAR has developed into a very efficient tool and still has very high potential for development. Laser scanning nowadays is used in a wide range of applications and is complementing other techniques in some applications, while have started completely replacing in some applications. A reliable basic elevation model has to have a high density of measuring points to provide a sound basis for all diverse applications. Micro flood modeling for urban areas can also be carried using LiDAR data. Proper planning of flight paths and the kind of system to be used should be decided in prior for any application. Once data is captured, Post processing should be carefully done by Highly Skilled Engineers to calibrate the data and to filter the data as per the project specifications. LiDAR data, if accurately filtered can generates accurate results so as to lead to accurate loss calculations and risk analysis than using data from any other techniques. References: Materials
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