Improving Floodplain Roughness Information Using LiDAR Data - Coursework Example

IMPROVING FLOODPLAIN ROUGHNESS INFORMATION USING LIDAR DATA Name: Course: Instructor: Date: Literature Review Contents IMPROVING FLOODPLAIN ROUGHNESS INFORMATION USING LIDAR DATA 1 Name: 1 Course: 1 Instructor: 1 Date: 1 Introduction 3 Technology in Surface Roughness and Flood mapping 3 Surface features and Terrain 5 LiDAR and Inundation 6 LiDAR and Remote Sensing 8 Shortcomings of LiDAR 11 Conclusion 11 REFERENCES 13 APPENDIX 16 Introduction Flood inundation models are continuously cited as the most important and therefore inevitable tools for mitigating the effects of flooding in flood-prone areas. Flood inundation models provide reliable predictions of the occurrence of flood, the extent as well as the probable depth of the floods which then form the basis onto which the development of spatially accurate hazard maps are developed (Gomes, 1999). Similarly, the Flood inundation models make it possible for modellers to carry out the various assessments of the extent of risk posed to both life and property found within the floodplain with an aim of coming up with a prioritisation plan of either modification and further improvement of current flood defences in the flood plain or the construction of completely new features in the same regard. However, Flood inundation models cannot be developed without the establishment of the surface roughness. As a result, the moist suitable for establishing such surface roughness must be identified prior to the development of the Flood inundation model for any given location (Gomes et al. 1999). Technology in Surface Roughness and Flood mapping Technology has found extensive applications in establishment of surface roughness so as to help alleviate the potential effects of floods. One of the driving pillars for the technological advancement in flood mitigation and risk management has been cited as the easy availability of most data required for parameterisation and validation of most models. The methods used to acquire the vast majority of data have been significantly developed as a result of technological innovations and creativity in the field of remote sensing (Maas, 1999). Satellite and aircraft remote sensing, allows the involved persons to rapidly collect spatial data over large areas at an extremely reduced cost when compared to ground survey. The double-dimensional synoptic feature of remotely sensed data makes it extremely easy to develop two- and higher-dimensional inundation models, which often depend on 2-Dimensional data for effective parameterisation and validation. Experts involved in the application of tidal and storm surge induction models have constantly insisted on the parameterization of surface roughness as a key component is their practice and professional engagements. To a large extent therefore, surface roughness has been approached as the ability of the terrain of any given terrain to act as a momentum sink to the overland water flowing across the surface as well as the prevailing moving air (wind) that subsequently help drive the flow. Hence, the effects of surface roughness on the overland water and wind are best determine using estimates of manning’s n, surface coverage canopy and effective aerodynamic roughness length of the given locality which at times vary spatially depending on the modeling domain as the pertinent function of the physical landscape. Werner(2004), for instance argues that while land usage and hence cover method may be used to automatically parameterize surface roughness of an extremely large model domain, certain parameter prediction errors emanate due to the differences in types of land cover , misclassification and at times inaccurate estimation of the inundation extent and duration. Surface features and Terrain According to Werner et al. (2004), the propagation of overland inundation is hugely dependent on the roughness of the terrain surface. In fact, roughness of the terrain and topography are considered as the two most important parameters that influence overland flow. Similarly, drag forces exerted on the water and wind flow by the above-ground obstacles in the flood plain such as vegetation cover, shrubs, grasses, trees and other erect structures help in the dissipation of hydraulic energy as well as the momentum of the flood wave in the floodplain. The obstacles or obstructions also help in the modification of wind characteristics which is an extremely important forcing mechanism in the modeling of hurricane storm surges. In most finite scenarios, the above phenomena are parameterized and subsequently implemented in a bottom friction coefficients form such as manning’s n and other coefficients that are used to depict surface canopy closure and overall roughness length. Various studies intended at establishing and improving surface flood plain roughness often consider the bottom friction calibrated model as compared to the use of LiDAR data. Nevertheless, several research studies have also advanced models that seek to elaborately describe the dynamics of the LiDAR data as purely as possible without the use of calibrated or tuned friction characteristics in establishment of the floodplain roughness. Human settlement in the floodplains is not a new concept in geographical studies. Available evidence for instance indicate that for a long time now, people have continuously developed settlements in floodplains and as a result, thousands of others have continued to do so, hence turning events of flooding into extremely dangerous, yet recurrent natural disasters of the modern generation. Flood risk management as well the overall reduction of potential destructive effects have been subsequently dealt with by various emergency management agencies that carry out mapping of flood prone areas in order to come up with Digital Flood Insurance Rate Maps (DFIRMS) for such regions. In 2007, the European Commission approved the implementation of the European Union (EU) Flood Directive of 2007 that requires all the affiliated member states to carry out exhaustive and elaborate risk easements of flooding areas and map potential flood extent so as to facilitate the coordination and other related activities collectively aimed at reducing the risk of floods. According to the European Union (EU) Flood Directive of 2007, topographic data was cited as an inevitable requirement in the entire process of establishment of the precision of flood maps and related models for most inland areas. Similarly, topographic data alone was found to offer some degree of error in flood modelling and hence the need to support such information with additional scientific research findings. LiDAR and Inundation For quite some time now land surface elevation data is usually obtained through use of time-consuming ground surveying techniques which often involve theodolites, the considerations of all established survey stations, the analysis of differential Global Positioning Sensor units, the use of micro profilers, obtaining measurement by using field tapes, making reference to existing and already documented national survey maps, and even analysis of aerial imagery from satellites. In this regard, the final elevation surfaces and surface roughness interpolated from the above methods often leads to some level of uncertainty in surface roughness and elevation values with obtained figures being over 10 times magnified and above the limits set by different environmental management authorities for floodplain mapping. In order to increase precision and hence accuracy, there has been a significant increase in the preference and hence the adoption of airborne light detection and ranging (LiDAR) data for flood for most inundation studies. Most experts acknowledge that the use of airborne light detection and ranging (LiDAR) data offers an advantage of increased efficiency in the obtaining of accurate information related to elevation and ground features present on the landscape. Despite the accurate dataset provided obtained from the airborne LiDAR sensors, most modelers point out that there are numerous challenges associated with the method especially when modeling complex urban environments. The uneven surface as a result of the numerous buildings and other erected structures makes it almost impractical to locate the central point where most data can be read and obtained to provide an accurate representation of urban areas. For instance, surface heterogeneities on the landscape as manifested in the presence of crisscrossing road cambers with pavement curbs and micro-undulations on the surface serve an extremely significant role in the diversion of overland flow and floodwaters and hence any instance inaccurate representation of such features may drastically affect the flood water flow paths (Garlick, 2004). Similarly, urban flood modeling requires that the modeler first resolves the flow of surface water between and around buildings and all other surface structures so as to obtain a representation of accurate topographic features and even the existing blockage effects. The above requirement hence makes it compulsory that the undefined and unending patterns of floodplain depths which often occur as a result of blocking effects of buildings are factored in the final flood map and surface roughness map. It therefore implies that there is a need to engage High-resolution details order to accurately bring out the ground features that characterize most urban landscape. Similarly, such finer scale features are also necessary in the establishment of how the various surface structures determine the nature of existing flood propagation methods. Information obtained from both ground and terrestrial LiDAR systems have subsequently proved to provide an improved the topographic vertical accuracy as compared to the similar elevation data for same locations provided by the airborne systems. LiDAR and Remote Sensing According to Lu (2004), LiDAR is considered as an active remote sensing system whose working mechanism is based on emission of laser beams of light propagated at extremely high speeds. The beams of light are converted into a transmission form known as photodiodes and accelerated towards a certain point on the earth surface. The photodiodes are then used to measure the average duration required by the improving pulse of light to hit the earth surface and to return to the sensor. In this case, the distance between the location of the LiDAR gadget and the various objects on the earth surface is established through multiplication of the resultant speed of the light pulse with the average duration of travel of the beam which often refers to the time lapse when the laser pulse is emitted and received and subsequently getting quotient of the product by two. In modern technology, Global positioning systems (GPS) and Inertial Measuring Units (IMU) are used alongside most LiDAR systems to calculate and establish the precise location of objects on the earth surface. The same technology is also used to explain the trajectory variability which often takes place during the collection of data for floodplain modeling. According to Wehr(1999), the popularity of usage of LiDAR systems has significantly increased as a result of the need to increase efficiency in the d data collection process as well as the emphasis on high point density data. Hence, given the fact that LiDAR works on an extremely high scope of automation, it guarantees significant level of accuracy at an extremely lowered cost. Most Flood inundation studies often revolve around surface mapping and the definition of the area covered by water in case of a flood event. In most cases, this is usually done through the comparison of digital water surface elevations with the initially established and documented digital terrain model (DTM) of the bare-earth elevation which is then followed by an indication of the various parts and regions where the water surface elevation seems to be above the land surface. Alsdorf (2000) further emphasizes that most accurate and precise floodplain models developed using airborne LiDAR data have been found in rural areas where there seems to be prevalent gradual changes in the topography. In fact, continuous usage of airborne LiDAR elevation data in urban areas has often been associated with insufficient details with regard to the representation of the finer scale topographic features. Nevertheless, there seems to be a limited amount of research and hence findings with regard to flooding in mountainous areas as well as on urban locations within mountainous sub-basins. According to Bates (1997), most elevation data obtained from mobile and terrestrial LiDAR systems (MLS and TLS, respectively) have found extensive use in the compensation of inadequate representations of most ground surfaces by airborne LiDAR systems (ALS). Andreadis et al (2007) carried out the first ever known flood inundation analysis using a sub-meter resolution elevation data that was obtained from a mobile LiDAR system in an urban environment. In the representation, the utility of the high resolution dataset was described as the best responses to the various gaps that arise from the limited field-of-view of their vehicle-based LiDAR system as manifested in the production of undesirable artifacts within the floodwater depth grids. Similarly, in most recent studies, the data gaps form a triangulated irregular network (TIN) especially when obtained from a combination of airborne, mobile, and terrestrial LiDAR data sources. The use of airborne data on the periphery of most studies area minimized artifacts which often manifest show up in form of artificial ponding which tend to allow voids to present themselves in the combined mobile and terrestrial dataset to be filled using an overlapping airborne dataset. Further still, the ground-based bare-earth dataset are often used to replace positions within the bare-earth airborne dataset that are often considered obsolete due to recent construction and restoration projects on the surface. While most modelers cite a composite of multi-platform datasets have been tried in the past, most studies tend to only combine two different platform datasets, often merging ALS and TLS, or MLS and TLS. The most precise and hence accurate combination preferred by most experts thus seems to be that of data from three LiDAR platforms, airborne, mobile, and terrestrial which often capitalizes on the complementary technologies to reduce and eradicate data collection weaknesses inherent in the individual systems that make up the composite.The concept of using a composite of multi-platform LiDAR datasets into a single TIN when developing flood models for various ground roughness in an urban environment has been extensively been studied and researched on. For instance, the accuracy provided by the elevation values in the combined methodology dataset when compared to TIN and other elevation data obtained from an airborne LiDAR survey indicates that composites often offer more reliable data. Shortcomings of LiDAR Despite the huge preference from most flood map developers, there are certain shortcomings of using LIDAR data. For instance, one critical challenge when using LIDAR data relates to the numerous the computational requirements necessary to handle such dense datasets. In fact, after successful completion of ground surveys and other field activities, the entire reliability of the outcomes of the whole exercise depends on the level of accuracy in the various computations. One of the requirements of using the method is that the researcher involved must have optimal computational ability in order to achieve reliable results. The synthesis of It is believed that the overall synthesis of airborne, mobile, and terrestrial LiDAR data in a GIS and hydraulic modeling environment turns out as a robust undertaking for accurately representing an urban floodplain and hence, the required flood modeling results emphasizes on the necessity to present such complex data in a manner likely to readily indicate the potential flood inundation locations. The complex computations are even further complicated in instances where there is no established homogeneity in ground surface and therefore different locations have distinct surface roughness. The above scenario is common in most urban regions where buildings are built to different heights and the ground surface is either excavated or filled to achieve a certain ground height as required by the different stakeholders. Conclusion When coming up with a flood map of any given floodplain basing on the surface roughness, there is always the need to consider the basic topographic data of the same place so as to facilitate the production of a requirement is for a Digital Terrain Model (DTM) of a high quality to give a clear representation of both the ground surface as well as an elaborate presentation of all surface objects. As a result, most modellers working on rural floodplain modelling often insists that the DTM is produced within a vertical accuracy of about 0.5m and alongside a corresponding spatial resolution of not less than 10m. although it may be argued out that level accuracy and special scale data are not sufficient enough to present an illustrative micro-topography of most relict channels found on rough surfaces as well as the resultant drainage ditches that characterize most floodplains, most floodplain maps indicate that the usage higher flood depths inundation alongside a larger scale valley morphology can easily lead to a less critical and more detailed micro-topography. Further on, other significant characteristics of floodplains such as embankments and overbank flow control levees also require significantly higher accuracy and spatial scale to become more pronounced for analysis. In this case a vertical scale of approximately 10cm and a spatial resolution of about 2m have been found to produce the required size of such features. (Smith et al., 2006). In other instances, modellers might also be forced to establish a variety of ground features to enhance accuracy and hence the use of separate Geographic Information System (GIS) layers in determination of distributed floodplain roughness coefficients. REFERENCES Alsdorf, D. E., Melack and J.M., Dunne, T. (2000). Interferometric radar measurements of water level changes on the Amazon flood plain. Nature, 404, 174-177. Alsdorf, D. E., Smith, L.C. and Melack, J.M. (2001). Amazon floodplain water level changes measured with interferometric SIR-C Radar. IEEE Transactions on Geoscience and Remote Sensing 39(2), 423-431. Andreadis, K. M., Clark, E. A., Lettenmaier, D. P. and Alsdorf, D. E. (2007). Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophysical Research Letters 34. doi:10.1029/2007GL029721. Bates, P. D., Horritt, M. S., Smith, C. N. and Mason, D. C. (1997). Integrating remote sensing observations of flood hydrology and hydraulic modelling. Hydrological Processes 11, 1777–1795. Bates, P.D. (2000), Development and testing of a subgrid-scale model for moving-boundary hydrodynamic problems in shallow water. Hydrological Processes, 14 (11-12), 2073-2088. Bates, P.D., Marks, K.J. and Horritt, M.S. (2003), Optimal use of high-resolution topographic data in flood inundation models. Hydrological Processes, 17, pp. 537-557. Bates, P.D., Wilson, M.D., Horritt, M.S., Mason, D., Holden, N., and Currie, A. (2006), Reach scale floodplain inundation dynamics observed using airborne Synthetic Aperture Radar imagery: data analysis and modeling. Journal of Hydrology, 328, 306-318. Beven, K. and Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6, 279–298. Beven, K. (2006). A manifesto for the equifinality thesis. Journal of Hydrology 320, 18–36. Birkett, C.M., Mertes, L.A.K., Dunne, T., Costa, M.H. and Jasinski, M.J. (2002). Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry. Journal of Geophysical Research, 107, doi:10.1029/2001JD000609. Frappart, F., Calmant, S., Cauhope, M., Seyler, F. and Cazenave, A. (2006). Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote Sensing of Environment 100, 252–264. Garlick, J.D., Berry, P.A.M., Mathers, E.L. and Benveniste, J. (2004). The ENVISAT n ERS river and lake retracking system. In Proceedings of the 2004 ENVISAT and ERS Symposium, September 6-10, Salzburg, Austria. Gomes Pereira, L.M.G. and Wicherson, R.J. (1999), Suitability of laser data for deriving geographical information: a case study in the context of management of fluvial zones. ISPRS J. Photogramm. Rem. Sens. 54(2-3), 104-114. Lu, D., Mausel, P., Brondizio, E. and Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing 25 (12), 2365–2407. Maas, H-G. and Vosselman, G. (1999), Two algorithms for extracting building models from raw laser altimetry data. ISPRS J. Photogramm. Rem. Sens., 54(2/3), 153-163. Wehr, A. and Lohr, U. (1999), Airborne Laser Scanning: An Introduction and Overview. ISPRS Journal of Photogrammetry & Remote Sensing, vol. 54, pp. 68-82. Werner, M., Blazkova, S. and Petr, J. (2005). Spatially distributed observations in constraining inundation modelling uncertainties. Hydrological Processes 19 (16), 3081–3096. APPENDIX Figure 1. Typical LiDAR system and its main components (after Smith et al., 2006). Figure 2. Mesh constructed over vegetated urban area (red = mesh, blue = building/taller vegetation heights; a river is present in the NE) (after Mason et al., 2007). Figure 3. 3m-resolution TerraSAR-X image of flooding in Tewkesbury in July 2007 (© DLR (2007)) (dark regions are water and radar shadow areas). Figure 5. River Danube water level fluctuations from 1993 to 2002. Source: http://www.legos.obs-mip.fr/en/equipes/gohs/resultats/i_hydroweb Read More

Experts involved in the application of tidal and storm surge induction models have constantly insisted on the parameterization of surface roughness as a key component is their practice and professional engagements. To a large extent therefore, surface roughness has been approached as the ability of the terrain of any given terrain to act as a momentum sink to the overland water flowing across the surface as well as the prevailing moving air (wind) that subsequently help drive the flow. Hence, the effects of surface roughness on the overland water and wind are best determine using estimates of manning’s n, surface coverage canopy and effective aerodynamic roughness length of the given locality which at times vary spatially depending on the modeling domain as the pertinent function of the physical landscape.

Werner(2004), for instance argues that while land usage and hence cover method may be used to automatically parameterize surface roughness of an extremely large model domain, certain parameter prediction errors emanate due to the differences in types of land cover , misclassification and at times inaccurate estimation of the inundation extent and duration. Surface features and Terrain According to Werner et al. (2004), the propagation of overland inundation is hugely dependent on the roughness of the terrain surface.

In fact, roughness of the terrain and topography are considered as the two most important parameters that influence overland flow. Similarly, drag forces exerted on the water and wind flow by the above-ground obstacles in the flood plain such as vegetation cover, shrubs, grasses, trees and other erect structures help in the dissipation of hydraulic energy as well as the momentum of the flood wave in the floodplain. The obstacles or obstructions also help in the modification of wind characteristics which is an extremely important forcing mechanism in the modeling of hurricane storm surges.

In most finite scenarios, the above phenomena are parameterized and subsequently implemented in a bottom friction coefficients form such as manning’s n and other coefficients that are used to depict surface canopy closure and overall roughness length. Various studies intended at establishing and improving surface flood plain roughness often consider the bottom friction calibrated model as compared to the use of LiDAR data. Nevertheless, several research studies have also advanced models that seek to elaborately describe the dynamics of the LiDAR data as purely as possible without the use of calibrated or tuned friction characteristics in establishment of the floodplain roughness.

Human settlement in the floodplains is not a new concept in geographical studies. Available evidence for instance indicate that for a long time now, people have continuously developed settlements in floodplains and as a result, thousands of others have continued to do so, hence turning events of flooding into extremely dangerous, yet recurrent natural disasters of the modern generation. Flood risk management as well the overall reduction of potential destructive effects have been subsequently dealt with by various emergency management agencies that carry out mapping of flood prone areas in order to come up with Digital Flood Insurance Rate Maps (DFIRMS) for such regions.

In 2007, the European Commission approved the implementation of the European Union (EU) Flood Directive of 2007 that requires all the affiliated member states to carry out exhaustive and elaborate risk easements of flooding areas and map potential flood extent so as to facilitate the coordination and other related activities collectively aimed at reducing the risk of floods. According to the European Union (EU) Flood Directive of 2007, topographic data was cited as an inevitable requirement in the entire process of establishment of the precision of flood maps and related models for most inland areas.

Similarly, topographic data alone was found to offer some degree of error in flood modelling and hence the need to support such information with additional scientific research findings.

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