1. Introduction
Humans have traditionally developed settlements in floodplains and continue to do so, making flood events one of the most consistent and recurring natural disasters experienced by human populations [
1,
2,
3]. The management of flood risk and the reduction of potential damages in the United States (U.S.) have been addressed by the Federal Emergency Management Agency (FEMA) through mapping flood hazard areas and the generation of Digital Flood Insurance Rate Maps (DFIRMS). The European Commission adopted the European Union (EU) Flood Directive in 2007 [
4]. The Directive requires member states to assess the risk of flooding, to map potential flood extent, and to coordinate efforts to reduce flood risk [
4]. The National Research Council (NRC) in the U.S. concluded that topographic data is the most important factor in determining the accuracy of flood maps for inland areas [
2]. The primacy of topographic data in flood modeling is supported by additional scientific research [
5,
6]. Traditionally, land surface elevation data has been collected using time-consuming ground surveying techniques consisting of total stations, differential GPS units, field tapes, theodolite, micro profilers, national survey maps, and stereoscopic analysis of aerial imagery [
7,
8,
9]. The elevation surfaces interpolated from the data collected using these methods can produce uncertainty in elevation values up to ten times greater than the acceptable limit set by FEMA for floodplain mapping [
2]. Recently, a rapid increase in the adoption of airborne light detection and ranging (LiDAR) data for flood inundation studies has occurred due to the efficient collection of accurate elevation data for features within the landscape.
Although the use of topographic data acquired from airborne LiDAR sensors has provided modelers with a more accurate dataset, challenges still exist when modeling complex urban environments. Urban flood modeling entails resolving surface water movement around buildings and representation of fine scale topographic and blockage effects [
10]. Irregular and discontinuous patterns of floodplain depths are caused by the blocking effect of buildings [
11]. Heterogeneities within the land surface such as road cambers, pavement curbs, and minute undulations in the topography play a substantial role in diverting overland flow and floodwaters and the lack of accurate representation of these features can drastically alter flood water flow paths [
12,
13,
14]. High-resolution data is necessary to represent finer scale features in an urban landscape and to investigate how these features affect flood propagation. Elevation data acquired by ground-based mobile and terrestrial LiDAR systems have proven advantageous for representing the landscape at a finer resolution, and for improving the vertical accuracy of topographic feature representation as compared to the elevation data acquired by airborne systems [
15,
16,
17].
LiDAR is an active remote sensing system which operates by emitting laser pulses of light at high frequencies towards the Earth’s surface as a photodiode measures the time it takes for the pulse to return from the surface to the sensor [
7,
8]. The distance to an object is then calculated by multiplying the speed of light by the time elapsed between when the laser pulse is emitted and received and dividing the product by two [
18]. Global positioning systems (GPS) and inertial measuring units (IMU) are linked to most LiDAR systems to determine location and account for trajectory variability that occurs during the collection process. The popularity of using LiDAR systems has increased due to their rapid data collection capability, high degree of automation, high point density, high level of accuracy, and cost efficiency. These characteristics led the NRC to recommend the creation of a nation-wide elevation dataset based upon LiDAR data in order to enhance floodplain mapping accuracy [
19,
20].
Flood inundation studies are based on mapping and defining an area covered by water during a flood event. This is typically done by comparing digital water surface elevations with the digital terrain model (DTM) of the bare-earth elevation and indicating where the water surface elevation is above the land surface [
21]. Successful floodplain models using airborne LiDAR data have been reported in rural areas where gradual changes in the topography are prevalent (e.g., [
22,
23,
24,
25,
26]). Alternatively, the use of airborne LiDAR elevation data in an urban environment has been shown to be insufficient with respect to the accurate representation of the finer scale topographic features (e.g., [
13,
14,
27,
28,
29]). A limited amount of research has been related to flooding in mountainous areas and the work that has been conducted has utilized airborne LiDAR data (e.g., [
30,
31]). Even fewer research efforts have been undertaken in urban locations within mountainous sub-basins (e.g., [
12]).
Elevation data from mobile and terrestrial LiDAR systems (MLS and TLS, respectively) have been utilized to compensate for inadequate representation of ground surfaces by airborne LiDAR systems (ALS). Fewtrell
et al. [
14] conducted one of the first flood inundation analyses based on sub-meter resolution elevation data acquired using a mobile LiDAR system in an urban environment and described the utility of such a high resolution dataset. They concluded that gaps found in their dataset were due to the limited field-of-view of their vehicle-based LiDAR system which produced a variety of undesirable artifacts within the floodwater depth grids (
i.e., ponding near the data voids). In the current study to compensate for potential data gaps a triangulated irregular network (TIN) based on a composite of airborne, mobile, and terrestrial LiDAR data was generated. Utilizing the airborne data on the periphery of the study area minimized artifacts, such as artificial ponding, and allowed the voids present in the combined mobile and terrestrial (or ground-based) dataset to be filled using an overlapping airborne dataset. In addition, the ground-based bare-earth dataset was used to replace locations within the bare-earth airborne dataset that had become obsolete due to recent construction and restoration projects. While combinations of multi-platform datasets have been attempted in the past (e.g., [
9,
32,
33,
34]), these studies only combined two different platform datasets, typically merging ALS and TLS, or MLS and TLS [
35]. To the author’s knowledge, the research presented in this paper is unique in that a combination of data from three LiDAR platforms (
i.e., airborne, mobile, and terrestrial) was created, therefore capitalizing on the complementary technologies and reducing the data collection weaknesses inherent in each individual system.
The methodology for combining multi-platform LiDAR datasets into a single TIN for flood modeling in an urban environment was explored in this research. The accuracy of the elevation values in this composite dataset was quantitatively compared to a TIN created solely from elevation data acquired from an airborne LiDAR survey. The composite and airborne TINs were then used to generate independent flood modeling results and compared using a one-dimensional (1D) steady flow analysis implemented in the hydraulic model HEC-RAS (US Army Corp of Engineers; Davis, CA, USA). The generation of geospatial geometric data for flood modeling and the representation of flooding extent and depth were undertaken using the geographic information system (GIS) ArcGIS (Environmental Systems Research Institute; Redlands, CA, USA), and the ArcGIS hydraulic model extension HEC-GeoRAS (US Army Corp of Engineers; Davis, CA, USA).
To ensure an accurate representation of the study area, surveying of manmade features that intersected the study stream reach was accomplished through the measurement of structures within the all-return mobile and terrestrial LiDAR point clouds. Bridge and culvert information is essential in creating accurate high resolution flood models as they can control variability in velocity, stage height, and flood extents [
36,
37]; minimizing or estimating their parameters reduces the accuracy of the flood model results. Given the accuracy of the point cloud data, the resultant structural measurements can be considered as accurate as field-grade survey information [
38], although a structure accuracy assessment was not completed for this study.
From 1983 to 2003, the state of North Carolina ranked ninth in terms of the highest total flood damages and twelfth in terms of the highest damages per capita in the United States [
39,
40]. In the Blue Ridge Mountain Province of the Southern Appalachian Mountains in North Carolina, locally intense, short duration precipitation events coupled with the built environment have produced numerous flash floods substantiating the need to better understand local flooding. Appalachian State University in Boone, North Carolina, USA is located in this province and Boone Creek, part of the Upper South Fork of the New River (USFNR) watershed, runs directly through campus. In November 2011, Boone Creek experienced a large amount of precipitation within a few hours causing a flow event that exceeded the bank-full capacity of the channel and consequently flooded several buildings on campus. Due to this and similar preceding events, this reach of Boone Creek was selected as the study area for this research.
In this research, the utility of a high resolution ground-based LiDAR dataset supplemented with an airborne LiDAR dataset for a flood inundation study in a hydraulic modeling and GIS environment was evaluated. The techniques for combining multi-platform LiDAR datasets were described and illustrated. The final composite dataset in the form of a TIN was quantitatively compared to a TIN generated solely from the airborne dataset. The all-return mobile and terrestrial LiDAR point clouds were used to extract structural information from features intersecting the study stream reach. Utilizing both the airborne and composite datasets, flood inundation analyses were completed and the resultant water surface profiles and depth grids were quantitatively compared. Challenges presented in this research included the complexity of attempting to accurately model an urban stream located in a mountainous headwater sub-basin.
4. Conclusions and Future Research
In this paper, the utility of a high resolution ground-based LiDAR terrain dataset supplemented with a medium to low resolution airborne LiDAR terrain dataset for a flood inundation study in a GIS and hydraulic modeling environment was presented. Multi-platform LiDAR data consisting of airborne, mobile, and terrestrial bare-earth points were merged into one composite triangulated irregular network (TIN) to form a seamless representation of the study stream reach and adjacent terrain. A separate TIN consisting solely of bare-earth airborne LiDAR data was also generated, and a comprehensive quantitative comparison was made between the elevation values of the composite and airborne LiDAR datasets. A flood inundation analysis was conducted utilizing both the composite and airborne LiDAR datasets. The all-return ground-based LiDAR point clouds were used to obtain structural information for bridges and culverts that intersected the study stream reach enabling a more accurate model of features in the study area. The flood modeling results (i.e., water surface extents and depth grids) were quantitatively compared using several diagnostic methods.
A comparison of the elevation values in the two LiDAR datasets indicated differences in landscape characterization. When comparing values across three land surface features (pavement, grass, and slope), the greatest differences were found on sloping terrain. A mean difference of 0.178 m and a maximum difference of 1.677 m were calculated between the datasets based on 120 sample points randomly distributed throughout the study area. An evaluation of 12 cross-section profiles drawn perpendicular to the stream illustrated approximate horizontal (e.g., 3.2 m) and vertical (e.g., 1 m) offsets of the stream centerline.
Through the combination of multi-platform LiDAR data and its use in a hydraulic flood modeling analysis, a 35% increase in maximum flood height using the composite LiDAR dataset compared to the airborne LiDAR dataset was observed. The distances flooded along transects drawn perpendicular to the stream were found to be statistically significantly different between the water surface profiles generated using the composite and airborne terrain datasets. Additionally, a 17.76% symmetric difference value was calculated indicating a notable difference in the area and shape of the two water surface profiles. The results of this research indicated an underestimation of flood extents and volumes while using the airborne LiDAR data.
While flood modeling may generally be accurate in rural or homogenous areas using the airborne LiDAR data, the effects of complex terrain and features such as buildings and infrastructure that affect floodwater direction and flow in more urban environments could create inaccuracies while generating flood maps. The unique “flashy” character with potentially high flow volumes of the mountainous sub-basin in which this study occurred make an accurate representation of the topography for flood modeling more important. The addition of high resolution data from ground-based LiDAR sensors can supplement existing airborne data in topographically complex or sensitive urban areas to increase accuracy of the flood level predictions which will assist in better informing local populations of their potential risks.
An important challenge in using LiDAR data is the computational requirements necessary to handle such dense datasets. The workflow presented in this study provides researchers with less than optimal computational power a method to achieve reliable results. It is believed that the methods presented in this paper, namely the synthesis of airborne, mobile, and terrestrial LiDAR data in a GIS and hydraulic modeling environment, provide a robust method for accurately representing an urban floodplain and the subsequent flood modeling results dictate the need to better represent such a complex environment to indicate potential flood inundation locations. Further exploration of the application of mobile and terrestrial LiDAR data for urban flood modeling in a mountainous environment is warranted.
The airborne LiDAR data used in this study represents the first statewide available dataset acquired in the U.S., and the previous and continued application of this data has greatly benefited residents and businesses in North Carolina. Higher accuracy airborne LiDAR datasets have since been acquired in other states, and a comparison between these datasets or a QL 2 LiDAR dataset as recommended by the NEEA, and a composite LiDAR dataset similar to that used in this study for urban flood modeling would be valuable. Also, flood modeling using only high resolution LiDAR datasets would likely benefit from further analysis using 1D/2D or 2D hydraulic models.