Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

Johansen, Kasper; Grove, James; Denham, Robert

doi:10.1117/1.jrs.7.073492

Cited by 13 publications

(7 citation statements)

References 53 publications

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“…The accuracy of the channel delineation may significantly affect the resulting inundation extent. Innovative approaches for automatic channel extraction from lidar data have been developed (e.g., Johansen et al, ; Orlandini et al, ; Passalacqua et al, ; Pelletier, ; Sangireddy et al, ), but these approaches have not been coupled with inundation mapping techniques. Here we further develop the approach of Zheng et al () for its application to high‐resolution terrain data.…”

Section: Introductionmentioning

confidence: 99%

GeoFlood: Large‐Scale Flood Inundation Mapping Based on High‐Resolution Terrain Analysis

Zheng

Maidment

Tarboton

et al. 2018

Water Resources Research

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Recent floods from intense storms in the southern United States and the unusually active 2017Atlantic hurricane season have highlighted the need for real-time flood inundation mapping using high-resolution topography. High-resolution topographic data derived from lidar technology reveal unprecedented topographic details and are increasingly available, providing extremely valuable information for improving inundation mapping accuracy. The enrichment of terrain details from these data sets, however, also brings challenges to the application of many classic approaches designed for lower-resolution data. Advanced methods need to be developed to better use lidar-derived terrain data for inundation mapping. We present a new workflow, GeoFlood, for flood inundation mapping using high-resolution terrain inputs that is simple and computationally efficient, thus serving the needs of emergency responders to rapidly identify possibly flooded locations. First, GeoNet, a method for automatic channel network extraction from high-resolution topographic data, is modified to produce a low-density, high-fidelity river network. Then, a Height Above Nearest Drainage (HAND) raster is computed to quantify the elevation difference between each land surface cell and the stream bed cell to which it drains, using the network extracted from high-resolution terrain data. This HAND raster is then used to compute reach-average channel hydraulic parameters and synthetic stage-discharge rating curves. Inundation maps are generated from the HAND raster by obtaining a water depth for a given flood discharge from the synthetic rating curve. We evaluate our approach by applying it in the Onion Creek Watershed in Central Texas, comparing the inundation extent results to Federal Emergency Management Agency 100-yr floodplains obtained with detailed local hydraulic studies. We show that the inundation extent produced by GeoFlood overlaps with 60%~90% of the Federal Emergency Management Agency floodplain coverage demonstrating that it is able to capture the general inundation patterns and shows significant potential for informing real-time flood disaster preparedness and response.Plain Language Summary Simple and computationally efficient flood inundation mapping methods are needed to take advantage of increasingly available high-resolution topography data. In this work, we present a new approach, called GeoFlood, for flood inundation mapping using high-resolution topographic data. This approach combines GeoNet, an advanced method for high-resolution terrain data analysis, and the Height Above Nearest Drainage. GeoFlood can rapidly convert real-time forecasted river flow conditions to corresponding flood maps. A case study in central Texas demonstrated that the flood maps generated with our approach capture the majority of the inundated extent reported by detailed Federal Emergency Management Agency flood studies. Our results show that GeoFlood is a valuable solution for rapid inundation mapping. ZHENG ET AL.10,013

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Section: Introductionmentioning

confidence: 99%

GeoFlood: Large‐Scale Flood Inundation Mapping Based on High‐Resolution Terrain Analysis

Zheng

Maidment

Tarboton

et al. 2018

Water Resources Research

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“…; Johansen et al. ). Once developed, kayak‐based LiDAR may provide a more accurate on‐the‐go measurement of bank height and bank angle.…”

Section: Resultsmentioning

confidence: 99%

“…Because this method has limited accuracy when compared to field site visits, the SVMS is most beneficial for studies focused on erosion hotspot identification and TSL estimation for an entire river system. Advances in technology has spurred the use of remotely sensed data such as airborne and terrestrial Light Detection and Ranging (LiDAR) for assessing streambank condition (Faux et al 2009;Johansen et al 2013). Once developed, kayak-based LiDAR may provide a more accurate on-the-go measurement of bank height and bank angle.…”

Section: Applications For An Integrated Methodsmentioning

confidence: 99%

Estimating Streambank Erosion Rates with a GPS‐Based Video Mapping System

Hensley

Ayers

2018

J American Water Resour Assoc

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Streambank erosion provides a direct supply of sediment to fluvial systems and significantly contributes to the total load in sediment impaired streams. The total amount and location of excessive streambank erosion must be determined to reduce the sediment supply and improve water quality within these impaired water bodies. The objective of our study was to integrate the Bank Assessment for Non‐point source Consequences of Sediment (BANCS) model with the Streambank Video Mapping System, a global positioning system‐based landscape‐scale data collection method, to estimate the total sediment load (SL) from streambank erosion over several river kilometers. This integrated methodology allowed us to identify erosion hotspots along a 20.4 km section of Upatoi Creek in Georgia. These streambank locations would be the highest priority sites for stream restoration efforts such as streambank stabilization, reestablishment of riparian vegetation, and floodplain reconnection. Our study reach had a SL per unit channel length (109 t/yr/km) comparable in magnitude to loading rates reported in the literature from other traditional BANCS assessments.

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“…Heeren et al [240] used the time-series of RGB-data (2003)(2004)(2005)(2006)(2007)(2008) for the monitoring and assessment of various geomorphic traits of stream bank retreat. A combination of terrestrial and airborne LiDAR with high spatial resolution RS-RGB data are crucial RS technologies for monitoring and assessing stream bank conditions [168,174,241]. UAV-based laser scanning in combination with other sensor technologies have also been used increasingly more for monitoring and modeling riverscape morphometric and vegetation traits [242][243][244].…”

Section: Flood Hazardmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

Cited by 13 publications

References 53 publications

GeoFlood: Large‐Scale Flood Inundation Mapping Based on High‐Resolution Terrain Analysis

GeoFlood: Large‐Scale Flood Inundation Mapping Based on High‐Resolution Terrain Analysis

Estimating Streambank Erosion Rates with a GPS‐Based Video Mapping System

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

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