USGS lidar science strategy—Mapping the technology to the science

Stoker, Jason M.; Brock, John C.; Soulard, Christopher E.; Ries, Kernell G.; Sugarbaker, Larry J.; Newton, Wesley E.; Haggerty, Patricia K.; Lee, Kathy E.; Young, John A.

doi:10.3133/ofr20151209

Cited by 6 publications

(5 citation statements)

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“…The application of LiDAR in supporting many science research activities such as geologic mapping, landslide hazards, and flood risk management cannot be disputed [43]. The number of publications of peer-reviewed research literature recorded in the Scopus database for the past 10 years, from 2010 to 2019, which discussed LiDAR data in flood studies, was determined.…”

Section: Applications Of Lidar System In Flood Monitoringmentioning

confidence: 99%

The Use of LiDAR-Derived DEM in Flood Applications: A Review

et al. 2020

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Flood occurrence is increasing due to escalated urbanization and extreme climate change; hence, various studies on this issue and methods of flood monitoring and mapping are also increasing to reduce the severe impacts of flood disasters. The advancement of current technologies such as light detection and ranging (LiDAR) systems facilitated and improved flood applications. In a LiDAR system, a laser emits light that travels to the ground and reflects off objects like buildings and trees. The reflected light energy returns to the sensor, whereby the time interval is recorded. Since the conventional methods cannot produce high-resolution digital elevation model (DEM) data, which results in low accuracy of flood simulation results, LiDAR data are extensively used as an alternative. This review aims to study the potential and the applications of LiDAR-derived DEM in flood studies. It also provides insight into the operating principles of different LiDAR systems, system components, and advantages and disadvantages of each system. This paper discusses several topics relevant to flood studies from a LiDAR-derived DEM perspective. Furthermore, the challenges and future perspectives regarding DEM LiDAR data for flood mapping and assessment are also reviewed. This study demonstrates that LiDAR-derived data are useful in flood risk management, especially in the future assessment of flood-related problems.

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Section: Applications Of Lidar System In Flood Monitoringmentioning

confidence: 99%

The Use of LiDAR-Derived DEM in Flood Applications: A Review

et al. 2020

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“…Newer constellations of “dove” satellites which form “flocks” and record the entire global surface at weekly to daily time scale solve a temporal limitation to remote sensing at scale, with nominal spatial resolution (1–3 m) similar to aerial orthophotography (Butler, 2014 ; McCabe et al, 2016 ; Zimmerman et al, 2017 ). In the United States, nationally available lidar data sets (Stoker et al, 2016 ) provide bare earth models at 1–2 m resolution. Our methods show how these data could be used for more accurately measuring dense vegetation cover, height, and volume from sUAS derived lidar or SfM photogrammetry data and scaling out spatially with satellite products.…”

Section: Future Outlookmentioning

confidence: 99%

“…Light detection and ranging (lidar) is the predominant technology for measuring vegetation and earth surface phenomena in three dimensions (3D) (Glennie et al, 2013 ; Harpold et al, 2015 ). Manned aircraft equipped with lidar now survey local areas to entire ecosystems or countries (Higgins et al, 2014 ; Stoker et al, 2016 ), while terrestrial lidar is used to collect data at sub-centimeter resolution over hectare size areas. Critically, the spatial scale needed for managing at landscape scale can be provided by manned aerial lidar but the scale needed for monitoring ecosystem process often requires resolution that can only be provided by terrestrial lidar.…”

Section: Introductionmentioning

confidence: 99%

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

et al. 2018

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Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.

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“…IfSAR, on the other hand, is an active RS technology generally used in places like Alaska, where cloud cover and the remote locations of target areas make the use of lidar less effective and relatively impractical. Radar from satellites can penetrate overcast weather and provides valuable continued round-the-clock imagery during storm events [29]. This active sensing technique combines two or more synthetic aperture radar (SAR) images that are derived from recording the stereoscopic effect caused by the differences in the phases of radiation waves that return from the target area after it is struck by a narrow radar beam transmitted from an antenna on a satellite platformbased sensor.…”

Section: Flood Mappingmentioning

confidence: 99%

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

Petty¹,

Noman²,

Ding³

et al. 2016

JGIS

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Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level; 2) integrate data and model analysis results from multiple sources; 3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams; and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE's inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.

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USGS lidar science strategy—Mapping the technology to the science

Cited by 6 publications

References 0 publications

The Use of LiDAR-Derived DEM in Flood Applications: A Review

The Use of LiDAR-Derived DEM in Flood Applications: A Review

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

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