Wet channel network extraction by integrating LiDAR intensity and elevation data

Hooshyar, Milad; Kim, Seo Young; Wang, Dingbao; Medeiros, Stephen C.

doi:10.1002/2015wr018021

Cited by 49 publications

(45 citation statements)

References 58 publications

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“…One exception is high‐resolution near‐infrared LiDAR, which Hooshyar et al . () employed to extract the wet stream network in a region with exposed channels. Even at sites without dense canopy cover, multiple collections of aerial photographs or LiDAR is necessary during both wet and dry conditions to accurately describe stream length variability.…”

Section: Introductionmentioning

confidence: 99%

Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands

Jensen

McGuire

Shao

et al. 2018

Earth Surf Processes Landf

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Despite the advancement of remote sensing and geospatial technology in recent decades, maps of headwater streams continue to have high uncertainty and fail to adequately characterize temporary streams that expand and contract in the wet length. However, watershed management and policy increasingly require information regarding the spatial and temporal variability of flow along streams. We used extensive field data on wet stream length at different flows to create logistic regression models of stream network dynamics for four physiographic provinces of the Appalachian Highlands: New England, Appalachian Plateau, Valley and Ridge, and Blue Ridge. The topographic wetness index (TWI) was the most important parameter in all four models, and the topographic position index (TPI) further improved model performance in the Appalachian Plateau, Valley and Ridge, and Blue Ridge. We included stream runoff at the catchment outlet as a model predictor to represent the wetness state of the catchment, but adjustment of the probability threshold defining wet stream presence/absence to high values for low flows was the primary mechanism for approximating network extent at multiple flow conditions. Classification accuracy was high overall (> 0.90), and McFadden's pseudo R2 values ranged from 0.69 for the New England model to 0.79 in the Appalachian Plateau. More notable errors included an overestimation of wet stream length in wide valleys and inaccurate reach locations amid boulder deposits and along headwardly eroding tributaries. Logistic regression was generally successful for modeling headwater streams at high and low flows with only a few simple terrain metrics. Modification and application of this modeling approach to other regions or larger areas would be relatively easy and provide a more accurate portrayal of temporary headwaters than existing datasets. © 2018 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands

Jensen

McGuire

Shao

et al. 2018

Earth Surf Processes Landf

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“…For example, LiDAR observations can be used to identify the interactions of hydrological and ecological processes [ Harpold , ]. LiDAR has also been used to detect wet channel distribution [ Hooshyar et al , ] and vegetation structure [ Dubayah et al , ; Saito et al , ; Simard et al , ; Tao et al , ]. However, the CZ extends to depth [ Guo and Lin , ; Richter and Mobley , ], and subsurface characterization is challenging.…”

Section: Introductionmentioning

confidence: 99%

The global distribution of Earth's critical zone and its controlling factors

Liu

2017

Geophysical Research Letters

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The near‐surface layer of Earth which provides essential elements for supporting life is now recognized as the critical zone (CZ). This study provides the first global assessment of the CZ thickness (CZT) and its controlling factors by combining data sets of climate, vegetation height (VH), water table depth (WTD), groundwater thickness (GWT), topography, and lithologic data. The analysis shows that CZT ranges from 0.7 to 223.5 m with an average value of 36.8 m across continental areas; CZT is thickest in midlatitudes (subtropical to temperate zones). The proportion of aboveground part (VH) to CZT is 19.9 ± 16.7% (mean ±one standard deviation), while it is 80.1 ± 16.7% for the underground part (WTD + GWT). A generalized linear model shows that compound topographic index (ln(a/tan(b)), where a is the upslope contributing area and b is the slope degree of the landscape) and potential evapotranspiration are the first two major controlling factors on the variations in CZT. This study opens opportunities for further advancing CZ science by providing one of its most important properties—its thickness.

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“…Hooshyar et al [22] delineated wet channels using LiDAR intensity and elevation data. The vegetation canopy was first filtered out by applying a threshold value on elevations, and a digital elevation model (DEM) was created.…”

Section: Of 21mentioning

confidence: 99%

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

2018

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Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevationand geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.

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Wet channel network extraction by integrating LiDAR intensity and elevation data

Cited by 49 publications

References 58 publications

Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands

Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands

The global distribution of Earth's critical zone and its controlling factors

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

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