Mapping detention basins and deriving their spatial attributes from airborne LiDAR data for hydrological applications

Liu, Hongxing; Wang, Lei

doi:10.1002/hyp.6834

Cited by 27 publications

(39 citation statements)

References 27 publications

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“…In the group of NLS grid DTMs, the number of depressions was the smallest in DTM10 and the largest in ALS-DTM2. Thus, depressions per km 2 and number of depressions per one pixel of SBA were smallest in DTM10. In the group of ALS-DTMs, the amount of depressions per one pixel of SBA increased owing to resampling process, whereas the number of depressions decreased with increasing grid size.…”

Section: Depression Variables In Dtms Studiedmentioning

confidence: 91%

“…Our study areas are the Lehmäjoki River (166 km 2 ), the Nenättömänluoma River (107 km 2 ) and the upper reaches of the Kainastonjoki River (87 km 2 ) whose watersheds are sub-basin areas (SBAs) of the Kyrönjoki River watershed (Figure 3). The Kyrönjoki River watershed is located in the western part of Finland and its main river bed drains into the Gulf of Bothnia.…”

Section: Study Areasmentioning

confidence: 99%

“…Laser scanning in particular has enabled more accurate data gathering with decreased horizontal and vertical error and better availability of detailed spatial data. For example, airborne laser scanning (ALS) [1][2][3][4][5], ALS systems for bathymetric measurements [6], fixed-position terrestrial laser scanning (TLS) and mobile laser scanning (MLS), such as boat-and cart-based mobile mapping systems (BoMMS/CartMMS) [7,8], have shown new potential in fluvial research.…”

Section: Introductionmentioning

confidence: 99%

“…The availability of more accurate DTMs that contain large amounts of depressions owed to LiDAR (Light Detection and Ranging) technology also underlines the need for a classifier. For example, Liu and Wang [2] classified modelled depressions from high-resolution ALS-DTM based on their spatial variables. Zandbergen [32] focused on the effects of vertical accuracies of DTMs on the probability of modelled depressions being actual landscape features.…”

Section: Introductionmentioning

confidence: 99%

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Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Vesakoski

Alho

Hyyppä

et al. 2014

Water

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Topographic depressions have an important role in hydrological processes as they affect the water balance and runoff response of a watershed. Nevertheless, research has focused in detail neither on the effects of acquisition and processing methods nor on the effects of resolution of nationwide grid digital terrain models (DTMs) on topographic depressions or the hydrological impacts of depressions. Here, we quantify the variation of hydrological depression variables between DTMs with different acquisition methods, processing methods and grid sizes based on nationwide 25 m × 25 m and 10 m × 10 m DTMs and 2 m × 2 m ALS-DTM in Finland. The variables considered are the mean depth of the depression, the number of its pixels, and its area and volume. Shallow and single-pixel depressions and the effect of mean filtering on ALS-DTM were also studied. Quantitative methods and error models were employed. In our study, the depression variables were dependent on the scale, area and acquisition method. When the depths of depression pixels were compared with the most accurate DTM, the maximum errors were OPEN ACCESSWater 2014, 6 272 found to create the largest differences between DTMs and hence dominated the amount and statistical distribution of the depth error. On the whole, the ability of a DTM to accurately represent depressions varied uniquely according to each depression, although DTMs also displayed certain typical characteristics. Thus, a DTM's higher resolution is no guarantee of a more accurate representation of topographic depressions, even though acquisition and processing methods have an important bearing on the accuracy.

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Section: Depression Variables In Dtms Studiedmentioning

confidence: 91%

Section: Study Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Vesakoski

Alho

Hyyppä

et al. 2014

Water

View full text Add to dashboard Cite

show abstract

“…DEM smoothing is desirable since it produces a more realistic surface while retaining detailed topographic characteristics of the landscape [33]. The median operator is an edge-preserving filter [34] that is used to remove data noise and suppress small artifact depressions without distorting the boundaries of true surface depressions, and is considered better than a mean (averaging) filter [35,36].…”

Section: -M Lidar Demmentioning

confidence: 99%

An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

Lane

Liu

2014

Remote Sensing

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Effective conservation of woodland vernal pools-important components of regional amphibian diversity and ecosystem services-depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km 2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool OPEN ACCESS Remote Sens. 2014, 6 11445 locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts.

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Modelling detention basins measured from high‐resolution light detection and ranging data

Wang

2012

Hydrological Processes

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Abstract:The construction of stormwater detention basins is a best management practice to effectively control floods, to provide additional surface storage for excess floodwater and to compensate for the adverse effects of urban development. Traditional field-based levelling survey methods are very time consuming and subject to human-induced arbitrariness and error. This article presents an approach to modelling detention basins measured from light detection and ranging remote sensing data. A case study is illustrated by using the White Oak Bayou watershed of Harris County, Texas. The storage-stage curve obtained from the volumetric analysis is used in a modified detention basins routing model, which was developed by adding the weir structure control to the traditional hydrologic reservoir routing equations. The model simulation showed that the peak flow of the synthetic 100-year reoccurrence event was effectively reduced and delayed by the detention basins. The comparison with the simulation results from the traditional reservoir routing model suggested that previous studies using the reservoir routing model were likely to underestimate the flood reduction effect of detention basins. The sensitivity analysis of the parameters showed that the detention basin design and evaluation should pay more attention on the weir height and river channel's roughness.

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Mapping detention basins and deriving their spatial attributes from airborne LiDAR data for hydrological applications

Cited by 27 publications

References 27 publications

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

Modelling detention basins measured from high‐resolution light detection and ranging data

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