A multiresolution index of valley bottom flatness for mapping depositional areas

Gallant, John; Dowling, T. I.

doi:10.1029/2002wr001426

Cited by 603 publications

(304 citation statements)

References 18 publications

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“…The values of these MrVBF images range from 0 to 7, with maximum values varying from different sites and resolutions. The maximum MrVBF value in a 3s image is generally lower than that in a 1s image for the same site, because the MrVBF values derived from 3s DEM in the first processing step correspond to those derived in the second step when using a base 1s resolution, according to Gallant and Dowling's algorithm [23]. For the relatively flatter sites, the maximum MrVBF may be even higher than those of the mountainous areas (site 3 and 4).…”

Section: Mrvbf Value Vs Hand Valuementioning

confidence: 95%

“…It does not rely on a prior identification of channels and can depict unchanneled valley areas including perched swamps and lake wetlands [23]. It is an index that integrates the flatness and lowness characteristics of valley bottoms.…”

Section: Calculation Of Mrvbf Indexmentioning

confidence: 99%

“…If the DEM resolution is substantially different from 1s, the initial slope threshold (Ts) must be adjusted to retain the relationship between slope and resolution. In this study, Ts for 1s DEM was set to be 16%, while that for 3s was set to be 8%, as suggested by Gallant and Dowling [23]. The other parameters used the same default values.…”

Section: Calculation Of Hand Indexmentioning

confidence: 99%

“…It is a mountainous area with an average elevation over 1600 m. Main water bodies in this site include Lago Maggiore, Lago di Lugano, Lago di Como, and Brienzersee. [23]. It tries to identify valley bottoms using a slope classification constrained to convergent areas, which is more likely to be occupied by water than simply low places.…”

Section: Study Sitesmentioning

confidence: 99%

“…Most of these terrain indices can generally be categorized into two groups, valley bottom based and drainage based. A classic and popular one in the first group is the Multi-resolution Valley Bottom Flatness (MrVBF) proposed by Gallant and Dowling [23]. It tries to identify valley bottoms using a slope classification constrained to convergent areas, which is more likely to be occupied by water than simply low places.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

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Abstract:The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.

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Section: Mrvbf Value Vs Hand Valuementioning

confidence: 95%

Section: Calculation Of Mrvbf Indexmentioning

confidence: 99%

Section: Calculation Of Hand Indexmentioning

confidence: 99%

Section: Study Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

Physically based modeling in catchment hydrology at 50: Survey and outlook

2015

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Integrated, process‐based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Richards, shallow water, and advection‐dispersion equations), and they require robust numerical techniques for their resolution. Traditional (and still open) challenges in developing reliable and efficient models are associated with heterogeneity and variability in parameters and state variables; nonlinearities and scale effects in process dynamics; and complex or poorly known boundary conditions and initial system states. As catchment modeling enters a highly interdisciplinary era, new challenges arise from the need to maintain physical and numerical consistency in the description of multiple processes that interact over a range of scales and across different compartments of an overall system. This paper first gives an historical overview (past 50 years) of some of the key developments in physically based hydrological modeling, emphasizing how the interplay between theory, experiments, and modeling has contributed to advancing the state of the art. The second part of the paper examines some outstanding problems in integrated catchment modeling from the perspective of recent developments in mathematical and computational science.

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Lags in hydrologic recovery following an extreme drought: Assessing the roles of climate and catchment characteristics

Yang

McVicar

Donohue

et al. 2017

Water Resources Research

128

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Drought, generally characterized by below-average water supply, propagates through the hydrologic system with consequent ecological and societal impacts. Compared with other drought aspects, the recovery of drought especially in the hydrological components, which directly relates to the recovery of water resources for agricultural, ecological and human needs, is less-understood. Here, taking the Millennium drought in southeast Australia (1997-2009) as an illustrating case, we comprehensively examined multiple aspects of the meteorological (i.e., precipitation) and hydrological (i.e., streamflow and base flow) droughts across 130 unimpaired catchments using long-term hydro-meteorological observations. Results show that the duration and intensity of the meteorological drought are both lengthened and amplified in the hydrological drought, suggesting a nonstationarity in the rainfall-runoff relationship during a prolonged drought. Additionally, we find a time lag commonly exists between the end of the meteorological droughts and the end of the hydrological drought, with the recovery of base flow showing a longer lag than the recovery of streamflow. The recovery rate of precipitation after drought was found to be the dominant factor that controls the recovery of hydrological droughts while catchment landscape (i.e., valley bottom flatness) plays an important but secondary role in controlling the lags in the hydrological recovery. Other hydro-climatic factors and catchment properties appear to have only minor influences governing hydrological drought recovery. Our findings highlight a delayed response in the terrestrial components of the hydrological cycle to precipitation after prolonged drought, and provide valuable scientific guidance to water resources management and water security assessment in regions facing future droughts.

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A multiresolution index of valley bottom flatness for mapping depositional areas

Cited by 603 publications

References 18 publications

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Physically based modeling in catchment hydrology at 50: Survey and outlook

Lags in hydrologic recovery following an extreme drought: Assessing the roles of climate and catchment characteristics

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