Analysis of soil moisture patterns in forested and suburban catchments in Baltimore, Maryland, using high‐resolution photogrammetric and LIDAR digital elevation datasets

Tenenbaum, David E.; Band, Lawrence E.; Kenworthy, Stephen T.; Tague, Christina

doi:10.1002/hyp.5895

Cited by 53 publications

(61 citation statements)

References 25 publications

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“…In general, these studies found that higher-quality vertical information results in appreciable improvements in the representation of topographic surfaces, more accurate delineations of hydrologically relevant parameters and more appropriate model outputs, especially regarding spatially distributed information. To our knowledge only two other studies, Tenenbaum et al (2006) they showed that lidar-TWIs provided improved predictions of near-surface soil moisture in an urbanizing environment where refined flowpath delineations were necessary, but not in a forested catchment where a coarser, photogrammetric DEM better captured the more generalized soil moisture patterns.…”

Section: Source Data: Usgs Dems Vs Lidar Demsmentioning

confidence: 85%

“…To some extent, discrepancies in TWIsoil moisture correlations of the previously mentioned studies may be due to variations in the accuracy of the underlying DEM data. Only a few studies have specifically examined the advantages of lidar-derived TWIs relative to other less precise DEM sources, such as the standard USGS 10 m DEMs (e.g., Tenenbaum et al, 2006;Murphy et al, 2009).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

“…For instance, using a 5 m lidar-derived TWI, Tague et al (2010) demonstrated an average R 2 value of 0.74 across two experimental plots in MD, USA. Likewise, Tenenbaum et al (2006), Sulebak et al (2000), and Schmidt and Persson (2003) used highresolution TWIs (< 10 m) derived from high-resolution elevation source data (not necessarily lidar), and found R 2 values ranging from 0.51 to 0.87.…”

Section: Best Overall Modelmentioning

confidence: 99%

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Evaluating topographic wetness indices across central New York agricultural landscapes

Buchanan

Fleming

Schneider

et al. 2014

Hydrol. Earth Syst. Sci.

106

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Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches -especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R 2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) lidar-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved correlations.

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Section: Source Data: Usgs Dems Vs Lidar Demsmentioning

confidence: 85%

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Section: Best Overall Modelmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating topographic wetness indices across central New York agricultural landscapes

Buchanan

Fleming

Schneider

et al. 2014

Hydrol. Earth Syst. Sci.

106

View full text Add to dashboard Cite

show abstract

“…High-resolution topographic information from lidar has proved important for stream channel delineation (Kinzel et al, 2013), rating curve estimation (Nathanson et al, 2012;Lyon et al, 2015), floodplain mapping and inundation (Marks and Bates, 2000;Kinzel et al, 2007), and topographic water accumulation indices (Sørensen and Seibert, 2007;Jensco et al, 2009). Lidar measurements of micro-topography shows potential for improving soil property and moisture information (e.g., Tenenbaum et al, 2006), surface and floodplain roughness (Mason et al, 2003, Forzieri et al, 2010Brasington et al, 2012;Brubaker et al, 2013), hydraulic dynamics and sediment transport McKean et al, 2014), surface ponding and storage volume calculations (Li et al, 2011;French, 2003), and wetland delineation (e.g., Lane and D'Amico, 2010). Certain hydrological modeling fields are well poised to utilize high-resolution topography, such as movement of water in urban environments (Fewtrell et al, 2008), in-channel flow modeling (Mandlburger et al, 2009;Legleiter et al, 2011), and hyporheic exchange and ecohydraulics in small streams (e.g., Wheaton et al, 2010b).…”

Section: Advances In Hydrology Using Lidarmentioning

confidence: 99%

Laser vision: lidar as a transformative tool to advance critical zone science

Harpold

Marshall

Lyon

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demon- Published by Copernicus Publications on behalf of the European Geosciences Union. 2882 A. A. Harpold et al.: Laser vision: lidar as a transformative toolstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.

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“…The refinement in topographic accuracy and resolution can also improve physically-based models, such as landslide hazard predictions (Tarolli and Tarboton, 2006) or estimates of soil moisture across the forested landscape (Tenenbaum et al, 2006). (Gaedeke, 2006;Hickman, 1993;Sawyer and Keeler-Wolf, 1995) Latin name and author Common name Mountains due to the prevalence of these features.…”

Section: Applications In Hydrology and Geomorphologymentioning

confidence: 99%

Accuracy of Forest Road and Stream Channel Characteristics Derived from LiDAR in Forested Mountain Conditions

White¹

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Accuracy of Forest Road and Stream Channel CharacteristicsDerived from LiDAR in Forested Mountain Conditions Russell White Forest roads and stream channels are mapped using a variety of remote sensing and ground-based techniques. In densely forested areas, conventional remote sensing methods provide limited terrain information, while ground-based surveys can be time-consuming, difficult, and expensive. Light Detection and Ranging (LiDAR) is an airborne remote sensing technology used to create high-resolution digital elevation models (DEMs) of the earth's surface.This study tests the accuracy of forest road and stream channel features mapped using LiDAR in the steep, forested terrain of California's Santa Cruz Mountains. A conventional total station survey was used to determine centerline position and elevations along a four-kilometer forest road, and along six thirty-meter stream channel study reaches. A 1.5 m LiDAR DEM was suitable to accurately map the location of the forest road and channel features. Ninety five percent of the LiDAR-derived road length was located within 2.2 m normal to the field-surveyed centerline and LiDAR-derived road slopes were not significantly different from field-surveyed slopes. Stream channel features derived from the LIDAR DEM were located within 2.7 m normal to the field-surveyed thalweg, while the LiDAR-derived slopes measured within 0.49 percent of field-surveyed slopes. These findings indicate that LiDAR can provide accurate terrain measurements that are suitable for resource management and assessment.

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Analysis of soil moisture patterns in forested and suburban catchments in Baltimore, Maryland, using high‐resolution photogrammetric and LIDAR digital elevation datasets

Cited by 53 publications

References 25 publications

Evaluating topographic wetness indices across central New York agricultural landscapes

Evaluating topographic wetness indices across central New York agricultural landscapes

Laser vision: lidar as a transformative tool to advance critical zone science

Accuracy of Forest Road and Stream Channel Characteristics Derived from LiDAR in Forested Mountain Conditions

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