Geology topics

John A. Young

Publications and source records attributed to John A. Young.

At least 37 records · Page 2Linked to original sources

USGS2019

Explaining harvests of wild-harvested herbaceous plants: American ginseng as a case study

Wild-harvested plants face increasing demand globally. As in many fisheries, monitoring the effect of harvesting on the size and trajectory of resource stocks presents many challenges given often limited data from disparate sources. Here we analyze American ginseng ( Panax quinquefolius L.) harvests from 18 states in the eastern U.S. 1978–2014 to infer temporal patterns and evidence of population declines, and we test the effects of local environmental and socioeconomic factors on ginseng harvesting at the county level 2000–2014. Despite rising prices, annual wild ginseng harvests decreased from a high point in the late 1980s to early 1990s, then, in most, increased after 2005 or 2010 - suggesting range-wide overexploitation notwithstanding federal regulations that, since 1999, restrict minimum harvest age. County-level harvest rates increased with available habitat, road density, poverty and unemployment, but decreased when public land formed a large proportion of county area. Harvests were largest in the Southern Appalachian region. Poverty and accessibility were strongly related to high levels of harvesting. A key implication is that to conserve valuable wild native plant products while also improving local livelihoods, wild cultivation and good stewardship practices must be strongly promoted. Our approach to assessing the condition of wild populations offers a broad template that could be adapted to other wild-harvested plants.

Biological Conservation↗

USGS2019

A preliminary assessment of hyperspectral remote sensing technology for mapping submerged aquatic vegetation in the Upper Delaware River National Parks

Hyperspectral remote sensing of submerged aquatic vegetation is a complex and difficult process that is affected by unique constraints on the energy flow profile near and below the water surface. In addition, shallow, winding, lotic systems, such as the Upper Delaware River, present additional remote sensing problems in the form of specular reflectance, variable depth and constituents in the water column and sometimes extremely weak signal strength due to absorption and scattering in the water column that can be statistically overwhelmed by the reflectance from upland vegetation in any individual image scene. Here we test hyperspectral imagery from the Civil Air Patrol’s (CAP), Airborne Real-time Cueing Hyperspectral Enhanced Recon (ARCHER) system in the scenic waters of two National Parks on the Upper Delaware River. A number of unique image processing problems were encountered, including specular reflectance from winding lotic systems, variable depth and flow dynamics of the riverine environment, and disproportionate signal strength from surface reflectance in this riverine environment. This were solved by applying a specular reflectance removal algorithm, applying field data collections to classification results and masking upland vegetation so as to not statistically overwhelm the weak reflectance signal from surface and near-surface water. Much was learned about conducting imaging spectroscopy in such difficult conditions. Significant results include successful mapping of SAV presenece/absence, advantages of upland masking of the reflectance signal, and a numkber of processing approaches that are unique to this environment. In this paper we summarize our results and identify unique issues that must be addressed in this environment

New Jersey, New York, Pennsylvania↗

USGS2018

Community for Data Integration fiscal year 2017 funded project report

The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 11 projects funded in fiscal year 2017, outlining their goals, activities, and outputs.

Open-File Report↗

USGS2018

Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data

Oil and gas development is changing the landscape in many regions of the United States and globally. However, the nature, extent, and magnitude of landscape change and development, and precisely how this development compares to other ongoing land conversion (e.g. urban/sub-urban development, timber harvest) is not well understood. In this study, we examine land conversion from oil and gas infrastructure development in the upper Susquehanna River basin in Pennsylvania and New York, an area that has experienced much oil and gas development over the past 10 years. We quantified land conversion in terms of forest canopy geometric volume loss in contrast to previous studies that considered only areal impacts. For the first time in a study of this type, we use fine-scale lidar forest canopy geometric models to assess the volumetric change due to forest clearing from oil and gas development and contrast this land change to clear cut forest harvesting, and urban and suburban development. Results show that oil and gas infrastructure development removed a large volume of forest canopy from 2006 to 2013, and this removal spread over a large portion of the study area. Timber operations (clear cutting) on Pennsylvania State Forest lands removed a larger total volume of forest canopy during the same time period, but this canopy removal was concentrated in a smaller area. Results of our study point to the need to consider volumetric impacts of oil and gas development on ecosystems, and to place potential impacts in context with other ongoing land conversions.

New York, Pennsylvania↗

USGS2018

A detailed risk assessment of shale gas development on headwater streams in the Pennsylvania portion of the Upper Susquehanna River Basin, U.S.A.

The development of unconventional oil and gas (UOG) involves infrastructure development (well pads, roads and pipelines), well drilling and stimulation (hydraulic fracturing), and production; all of which have the potential to affect stream ecosystems. Here, we developed a fine-scaled (1:24,000) catchment-level disturbance intensity index (DII) that included 17 measures of UOG capturing all steps in the development process (infrastructure, water withdrawals, probabilistic spills) that could affect headwater streams (< 200 km 2 in upstream catchment) in the Upper Susquehanna River Basin in Pennsylvania, U.S.A. The DII ranged from 0 (no UOG disturbance) to 100 (the catchment with the highest UOG disturbance in the study area) and it was most sensitive to removal of pipeline cover, road cover and well pad cover metrics. We related this DII to three measures of high quality streams: Pennsylvania State Exceptional Value (EV) streams, Class A brook trout streams and Eastern Brook Trout Joint Venture brook trout patches. Overall only 3.8% of all catchments and 2.7% of EV stream length, 1.9% of Class A streams and 1.2% of patches were classified as having medium to high level DII scores (> 50). Well density, often used as a proxy for development, only correlated strongly with well pad coverage and produced materials, and therefore may miss potential effects associated with roads and pipelines, water withdrawals and spills. When analyzed with a future development scenario, 91.1% of EV stream length, 68.7% of Class A streams and 80.0% of patches were in catchments with a moderate to high probability of development. Our method incorporated the cumulative effects of UOG on streams and can be used to identify catchments and reaches at risk to existing stressors or future development.

Pennsylvania↗

USGS2018

Brook trout distributional response to unconventional oil and gas development: Landscape context matters

We conducted a large-scale assessment of unconventional oil and gas (UOG) development effects on brook trout ( Salvelinus fontinalis ) distribution. We compiled 2231 brook trout collection records from the Upper Susquehanna River Watershed, USA. We used boosted regression tree (BRT) analysis to predict occurrence probability at the 1:24,000 stream-segment scale as a function of natural and anthropogenic landscape and climatic attributes. We then evaluated the importance of landscape context (i.e., pre-existing natural habitat quality and anthropogenic degradation) in modulating the effects of UOG on brook trout distribution under UOG development scenarios. BRT made use of 5 anthropogenic (28% relative influence) and 7 natural (72% relative influence) variables to model occurrence with a high degree of accuracy [Area Under the Receiver Operating Curve (AUC) = 0.85 and cross-validated AUC = 0.81]. UOG development impacted 11% ( n = 2784) of streams and resulted in a loss of predicted occurrence in 126 (4%). Most streams impacted by UOG had unsuitable underlying natural habitat quality ( n = 1220; 44%). Brook trout were predicted to be absent from an additional 26% ( n = 733) of streams due to pre-existing non-UOG land uses (i.e., agriculture, residential and commercial development, or historic mining). Streams with a predicted and observed (via existing pre- and post-disturbance fish sampling records) loss of occurrence due to UOG tended to have intermediate natural habitat quality and/or intermediate levels of non-UOG stress. Simulated development of permitted but undeveloped UOG wells ( n = 943) resulted in a loss of predicted occurrence in 27 additional streams. Loss of occurrence was strongly dependent upon landscape context, suggesting effects of current and future UOG development are likely most relevant in streams near the probability threshold due to pre-existing habitat degradation.

New York, Pennsylvania↗

USGS2018

Predicting biological conditions for small headwater streams in the Chesapeake Bay watershed

A primary goal for Chesapeake Bay watershed restoration is to improve stream health and function in 10% of stream miles by 2025. Predictive spatial modeling of stream conditions, when accurate, is one method to fill gaps in monitoring coverage and estimate baseline conditions for restoration goals. Predictive modeling can also monitor progress as additional data become available. We developed a random forests model to predict biological condition of small streams (<200 km 2 in drainage) in the Chesapeake Bay watershed. Biological condition was measured with the Chesapeake Bay Basin-wide Index of Biotic Integrity (Chessie BIBI), a stream macroinvertebrate index. Our goal was to predict biological condition in all unsurveyed small streams present in a 1:24,000 scale catchment layer as a 2004–2008 baseline. We reclassified the 5-category Chessie BIBI ratings into two categories, poor and fair/good, to align with management goals of the Chesapeake Bay Program. The model included 12 geospatial predictor variables including measures on spatial location, bioregion, land cover, soil, precipitation, and number of dams in local catchments. We trained the model with a random 75% subset of Chessie BIBI data ( n = 1449), and used the remaining 25% of Chessie BIBI data ( n = 484) as test data. The model performed well, correctly predicting 72% of samples in training data and 73% of samples in test data, but model accuracy varied among bioregions. We performed uncertainty analyses by adding bands of either ±0.05 or ±0.10 BIBI units to the cutoff between poor and fair/good. These uncertainty analyses resulted in 14.5% (±0.05 band) and 24.8% (±0.10 band) of samples in test data being classified as in uncertain condition. For 95,877 small stream reaches in the Chesapeake Bay watershed, the model predicted 64% in fair/good condition, the ±0.05 uncertainty analyses predicted 57% in fair/good condition, and the ±0.10 uncertainty analysis predicted 50% in fair/good condition. These reported values have different implications for the number of improved stream miles required to meet the goal of improving 10%. Incorporating uncertainty provides an assessment of model strength as well as confidence in predictions. We, therefore, suggest increased reporting of uncertainty in studies that spatially predict stream conditions.

Chesapeake Bay watershed↗

USGS2017

Shale Gas Development and Brook Trout: Scaling Best Management Practices to Anticipate Cumulative Effects

Shale gas development may involve trade-offs between energy development and benefits provided by natural ecosystems. However, current best management practices (BMPs) focus on mitigating localized ecological degradation. We review evidence for cumulative effects of natural gas development on brook trout (Salvelinus fontinalis) and conclude that BMPs should account for potential watershed-scale effects in addition to localized influences. The challenge is to develop BMPs in the face of uncertainty in the predicted response of brook trout to landscape-scale disturbance caused by gas extraction. We propose a decision-analysis approach to formulating BMPs in the specific case of relatively undisturbed watersheds where there is consensus to maintain brook trout populations during gas development. The decision analysis was informed by existing empirical models that describe brook trout occupancy responses to landscape disturbance and set bounds on the uncertainty in the predicted responses to shale gas development. The decision analysis showed that a high efficiency of gas development (e.g., 1 well pad per square mile and 7 acres per pad) was critical to achieving a win-win solution characterized by maintaining brook trout and maximizing extraction of available gas. This finding was invariant to uncertainty in predicted response of brook trout to watershed-level disturbance. However, as the efficiency of gas development decreased, the optimal BMP depended on the predicted response, and there was considerable potential value in discriminating among predictive models through adaptive management or research. The proposed decision-analysis framework provides an opportunity to anticipate the cumulative effects of shale gas development, account for uncertainty, and inform management decisions at the appropriate spatial scales.

Environmental Practice↗

USGS2017

Integration of vegetation community spatial data into a prescribed fire planning process at Shenandoah National Park, Virginia (USA)

Many eastern forest communities depend on fire for regeneration or are enhanced by fire as a restoration practice. However, the use of prescribed fire in the mesic forested environments and the densely populated regions of the eastern United States has been limited. The objective of our research was to develop a science-based approach to prioritizing the use of prescribed fire in appropriate forest types in the eastern United States based on a set of desired management outcomes. Through a process of expert elicitation and data analysis, we assessed and integrated recent vegetation community mapping results along with other available spatial data layers into a spatial prioritization tool for prescribed fire planning at Shenandoah National Park (Virginia, USA). The integration of vegetation spatial data allowed for development of per-pixel priority rankings and exclusion areas enabling precise targeting of fire management activities on the ground, as well as a park-wide ranking of fire planning compartments. We demonstrate the use and evaluation of this approach through implementation and monitoring of a prescribed burn and show that progress is being made toward desired conditions. Integration of spatial data into the fire planning process has served as a collaborative tool for the implementation of prescribed fire projects, which assures projects will be planned in the most appropriate areas to meet objectives that are supported by current science.

Virginia↗

USGS2016

Predicting the occurrence of cave-inhabiting fauna based on features of the earth surface environment

One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the presence of species in understudied areas.

Appalachian Landscape Conservation Cooperative↗

USGS2016

USGS lidar science strategy—Mapping the technology to the science

Summary The U.S. Geological Survey (USGS) utilizes light detection and ranging (lidar) and enabling technologies to support many science research activities. Lidar-derived metrics and products have become a fundamental input to complex hydrologic and hydraulic models, flood inundation models, fault detection and geologic mapping, topographic and land-surface mapping, landslide and volcano hazards mapping and monitoring, forest canopy and habitat characterization, coastal and fluvial erosion mapping, and a host of other research and operational activities. This report documents the types of lidar being used by the USGS, discusses how lidar technology facilitates the achievement of individual mission area goals within the USGS, and offers recommendations and suggested changes in direction in terms of how a mission area could direct work using lidar as it relates to the mission area goals that have already been established.

Open-File Report↗

USGS2015

Accounting for groundwater in stream fish thermal habitat responses to climate change

Forecasting climate change effects on aquatic fauna and their habitat requires an understanding of how water temperature responds to changing air temperature (i.e., thermal sensitivity). Previous efforts to forecast climate effects on brook trout habitat have generally assumed uniform air-water temperature relationships over large areas that cannot account for groundwater inputs and other processes that operate at finer spatial scales. We developed regression models that accounted for groundwater influences on thermal sensitivity from measured air-water temperature relationships within forested watersheds in eastern North America (Shenandoah National Park, USA, 78 sites in 9 watersheds). We used these reach-scale models to forecast climate change effects on stream temperature and brook trout thermal habitat, and compared our results to previous forecasts based upon large-scale models. Observed stream temperatures were generally less sensitive to air temperature than previously assumed, and we attribute this to the moderating effect of shallow groundwater inputs. Predicted groundwater temperatures from air-water regression models corresponded well to observed groundwater temperatures elsewhere in the study area. Predictions of brook trout future habitat loss derived from our fine-grained models were far less pessimistic than those from prior models developed at coarser spatial resolutions. However, our models also revealed spatial variation in thermal sensitivity within and among catchments resulting in a patchy distribution of thermally suitable habitat. Habitat fragmentation due to thermal barriers therefore may have an increasingly important role for trout population viability in headwater streams. Our results demonstrate that simple adjustments to air-water temperature regression models can provide a powerful and cost-effective approach for predicting future stream temperatures while accounting for effects of groundwater.

Virginia↗

USGS2014

Using ecological indicators and a decision support system for integrated ecological assessment at two national park units in the Mid-Atlantic region, U.S.A.

We implemented an integrated ecological assessment using a GIS-based decision support system model for Upper Delaware Scenic and Recreational River (UPDE) and Delaware Water Gap National Recreation Area (DEWA)—national park units with the mid-Atlantic region of the United States. Our assessment examined a variety of aquatic and terrestrial indicators of ecosystem components that reflect the parks’ conservation purpose and reference condition. Our assessment compared these indicators to ecological thresholds to determine the condition of park watersheds. Selected indicators included chemical and physical measures of water quality, biologic indicators of water quality, and landscape condition measures. For the chemical and physical measures of water quality, we used a water quality index and each of its nine components to assess the condition of water quality in each watershed. For biologic measures of water quality, we used the Ephemeroptera, Plecoptera, Trichoptera aquatic macroinvertebrate index and, secondarily, the Hilsenhoff aquatic macroinvertebrate index. Finally, for the landscape condition measures of our model, we used percent forest and percent impervious surface. Based on our overall assessment, UPDE and DEWA watersheds had an ecological assessment score of 0.433 on a −1 to 1 fuzzy logic scale. This score indicates that, in general, the natural resource condition within watersheds at these parks is healthy or ecologically unimpaired; however, we had only partial data for many of our indicators. Our model is iterative and new data may be incorporated as they become available. These natural parks are located within a rapidly urbanizing landscape—we recommend that natural resource managers remain vigilant to surrounding land uses that may adversely affect natural resources within the parks.

New Jersey, New York, Pennsylvania↗

USGS2014

A landscape-based reconnaissance survey of estrogenic activity in streams of the upper Potomac, upper James,and Shenandoah Rivers, USA

Endocrine-disrupting compounds (EDCs) are becoming of increasing concern in waterways of the USA and worldwide. What remains poorly understood, however, is how prevalent these emerging contaminants are in the environment and what methods are best able to determine landscape sources of EDCs. We describe the development of a spatially structured sampling design and a reconnaissance survey of estrogenic activity along gradients of land use within sub-watersheds. We present this example as a useful approach for state and federal agencies with an interest in identifying locations potentially impacted by EDCs that warrant more intensive, focused research. Our study confirms the importance of agricultural activities on levels of a measured estrogenic equivalent (E2Eq) and also highlights the importance of other potential sources of E2Eq in areas where intensive agriculture is not the dominant land use. Through application of readily available geographic information system (GIS) data, coupled with spatial statistical analysis, we demonstrate the correlation of specific land use types to levels of estrogenic activity across a large area in a consistent and unbiased manner.

Environmental Monitoring and Assessment↗

USGS2014

Using science to strengthen our Nation's resilience to tomorrow's challenges: understanding and preparing for coastal impacts

Hurricane Sandy caused unprecedented damage across some of the most densely populated coastal areas of the northeastern United States. The costly, landscape-altering destruction left in the wake of this storm is a stark reminder of our Nation’s need to become more resilient as we inevitably face future coastal hazards. As our Nation recovers from this devastating natural disaster, it is clear that accurate scientific information is essential as we seek to identify and develop strategies to address trends in coastal landscape change and reduce our future vulnerability to major storm events. To address this need, the U.S. Geological Survey (USGS) received $43.2 million in supplemental appropriations from the Department of the Interior (DOI) to conduct the scientific research needed to guide response, recovery, and rebuilding activities and to develop effective strategies for protecting coastal communities and resources in the future. This fact sheet describes how the USGS is combining interdisciplinary science with state-of-the-art technologies to achieve a comprehensive understanding of coastal change caused by Hurricane Sandy. By assessing coastal change impacts through research and by developing tools that enhance our science capabilities, support coastal stakeholders, and facilitate effective decision making, we continue to build a greater understanding of the processes at work across our Nation’s complex coastal environment—from wetlands, estuaries, barrier islands, and nearshore marine areas to infrastructure and human communities. This improved understanding will increase our resilience as we prepare for future short-term, extreme events as well as long-term coastal change.

Fact Sheet↗

USGS2013

Chemical contaminants in water and sediment near fish nesting sites in the Potomac River basin: determining potential exposures to smallmouth bass (Micropterus dolomieu)

The Potomac River basin is an area where a high prevalence of abnormalities such as testicular oocytes (TO), skin lesions, and mortality has been observed in smallmouth bass (SMB, Micropterus dolomieu). Previous research documented a variety of chemicals in regional streams, implicating chemical exposure as one plausible explanation for these biological effects. Six stream sites in the Potomac basin (and one out-of-basin reference site) were sampled to provide an assessment of chemicals in these streams. Potential early life-stage exposure to chemicals detected was assessed by collecting samples in and around SMB nesting areas. Target chemicals included those known to be associated with important agricultural and municipal wastewater sources in the Potomac basin. The prevalence and severity of TO in SMB were also measured to determine potential relations between chemistry and biological effects. A total of 39 chemicals were detected at least once in the discrete-water samples, with atrazine, caffeine, deethylatrazine, simazine, and iso-chlorotetracycline being most frequently detected. Of the most frequently detected chemicals, only caffeine was detected in water from the reference site. No biogenic hormones/sterols were detected in the discrete-water samples. In contrast, 100 chemicals (including six biogenic hormones/sterols) were found in a least one passive-water sample, with 25 being detected at all such samples. In addition, 46 chemicals (including seven biogenic hormones/sterols) were found in the bed-sediment samples, with caffeine, cholesterol, indole, para-cresol, and sitosterol detected in all such samples. The number of herbicides detected in discrete-water samples per site had a significant positive relation to TO rank (a nonparametric indicator of TO), with significant positive relations between TO rank and atrazine concentrations in discrete-water samples and to total hormone/sterol concentration in bed-sediment samples. Such significant correlations do not necessarily imply causation, as these chemical compositions and concentrations likely do not adequately reflect total SMB exposure history, particularly during critical life stages.

Potomac River Basin↗

USGS2013

Significance of headwater streams and perennial springs in ecological monitoring in Shenandoah National Park

Shenandoah National Park has been monitoring water chemistry and benthic macroinvertebrates in stream ecosystems since 1979. These monitoring efforts were designed to assess the status and trends in stream condition associated with atmospheric deposition (acid rain) and changes in forest health due to gypsy moth infestations. The primary objective of the present research was to determine whether the current long-term macroinvertebrate and water-quality monitoring program in Shenandoah National Park was failing to capture important information on the status and trends in stream condition by not sufficiently representing smaller, headwater streams. The current benthic-macroinvertebrate and water-chemistry sampling designs do not include routine collection of data from streams with contributing watershed areas smaller than 100 hectares, even though these small streams represent the overwhelming proportion of total stream length in the park. In this study, we sampled headwater sites, including headwater stream reaches (contributing watershed area approximately 100 hectares (ha) and perennial springs, in the park for aquatic macroinvertebrates and water chemistry and compared the results with current and historical data collected at long-term ecological monitoring (LTEM) sites on larger streams routinely sampled as part of ongoing monitoring efforts. The larger purpose of the study was to inform ongoing efforts by park managers to evaluate the effectiveness and efficiency of the current aquatic monitoring program in light of other potential stressors (for example, climate change) and limited resources. Our results revealed several important findings that could influence management decisions regarding long-term monitoring of park streams. First, we found that biological indicators of stream condition at headwater sites and perennial springs generally were more indicative of lower habitat quality and were more spatially variable than those observed at sites on routinely monitored larger streams. We hypothesized that poorer stream condition observed in smaller streams was due to stream drying that occurs more frequently in headwater areas. We also found that biological and water-chemistry measures responded differently to landscape drivers. Variation in most biological endpoints was driven primarily by stream size and was only secondarily associated with bedrock geology. In contrast, water chemistry showed essentially the opposite pattern, with underlying geology explaining much of the variation and stream size being of secondary importance. Therefore, expanding the LTEM program to include headwater areas would yield substantially different biological information, whereas broad inferences regarding spatial patterns in water chemistry would probably not change. Although significant differences in community composition were observed among streams of different sizes, no taxa were unique to headwater sites. All taxa collected at the 45 headwater sites also had been collected at one or more LTEM sites during one or more years. This observation indicates that headwater sites in the park may be structured by biotic nestedness; consequently, focusing management efforts on preserving the species pool at the larger LTEM sites would likely result in the protection of most taxa parkwide. Finally, linkages (correlations) between water chemistry and biological measures of stream condition were signficantly stronger when assessed at the LTEM sites than when assessed at the springs or headwater sites, indicating that conditions at downstream sites may be better indicators of water-quality trends.

Virginia↗

USGS2013

An evaluation of automated GIS tools for delineating karst sinkholes and closed depressions from 1-meter LIDAR-derived digital elevation data

LiDAR (Light Detection and Ranging) surveys of karst terrains provide high-resolution digital elevation models (DEMs) that are particularly useful for mapping sinkholes. In this study, we used automated processing tools within ArcGIS (v. 10.0) operating on a 1.0 m resolution LiDAR DEM in order to delineate sinkholes and closed depressions in the Boyce 7.5 minute quadrangle located in the northern Shenandoah Valley of Virginia. The results derived from the use of the automated tools were then compared with depressions manually delineated by a geologist. Manual delineation of closed depressions was conducted using a combination of 1.0 m DEM hillshade, slopeshade, aerial imagery, and Topographic Position Index (TPI) rasters. The most effective means of visualizing depressions in the GIS was using an overlay of the partially transparent TPI raster atop the slopeshade raster at 1.0 m resolution. Manually identified depressions were subsequently checked using aerial imagery to screen for false positives, and targeted ground-truthing was undertaken in the field. The automated tools that were utilized include the routines in ArcHydro Tools (v. 2.0) for prescreening, evaluating, and selecting sinks and depressions as well as thresholding, grouping, and assessing depressions from the TPI raster. Results showed that the automated delineation of sinks and depressions within the ArcHydro tools was highly dependent upon pre-conditioning of the DEM to produce "hydrologically correct" surface flow routes. Using stream vectors obtained from the National Hydrologic Dataset alone to condition the flow routing was not sufficient to produce a suitable drainage network, and numerous artificial depressions were generated where roads, railways, or other manmade structures acted as flow barriers in the elevation model. Additional conditioning of the DEM with drainage paths across these barriers was required prior to automated 2delineation of sinks and depressions. In regions where the DEM had been properly conditioned, the tools for automated delineation performed reasonably well as compared to the manually delineated depressions, but generally overestimated the number of depressions thus necessitating manual filtering of the final results. Results from the TPI thresholding analysis were not dependent on DEM pre-conditioning, but the ability to extract meaningful depressions depended on careful assessment of analysis scale and TPI thresholding.

Conference Paper↗