Geology topics

Monica Dorning

Publications and source records attributed to Monica Dorning.

9 recordsLinked to original sources

USGS2019

Review of indicators for comparing environmental effects across energy sources

Robust, quantitative comparisons of environmental effects across energy sources can support development of energy planning strategies that meet growing demand while managing and minimizing undesirable effects on environmental resources. Multicriteria analyses of energy systems often use a suite of indicators to make such comparisons, but those indicators and their units of measure vary among studies. We reviewed 179 papers that described or applied energy indicators to compare environmental effects of different primary energy sources to answer four questions: (1) what environmental indicators have been used in multicriteria energy-source comparisons? (2) across studies, how consistently are these indicators used to quantify effects? (3) to what degree are different effects accounted for across energy sources and locations? and (4) how comprehensive are indicators in terms of assessing known environmental effects? For reviewed studies, we quantified the number of unique indicators and the diversity of indicators used to measure different effects. We also recorded the specific measurement units applied to quantify each indicator, the energy sources evaluated, and the continent where each study was conducted. While we found that many environmental effects of energy development have been analyzed across multiple sources, indicators were frequently measured, interpreted, and applied in ways that are not directly comparable, and some known environmental effects were infrequently assessed. We also found an emphasis on applying indicators to renewable energy sources; assessing current and potential energy sources, both renewable and nonrenewable, would greatly clarify the full suite of tradeoffs among sources and can inform energy development strategies that minimize adverse environmental effects. Overall, our review indicated that making comprehensive comparisons of the effects of energy development across sources may require efforts to standardize how effects are measured, synthesize effects literature into an open-source database, expand the range of environmental effects analyzed, and establish consistent frameworks for comparison.

Environmental Research Letters↗

USGS2019

Geographic context affects the landscape change and fragmentation caused by wind energy facilities

Wind energy generation causes transformation of landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change and fragmentation caused by these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities’ geographic context on changes in landscape patterns. We digitized the footprints of 39 wind facilities and the surrounding land cover using high-resolution imagery. We then measured landscape pattern before and after facility construction using 3 metrics associated with species responses to habitat loss and fragmentation (amount, core area, and connectivity of undeveloped land) within 1km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land, on average, by just 1.8% while changes in metrics of landscape pattern were relatively much larger (50 to 140%). Statistical models indicated levels of pre-construction development were a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Density of new roads was higher in places with little pre-existing development and may be influenced by facility design. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

PeerJ↗

USGS2019

Biases in the literature on direct wildlife mortality from energy development

Comparing environmental impacts of different energy sources can inform energy investments and environmental conservation. Direct wildlife mortality from energy development receives substantial public and scientific attention, but it is unclear whether rigorous comparisons of mortality among energy sources are possible. To address this question, we compared availability of mortality studies among energy sources, wildlife groups, and regions, and assessed comparability of mortality indicators measured. Whereas wind and hydropower have received substantial mortality research exceeding their proportional contributions to global energy production, coal, oil and gas, and bioenergy have received fewer studies and are underrepresented relative to their contributions. Furthermore, research is biased toward birds and fish and North America and Europe, and there are inconsistencies among energy sources and limited replication of most indicators measured. These results indicate that rigorous comparisons of direct wildlife mortality among energy sources are not currently possible and highlight research needs for improving understanding of energy's environmental impacts.

BioScience↗

USGS2019

Modeling landowner interactions and development patterns at the urban fringe

Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland.

North Carolina↗

USGS2018

Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numer- ous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and tem- poral determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these land- scape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual- sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially vari- able visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

North Carolina↗

USGS2017

Integrating spatially explicit representations of landscape perceptions into land change research

Purpose of Review Human perceptions of the landscape can influence land-use and land-management decisions. Recognizing the diversity of landscape perceptions across space and time is essential to understanding land change processes and emergent landscape patterns. We summarize the role of landscape perceptions in the land change process, demonstrate advances in quantifying and mapping landscape perceptions, and describe how these spatially explicit techniques have and may benefit land change research. Recent Findings Mapping landscape perceptions is becoming increasingly common, particularly in research focused on quantifying ecosystem services provision. Spatial representations of landscape perceptions, often measured in terms of landscape values and functions, provide an avenue for matching social and environmental data in land change studies. Integrating these data can provide new insights into land change processes, contribute to landscape planning strategies, and guide the design and implementation of land change models. Summary Challenges remain in creating spatial representations of human perceptions. Maps must be accompanied by descriptions of whose perceptions are being represented and the validity and uncertainty of those representations across space. With these considerations, rapid advancements in mapping landscape perceptions hold great promise for improving representation of human dimensions in landscape ecology and land change research.

Current Landscape Ecology Reports↗

USGS2017

Bayesian methods to estimate urban growth potential

Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.

Landscape and Urban Planning↗

USGS2017

Payments for carbon sequestration to alleviate development pressure in a rapidly urbanizing region

The purpose of this study was to determine individuals' willingness to enroll in voluntary payments for carbon sequestration programs through the use of a discrete choice experiment delivered to forest owners living in the rapidly urbanizing region surrounding Charlotte, North Carolina. We examined forest owners' willingness to enroll in payments for carbon sequestration policies under different levels of financial incentives (annual revenue), different contract lengths, and different program administrators (e.g., private companies versus a state or federal agency). We also examined the influence forest owners' sense of place had on their willingness to enroll in hypothetical programs. Our results showed a high level of ambivalence toward participating in payments for carbon sequestration programs. However, both financial incentives and contract lengths significantly influenced forest owners' intent to enroll. Neither program administration nor forest owners' sense of place influenced intent to enroll. Although our analyses indicated that payments from carbon sequestration programs are not currently competitive with the monetary returns expected from timber harvest or property sales, certain forest owners might see payments for carbon sequestration programs as a viable option for offsetting increasing tax costs as development encroaches and property values rise.

Georgia, North Carolina, Tennessee, Virginia, Sout↗

USGS2016

Oil and gas development influences big-game hunting in Wyoming

Development from extracting oil and gas resources can have unintended effects on multiple ecosystem functions, with cascading effects on wildlife, ecosystem services, and local economies. Big-game hunting opportunities may be closely related to these effects, but empirical analyses of impacts of energy development on hunting are limited. We examined the influence of oil and gas development density on harvest efficiency, or harvest per unit of hunter effort, within all hunt areas in Wyoming, USA, from 2008 to 2014 for 3 big-game species: elk ( Cervus canadensis ), mule deer ( Odocoileus hemionus ), and pronghorn ( Antilocapra americana ). Using harvest/hunter day as the response variable, we compared linear mixed-effects models for each species that included total well density (i.e., all wells constructed up to the year of record), active well density (i.e., only those wells currently producing oil or gas in that year), or neither as a predictor variable. We used well densities as indicators of development in the absence of data specifying the locations of other oil and gas infrastructure (e.g., roads, well pads). Models also accounted for the fixed effects of road density, hunter density, proportion of the area that is public land with unrestricted hunter access, proportion of the area that is forested, year of observation, and random effects of variation among hunt areas nested within associated game herd units. Presence of oil and gas wells had a positive influence on harvest efficiency for elk and mule deer. Although there was no overall effect to pronghorn, there was a negative influence of wells on juvenile pronghorn harvest efficiency. Changes in harvest efficiency due to expanding oil and gas development could alter the time spent hunting by hunters and their chances of harvesting an animal. This could have subsequent impacts on hunter satisfaction, game populations, and economic revenue generated from recreational hunters.

Wyoming↗