Using Unmanned Aerial Vehicles to Evaluate Revegetation Success on Natural Gas Pipelines.

The Appalachian region of the United States has experienced significant growth in the production of natural gas. Developing the infrastructure required to transport this resource to market creates significant disturbances across the landscape, as both well pads and transportation pipelines must be created in this mountainous terrain. Midstream infrastructure, which includes pipeline rights-of-way and associated infrastructure, can cause significant environmental degradation, especially in the form of sedimentation. The introduction of this non-point source pollutant can be detrimental to freshwater ecosystems found throughout this region. This ecological risk has necessitated the enactment of regulations related to midstream infrastructure development. Weekly, inspectors travel afoot along new pipeline rights-of-way, monitoring the re-establishment of surface vegetation and identifying failing areas for future management. The topographically challenging terrain of West Virginia makes these inspections difficult and dangerous to the hiking inspectors. We evaluated the accuracy at which unmanned aerial vehicles replicated inspector classifications to evaluate their use as a complementary tool in the pipeline inspection process. Both RGB and multispectral sensor collections were performed, and a support vector machine classification model predicting vegetation cover were made for each dataset. Using inspector defined validation plots, our research found comparable high accuracy between the two collection sensors. This technique displays the capability of augmenting the current inspection process, though it is likely that the model can be improved further. The high accuracy thus obtained suggests valuable implementation of this widely available technology in aiding these challenging inspections.

Land Cover Change Associated with Unconventional Oil and Gas Development in the Appalachian Region.

Unconventional oil and gas (UOG) wells from the Marcellus and Utica shale plays have expanded greatly across the Appalachian region of the United States (US) since the early 2000s. This region is now the single largest natural gas producing area of the US. The local and regional impacts of this industry on the landscape make it critical to understand for future planning efforts. This study investigated land cover change associated with over 21,000 unconventional wells representing 4,240 well pads permitted from 2007 to 2017 in Pennsylvania, West Virginia, and Ohio. The goal was to characterize UOG disturbance to document development patterns and extents in the region. Supervised classification was used to map land use and land-cover changes within a 25-ha buffer of well pads identified in the region. On average, disturbance related to unconventional development impacted 6.2 ha in Pennsylvania, 4.7 ha in Ohio and 4.4 ha in West Virginia and 5.6 ha over the region. Forest and grassland were found to be the most impacted cover types, with increases in impervious surface areas being a significant contributor to land-use classification change. These conversions can contribute to increased forest fragmentation and edge, which can in turn adversely impact biodiversity indicators at the regional level. Additionally, increases in impervious surface in small headwater watersheds can lead to increased sediment and runoff loads in receiving streams. Local and regional land use planning should be implemented during the well pad permit review process to help minimize environmental impacts over larger geographic scales.

Energy production and well site disturbance from conventional and unconventional natural gas development in West Virginia.

Natural gas production from the Appalachian region has reached record levels, primarily due to the rapid increase in development of unconventional oil and gas (UOG) resources. In 2020, over 65,000 conventional wells reported natural gas production; however, this only represented 5% of the total natural gas produced. The remaining 95% of natural gas production can be attributed to 3,901 UOG wells. There has been a wide body of research on disturbance trends related to unconventional development in the region; however, there is limited characterization of disturbance related to production of conventional oil and gas (COG) or research that details energy production in relation to land disturbance. This study compares land disturbance from COG and UOG development as well as energy production. Land disturbance related to COG and UOG development was assessed for wells drilled during 2009-2012. Production data were summarized for the same wells during the period of 2009-2020. The average area disturbed for COG pads was 0.82 ha while UOG pads disturbed 4.02 ha. Results from this study showed that COG wells disturbed significantly less land area during construction; however, UOG wells produced almost 28 times more energy per hectare of land disturbed. This energy production imbalance as well as the over 65,000 COG wells reporting production in 2020, indicates that the retirement and restoration of COG infrastructure could be done without significantly impacting total energy production. Continued research that includes ecosystem services and carbon sequestration opportunities in relation to production losses from retiring existing infrastructure should be considered.

Microbial communities reveal impacts of unconventional oil and gas development on headwater streams.

The demand for natural gas has led to the development of techniques used to access unconventional oil and natural gas (UOG) resources, due to the novelty of UOG, the potential impacts to freshwater ecosystems are not fully understood. We used a dual pronged approach to study the effects of UOG development on microbial biodiversity and function via a laboratory microcosm experiment and a survey study of streams with and without UOG development within their watersheds. The microcosm experiment simulated stream contamination with produced water, a byproduct of UOG operations, using sediment collected from one high water-quality stream and two low water-quality streams. For the survey study, biofilm and sediment samples were collected from streams experiencing varying levels of UOG development. In the microcosm experiment, produced water decreased microbial aerobic and anaerobic CO2 production in the high water-quality stream sediment but had a positive effect on this microbial activity in the lower water-quality stream sediments, suggesting habitat degradation alters the response of microbes to contaminants. Results from the stream survey indicate UOG development alters stream water temperature, chemistry, sediment aerobic and anaerobic CO2 production, and microbial community biodiversity in both sediments and biofilms. Correlations among UOG associated land use, environmental, and microbial variables suggest increases in light availability and sediment delivery to streams, due to deforestation and land disturbance, impact stream microbial communities and their function. Consistent changes in the relative abundance of bacterial taxa suggest microorganisms may be good indicators of the environmental changes associated with UOG development. The observed impacts of UOG development on microbial community composition and carbon cycling could have cascading effects on stream health and broader ecosystem function.

A spatially explicit framework for quantifying downstream hydrologic conditions.

Continued improvements in spatial datasets and hydrological modeling algorithms within Geographic Information Systems (GISs) have enhanced opportunities for watershed analysis. With more detailed hydrology layers and watershed delineation techniques, we can now better represent and model landscape to water quality relationships. Two challenges in modeling these relationships are selecting the appropriate spatial scale of watersheds for the receiving stream segment, and handling the network or pass-through issues of connected watersheds. This paper addresses these two important issues for enhancing cumulative watershed capabilities in GIS. Our modeling framework focuses on the delineation of stream-segment-level watershed boundaries for 1:24,000 scale hydrology, in combination with a topological network model. The result is a spatially explicit, vector-based, spatially cumulative watershed modeling framework for quantifying watershed conditions to aid in restoration. We demonstrate the new insights available from this modeling framework in a cumulative mining index for the management of aquatic resources in a West Virginia watershed.

Watershed Planning within a Quantitative Scenario Analysis Framework.

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall short as a result of an inability to quantify and predict complex cumulative effects of current and future land use scenarios at relevant spatial scales. The goal of this manuscript is to provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework. Sites are first selected for inclusion within the watershed assessment by identifying sites that fall along independent gradients and combinations of known stressors. Field and laboratory techniques are then used to obtain data on the physical, chemical, and biological effects of multiple land use activities. Multiple linear regression analysis is then used to produce landscape-based cumulative effects models for predicting aquatic conditions. Lastly, methods for incorporating cumulative effects models within a scenario analysis framework for guiding management and regulatory decisions (e.g., permitting and mitigation) within actively developing watersheds are discussed and demonstrated for 2 sub-watersheds within the mountaintop mining region of central Appalachia. The watershed assessment and management approach provided herein enables resource managers to facilitate economic and development activity while protecting aquatic resources and producing opportunity for net ecological benefits through targeted remediation.

Complex contaminant mixtures in multistressor Appalachian riverscapes.

Runoff from watersheds altered by mountaintop mining in the Appalachian region (USA) is known to pollute headwater streams, yet regional-scale assessments of water quality have focused on salinization and selenium. The authors conducted a comprehensive survey of inorganic contaminants found in 170 stream segments distributed across a spectrum of historic and contemporary human land use. Principal component analysis identified 3 important dimensions of variation in water chemistry that were significantly correlated with contemporary surface mining (principal component 1: elevated dominant ions, sulfate, alkalinity, and selenium), coal geology and legacy mines (principal component 2: elevated trace metals), and residential development (principal component 3: elevated sodium and chloride). The combination of these 3 dominant sources of pollutants produced a complex stream-to-stream patchwork of contaminant mixtures. Seventy-five percent of headwater streams (catchments < 5 km(2) ) had water chemistries that could be classified as either reference (49%), development only (18%), or mining only (8%). Only 21% of larger streams (catchments > 5 km(2) ) were classified as having reference chemistries, and chemistries indicative of combined mining and development contaminants accounted for 47% of larger streams (compared with 26% of headwater streams). Extreme degradation of larger streams can be attributed to accumulation of contaminants from multiple human land use activities that include contemporary mountaintop mining, underground mining, abandoned mines, and untreated domestic wastewater. Consequently, water quality improvements in this region will require a multicontaminant remediation approach.

Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia.

Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts.

Aggregating high-priority landscape areas to the parcel level: an easement implementation tool.

Landscape characteristics and parcel ownership information are often collected on different spatial scales leading to difficulties in implementing land use plans at the parcel level. This study provides a method for aggregating highly resolute landscape planning information to the parcel level. Our parcel prioritization model directly incorporates a Land Trust's conservation goals in the form of a compromise programming model. We then demonstrate the use of our approach for implementation decisions, including parcel selection under a budget constraint and the estimation of a total conservation budget necessary to meet specific conservation goals. We found that these cost constraints significantly alter the composition of the 'best' parcels for conservation and can also provide guidance for implementation. The model's results were integral to a local Land Trust's ability to further define and achieve their goals.