Autumn olive (Elaeagnus umbellata) presence and proliferation on former surface coal mines in Eastern USA

Linking mountaintop removal mining to water quality for imperiled species using satellite data

Environmental laws need sound data to protect species and ecosystems. In 1996, a proliferation of mountaintop removal coal mines in a region home to over 50 federally protected species was approved under the Endangered Species Act. Although this type of mining can degrade terrestrial and aquatic habitats, the available data and tools limited the ability to analyze spatially extensive, aggregate effects of such a program. We used two large, public datasets to quantify the relationship between mountaintop removal coal mining and water quality measures important to the survival of imperiled species at a landscape scale across Kentucky, Tennessee, Virginia, and West Virginia. We combined an annual map of the extent of surface mines in this region from 1985 to 2015 generated from Landsat satellite imagery with public water quality data collected over the same time period from 4,260 monitoring stations within the same area. The water quality data show that chronic and acute thresholds for levels of aluminum, arsenic, cadmium, conductivity, copper, lead, manganese, mercury, pH, selenium, and zinc safe for aquatic life were exceeded thousands of times between 1985 and 2015 in streams that are important to the survival and recovery of species on the Endangered Species List. Linear mixed models showed that levels of manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc increased by 6.73E+01 to 6.87E+05 μg/L and conductivity by 3.30E+06 μS /cm for one percent increase in the mined proportion of the area draining into a monitoring station. The proportion of a drainage area that was mined also increased the likelihood that chronic thresholds for copper, lead, and zinc required to sustain aquatic life were exceeded. Finally, the proportion of a watershed that was mined was positively related to the likelihood that a waterway would be designated as impaired under the Clean Water Act. Together these results demonstrate that the extent of mountaintop removal mining, which can be derived from public satellite data, is predictive of water quality measures important to imperiled species—effects that must be considered under environmental law. These findings and the public data used in our analyses are pertinent to ongoing re-evaluations of the effects of current mine permitting regulations to the recovery and survival of federally protected species.

Changes detected in the extent of surface mining and reclamation using multitemporal Landsat imagery: a case study of Jiu Valley, Romania

Surface mining represents the dominant driver of land coverage changes in the Jiu Valley mining area in Romania. Detecting and quantifying active mines and reclaimed areas are very important tasks given the effects of surface mining on the environment. In this paper, Landsat imagery for the years 1988, 1998, 2008, and 2017 was used to map the extent of surface mining and reclamation in the Jiu Valley mining area. The satellite images were classified using the Support Vector Machine (SVM) algorithm to map land cover classes, including mined areas, and post-classification comparison (PCC) technique to track changes through time. In order to identify and quantify active mines and reclaimed areas of mined areas, we used indices such as Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Modified Soil-Adjusted Vegetation Index-2 (MSAVI-2). For the entire area studied, during the period 1988-2017, the extent of surface mining was 6.5%, with peaks in the periods 1988-1998 and 1998-2008, namely, 205.2% and 4.0%, respectively, as a result of the extension of surface exploitation as distinct from that underground. Land cover conversion to mined areas was almost exclusively from agricultural, forest, and pasture. The results show that NDVI estimated the largest surfaces with active mines, reclaimed grassland, and reclaimed woodland, within the mined areas. SAVI and MSAVI-2 estimated larger surfaces classified as reclaimed forest. As a result of the expansion of surface mining areas, the landscape was considerably degraded through mining scars, landscape fragmentation, degradation, and pollution. However, during the past few years, reclamation activity has intensified in the affected areas through the occurrence of spontaneous vegetation, but also through forestation.

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.

Woody Vegetation Removal Benefits Grassland Birds on Reclaimed Surface Mines

Grassland birds have declined throughout North America. In the midwestern United States, reclaimed surface mines often provide expanses of contiguous grassland that support grassland birds. However, some reclaimed surface mines often experience severe woody vegetation encroachment, typically by invasive trees and shrubs, including black locust Robinia pseudoacacia, autumn olive Elaeagnus umbellata, and bush honeysuckle Lonicera spp. We conducted point-count surveys to investigate the effects of woody canopy cover and response to treatments of woody vegetation on the abundance of birds. Our treatments were a control, an herbicide application, and an herbicide application followed by cutting and shredding of standing dead woody vegetation. Estimated density of eastern meadowlark Sturnella magna, grasshopper sparrow Ammodramus savannarum, and Henslow's sparrow Centronyx henslowii was 670%, 958%, and 200%, respectively, greater on areas treated with herbicide and shredding and 279%, 666%, and 155%, respectively, greater on areas treated with herbicide-only when compared with control sites. When woody canopy cover increased from 0% to 20%, densities of eastern meadowlark, grasshopper sparrow, and Henslow's sparrow decreased by 83.9%, 74.9%, and 50.7%, respectively. Conversely, densities of eastern towhee Pipilo erythrophthalmus, prairie warbler Setophaga discolor, yellow-breasted chat Icteria virens, and yellow warbler Setophaga petechia increased 67.4%, 57.0%, 34.6%, and 117.7%, respectively, as estimated woody canopy coverage increased from 20% to 60%. Our results showed treating encroaching woody vegetation on reclaimed surface mines with herbicide and shredding increases available habitat used by grassland birds. Maintaining grasslands on reclaimed surface mines at ≤10% woody canopy coverage would be most beneficial to eastern meadowlarks, grasshopper sparrows, and Henslow's sparrows.

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

Watershed Vulnerability to Invasive N2-Fixing Autumn Olive and Consequences for Stream Nitrogen Concentrations

Autumn olive ( Thunb.) is an invasive and exotic N-fixing plant species found throughout the United States. Proliferation and spread of autumn olive have displaced native plants and raised concerns about the effects of N fixation and cycling on water quality in invaded areas. This study investigated the relationship between autumn olive cover and stream N concentrations. Twelve forested watersheds were selected and classified into edge, mid-distance, and interior-of-the-forest watersheds based on autumn olive density and distance from the permanent edge of invasion point along a major road corridor. For the 2012 vegetation survey, autumn olive cover in edge, mid, and interior watersheds ranged from 37 to 61%, 18 to 37%, and 4 to 10%, respectively. From 2006 to 2012, mean stream water NO-N concentration in the edge watersheds was significantly higher (1.39 mg L, < 0.0001) than mid (0.37 mg L) and interior (0.27 mg L) watersheds. A linear relationship was found between NO-N concentration and autumn olive cover ( = 0.72, = 0.0001). Mean stream water NH-N, specific conductivity, and pH were significantly less in the interior watersheds than in the edge watersheds. Additionally, peak specific conductivity and NO-N from edge watersheds coincided with peak stage for these watersheds, demonstrating that N flushing events were driven by surface and shallow subsurface flow pathways proximal to the stream. Results from this study demonstrate how encroachment of autumn olive can influence water quality and transform biogeochemical cycles in natural systems, which points to the need for effective management of autumn olive in the edge watersheds and riparian zones that are vulnerable to invasion and increased N export.

Identification of the disturbance and trajectory types in mining areas using multitemporal remote sensing images

Surface coal mining disturbances affect the local ecology, human populations and environmental quality. Thus, much public attention has been focused on mining issues and the need for monitoring of environmental disturbances in mining areas. An automated method for identifying mining disturbances, and for characterizing recovery of vegetative cover on disturbed areas using multitemporal Landsat imagery is described. The method analyzes normalized difference vegetation index (NDVI) data to identify sample points with multitemporal spectral characteristics ("trajectories") that indicate the presence of environmental disturbances caused by mining. A typical disturbance template of mining areas is created by analyzing NDVI trajectories of disturbed points and used to describe NDVI multitemporal patterns before, during, and following disturbances. The multitemporal sequences of disturbed sample points are dynamically matched with the typical disturbance template to obtain information including the disturbance year, trajectory type, and the nature of vegetation recovery. The method requires manual analysis of randomly selected sample points from within the study area to calculate several thresholds; once those thresholds are determined, the method's application can be automated. We applied the method to a stack of 26 Landsat images over a 32-year period, 1984 to 2015, for mining areas of Martin County KY and Logan County WV in eastern USA. When compared with the samples determined by direct interpretation, the method identified mining disturbances with 97% accuracy, the disturbance year with 90% accuracy, and disturbance-recovery trajectory type with 90% accuracy.

Integrated electrochemical Chip-on-Plant functional sensor for monitoring gene expression under stress

The ability to interact with plants, both to sense and to actuate, would open new opportunities for precision agriculture. These interactions can be achieved by using the plant as part of the sensing system. The present work demonstrates real-time monitoring of β-glucuronidase (GUS) expression in transgenic tobacco plants using its activity as a biomarker for functional sensing. As "proof of concept", we demonstrated GUS enzyme biosensing under constitutive expression in Msk8 tomato cells and transgenic tobacco plants and in heat shock inducible BY2 tobacco cells and tobacco plants. The sensing was done using a three-electrode microchip in Msk8 or BY2 cell culture or in tobacco plant leaves. The electrode microchip was used to transduce the expression of the GUS enzyme by chronoamperometry to a measurable electrical current signal. For the constitutive expression of GUS in Msk8 cells, the system sensitivity was 0.076 mA/mM-cm2 and the limit of detection was 0.1 mM. For the heat shock inducible BY2 cells the GUS enzyme activity was detected 12-26 h after the heat shock was applied (40 °C for 2 h) using two different substrates: p-nitrophenyl-β-glucuronide (with sensitivity of 0.051 mA/mM-cm2) and phenolphthalein-β-glucuronide (with sensitivity of 0.029 mA/mM-cm2).

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine

Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining’s extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km² of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km². Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m² of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.

Spatial factors of white-tailed deer herbivory assessment in the central Appalachian Mountains

Because moderate to over-abundant white-tailed deer (Odocoileus virginianus) herbivory impacts biodiversity and can alter community function, ecological benchmarks of herbivory impact are needed to assess deer impacts. We evaluated spatial patterns of deer herbivory and their relation to herbivory assessment by evaluating woody vegetation along 20 transects at each of 30 sites spread across a wide range of deer herd densities and vegetative condition throughout the biodiverse Appalachian Mountains of Virginia, USA. Surprisingly, herbivory patterns and the availability of woody forage generally were unchanged among physiographic regions and land use diversity classes. However, some relationships between browsing pattern and vegetation varied with scale. The total quantity of vegetation browsed on a given site and at the transect scale were related positively to the availability of forage, as the proportion of stems browsed decreased as stem density increased. However, this was only true when all stems were considered equally. When stem densities by species were weighted for deer preference, the proportion of stems browsed had no relationship or increased with stem density. Compared to the value from all transects sampled, on average, the mean of ≥ 3 transects within a site was within 0.1 of the browsing ratio and stem densities were within 0.5 stems m^-2. Our results suggest that one transect per square kilometer with a minimum of three transects may be sufficient for most browsing intensity survey requirements to assess herbivory impacts in the Appalachian region of Virginia. Still, inclusion of spatial factors to help partition variation of deer herbivory potentially may allow for improved precision and accuracy in the design of field herbivory impact assessment methods and improve their application across various landscape contexts.