Michael P. Strager

Additive effects of mining and residential development on stream conditions in a central Appalachian watershed

Abstract Large-scale surface mining in southern West Virginia significantly alters headwater stream networks. The extent to which mining interacts with other stressors to determine physical, chemical, and biological conditions in aquatic systems downstream is unclear. We conducted a watershed-scale assessment of Pigeon Creek, an intensively mined watershed of the Tug Fork drainage in Mingo County, West Virginia. Our objectives were to: 1) develop landscape-based indicators of mining and residential development, 2) quantify the interactive effects of mining and residential development on in-stream conditions, and 3) identify landscape-based thresholds above which biological impairment occurs in this watershed. Macroinvertebrate community structure was negatively correlated with intensity of mining and residential development. Correlation analysis and partial Mantel tests indicated that mining (% of total subwatershed area) caused acute changes in water chemistry (r = 0.55–0.91), whereas residential development (parcel density) strongly affected both physical habitat (r = 0.59–0.81) and macroinvertebrate community structure (r = 0.59–0.93). The combined effects of mining and development on in-stream biotic conditions were additive. Sites affected by equivalent levels of both stressors had lower Ephemeroptera, Plecoptera, Trichoptera richness than sites affected by either stressor alone. Biological impairment thresholds occurred at ~25% total mining (equivalent to a specific conductance of ~250 µS/cm) and at parcel densities of ~5 and 14 parcels/km2. Our results provide a tool that can be used to predict downstream ecological response to proposed mining given pre-existing watershed conditions. Our study suggests that effective management of impacts from new mine development must address nonmining-related impacts in this region.

Landscape indicators and thresholds of stream ecological impairment in an intensively mined Appalachian watershed

Abstract Coal-mine development is occurring at a rapid rate in the central Appalachians, but few tools exist to assess the consequences of cumulative effects of mining to downstream aquatic resources. We constructed and applied an index of mining intensity (MI) to the Lower Cheat River basin, northern West Virginia. Our objectives were to: 1) determine if the MI could be used to predict stream-water quality and biological conditions, 2) quantify the extent to which geology and the geographic position of mines modulate the effects of mining on in-stream conditions, and 3) identify thresholds of MI that produce quantifiable changes to benthic macroinvertebrate communities. We quantified water chemistry, habitat quality, and benthic macroinvertebrate communities from May 2002 to May 2003 in 39 stream segments randomly distributed across a range of MI, coal geology, elevation, and watershed area. We sampled benthic macroinvertebrates at an additional 41 validation sites in May 2002. The MI was positively correlated with dissolved metals (r  =  0.65–0.85) and negatively correlated with ecological condition metrics (r  =  0.49–0.78), including total richness, Ephemeroptera, Plecoptera, Trichoptera richness, and the West Virginia Stream Condition Index. Coal geology and distance from mining had a significant interactive effect on benthic macroinvertebrate responses. Streams draining watersheds with Freeport coal geology had significantly poorer water quality and ecological condition than streams draining watersheds with similar MI but with Kittanning coal geology. Mining effects on stream conditions diminished as distance from the nearest mining activities upstream increased to a distance of ∼10 km. Changepoint analysis provided evidence of threshold effects of mining on benthic macroinvertebrate communities in Freeport coal watersheds but not in Kittanning coal watersheds. Abrupt reductions in ecological condition occurred at MI values as low as 1 to 5% of maximum intensity, and ecological impairment to streams became almost certain at MI >18 to 20%. Our results provide evidence of an interactive effect of landuse intensity, underlying geology, and the spatial arrangement of disturbance on the degree of impairment to receiving water bodies. The thresholds we identified could be used by water-resource managers to protect and restore stream conditions in actively mined watersheds of the central Appalachian region.

The Overlooked Terrestrial Impacts of Mountaintop Mining

Ecological research on mountaintop mining has been focused on aquatic impacts because the overburden (i.e., the mountaintop) is disposed of in nearby valleys, which leads to a wide range of water-quality impacts on streams. There are also numerous impacts on the terrestrial environment from mountaintop mining that have been largely overlooked, even though they are no less wide ranging, severe, and multifaceted. We review the impacts of mountaintop mining on the terrestrial environment by exploring six broad themes: (1) the loss of topographic complexity, (2) forest loss and fragmentation, (3) forest succession and soil loss, (4) forest loss and carbon sequestration, (5) biodiversity, and (6) human health and well-being.

Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation

National Agriculture Imagery Program (NAIP) orthophotography is a potentially useful data source for land cover classification in the United States due to its nationwide and generally cloud-free coverage, low cost to the public, frequent update interval, and high spatial resolution. Nevertheless, there are challenges when working with NAIP imagery, especially regarding varying viewing geometry, radiometric normalization, and calibration. In this article, we compare NAIP orthophotography and RapidEye satellite imagery for high-resolution mapping of mining and mine reclamation within a mountaintop coal surface mine in the southern coalfields of West Virginia, USA. Two classification algorithms, support vector machines and random forests, were used to classify both data sets. Compared to the RapidEye classification, the NAIP classification resulted in lower overall accuracy and kappa and higher allocation disagreement and quantity disagreement. However, the accuracy of the NAIP classification was improved by reducing the number of classes mapped, using the near-infrared band, using textural measures and feature selection, and reducing the spatial resolution slightly by pixel aggregation or by applying a Gaussian low-pass filter. With such strategies, NAIP data can be a potential alternative to RapidEye satellite data for classification of surface mine land cover.

Watershed analysis with GIS: The watershed characterization and modeling system software application

The watershed characterization and modeling system (WCMS) was developed to support decision making and the management of water resources at a statewide level in West Virginia. Specific hydrological analysis functions were combined within a customized GIS interface to provide decision support capabilities to both technical and non-technical users. Components of the current system include: an overland flow path model that indicates optimum water quality sampling locations, flow estimation for all streams in an identified area, an instream water quality and loading model for pollutant levels, and a ranking model to prioritize treatment alternatives based on user defined criteria and preferences. The primary goals of this system are to provide consistent technical information related to natural watershed processes and to predict the impacts of alternative management scenarios for decision makers. WCMS is currently used by the West Virginia Department of Environmental Protection (WVDEP) to guide policy development and management decisions that address watershed and water quality issues throughout the state.

Hierarchical classification of stream condition: a house–neighborhood framework for establishing conservation priorities in complex riverscapes

Abstract.  Despite improved understanding of how aquatic organisms are influenced by environmental conditions at multiple scales, we lack a coherent multiscale approach for establishing stream conservation priorities in active coal-mining regions. We classified watershed conditions at 3 hierarchical spatial scales, following a house–neighborhood–community approach, where houses (stream segments) are embedded within neighborhoods (Hydrologic Unit Code [HUC]-12 watersheds) embedded within communities (HUC-10 watersheds). We used this information to develop a framework to prioritize restoration and protection in two HUC-8 watersheds in an intensively mined region of the central Appalachians. We used landscape data to predict current conditions (water chemistry and macroinvertebrate biotic integrity) for all stream segments with boosted regression tree (BRT) analysis. Mining intensity, distance to mining, and coal type were the dominant predictors of water quality and biological integrity. A hardness–salinity dimension of the water-chemistry data was explained by land-cover features and stream elevation. We compiled segment-level conditions to the HUC-12 and HUC-10 watershed scales to represent aquatic resource conditions hierarchically across 3 watershed-management scales. This process enabled us to relate stream-segment watershed conditions to watershed conditions in the broader context, and ultimately to identify key protection and restoration priorities in a metacommunity context. Our hierarchical classification system explicitly identifies stream restoration and protection priorities within a HUC-12 watershed context, which ensures that the benefits of restoration will extend beyond the stream reach. Highest protection priorities are high-quality HUC-12 watersheds adjacent to low-quality HUC-12 watersheds. Highest restoration priorities are HUC-12 watersheds in poor–fair condition within HUC-10 watersheds of good–excellent condition, whereas lowest restoration priorities are isolated HUC-12 watersheds. In high-priority HUC-12 watersheds, stream segments with the highest restoration priority are those that maximize watershed-scale restorability. A similar process for classifying conditions and restoration priorities may be valuable in other heavily impacted regions where strategic approaches are needed to maximize watershed-scale recovery.

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.

Scenario analysis predicts context-dependent stream response to landuse change in a heavily mined central Appalachian watershed

Abstract.  Scenario analysis has the potential to improve management of aquatic systems throughout the Mountaintop Removal–Valley Fill mining (MTR–VF) region of central Appalachia. However, the extent to which surface mining interacts with other landuse stressors (i.e., cumulative effects) is unclear, and this limits our ability to predict the effects of new mines on physical, chemical, and biological conditions downstream. We tested for additive and interactive effects of landuse change (surface mining, deep mining, and residential development) on water quality (specific conductance and Se), habitat quality, and benthic macroinvertebrates via a uniquely designed watershed-scale assessment of the Coal River, West Virginia (USA). We derived equations for predicting in-stream response to landscape changes and predicted the outcome of a realistic future scenario involving development of 15 permitted mines. Elevated Se concentrations were directly correlated with incremental increases in surface-mining extent. Surface mining, deep mining, and residential development had additive effects on elevated specific conductance and reduced biological condition. We found evidence of a positive interactive effect (stressor antagonism) of deep mining and residential development on biological condition, presumably caused by stream-flow augmentation from deep mines. Landscape context influenced predicted impacts from construction of 15 new mines because of additive and interactive effects of landuse change. New surface mines increased the number of receiving streams exceeding chemical and biological criteria, but a greater proportion of receiving streams exceeded chemical and biological criteria at equivalent levels of new mine development when pre-existing stressors were present. When surface mining was the only stressor, ≥30 or 40% increases in surface mining caused 100% of streams to exceed chemical or biological standards, respectively, whereas in streams stressed by deep mining and residential development, ≥10% additional surface mining caused 100% of streams to exceed chemical and biological standards. Continued progress in this area will require a better understanding of how landuse change affects aquatic systems in the rest of the MTR–VF mining region, where watershed-to-watershed variation in landuse patterns probably causes variability in ecological response.

Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation

This study investigates machine-learning algorithms and measures derived from RapidEye satellite imagery and light detection and ranging (lidar) data for geographic object-based image analysis classification of mining and mine reclamation. Support vector machines, random forests, and boosted classification and regression trees classification algorithms were assessed and compared with the k-nearest neighbour (k-NN) classifier. For geographic object-based image analysis classification of mine landscapes, the use of disparate data (i.e. lidar data) improved overall accuracy, whereas the use of complex, object-oriented variables such as object geometry measures, first-order texture, and second-order texture from the grey-level co-occurrence matrix decreased or did not improve the classification accuracy. Support vector machines generally outperformed k-NN and the ensemble tree classifiers when only using the band means. With the incorporation of lidar-descriptive statistics, all four algorithms provided statistically comparable accuracies. K-NN suffered reduced classification accuracy with high-dimensional feature spaces, suggesting that a more complex machine-learning algorithm may be more appropriate when a large number of predictor variables are used.

Combining RapidEye Satellite Imagery and Lidar for Mapping of Mining and Mine Reclamation

The combination of RapidEye satellite imagery and light detection and ranging (lidar) derivatives was assessed for mapping land-cover within a mountaintop coal surface mine complex in the southern coalfields of West Virginia, USA. Support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART) algorithms were used. Incorporation of the lidar-derived data increased map accuracy in comparison to using only the five imagery bands, and SVM generally produced a more accurate classification than the ensemble tree algorithms based on overall map accuracy, Kappa statistics, allocation disagreement, quantity disagreement, and McNemar’s test of statistical significance. Based on measures of predictor variable importance within the ensemble tree classifiers, the normalized digital surface model (nDSM) was found to be more useful than first return intensity data for differentiating the classes. Introduction Commercial satellite imagery such as Ikonos, GeoEye, and RapidEye provide high spatial resolution but low spectral resolution compared to sensors such as Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), or Moderate Resolution Imaging Spectrometer (MODIS) (Warner et al., 2009). Although high spatial resolution can yield fine detail for land-cover and vegetative mapping, classification is complicated by the increased spatial resolution and decreased spectral resolution. Fine spatial resolution tends to generate high internal variability within land-cover classes, which can lead to decreases in classification accuracy (Townshend, 1981; Cushnie, 1987; Townshend, 1992; Baker et al., 2013). This research investigated a potential means to enhance classification accuracy by combining high-resolution commercial satellite imagery with light detection and ranging (lidar) data. The analysis focused on mapping land-cover classes in a mountaintop coal surface mine complex in the southern coalfields of the eastern United States. Because surface mine complexes experience rapid change due to human disturbance and reclamation, they are particularly good examples of disturbed landscapes. Although this research focuses on mapping land-cover within a mountaintop coal mine, the challenges in mapping mining landscapes are typical of other disturbances, such as timber harvesting, urban sprawl, etc. This work adds to prior remote sensing of surface mines research by investigating information gained by combining lidar and commercial satellite data for mapping land-cover (Cowen et al., 2000). This research had two components. First, we assessed lidar-derived inputs as predictor variables when combined with commercial satellite imagery to enhance land-cover mapping. Second, we compared three machine learning algorithms for the classification: support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART). The image data consisted of commercial RapidEye imagery. Lidar-derived predictor variables included the normalized digital surface model (nDSM) generated by subtracting ground return data from the first return data, first return intensity data, and the first return intensity range within raster grid cells.