Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale

The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data

The relevance of ERTS-1 data to the state of Ohio

There are no author-identified significant results in this report

Remote sensing for mined area reclamation: Application inventory

Applications of aerial remote sensing to coal mined area reclamation are documented, and information concerning available data banks for coal producing areas in the east and midwest is given. A summary of mined area information requirements to which remote sensing methods might contribute is included

Automatic interpretation of MSS-LANDSAT data applied to coal refuse site studies in southern Santa Catarina State, Brazil

The coal mining district in southeastern Santa Catarina State is considered one of the most polluted areas of Brazil. The author has identified significant preliminary results on the application of MSS-LANDSAT digital data to monitor the coal refuse areas and its environmental consequences in this region

Third Earth Resources Technology Satellite Symposium. Volume 2: Summary of results

Summaries are provided of significant results taken from presentations at the symposium along with some typical examples of the applications of ERTS-1 data for solving resources management problems at the national, state, and local levels

Assessment of Carbon Storage and Biomass on Minelands Reclaimed to Grassland Environments Using Landsat Spectral Indices

This study investigated carbon (C) storage and biomass in grasslands of West Virginia reclaimed surface minesites. Mine-related disturbance and subsequent reclamation may be an important component of C cycling. Biomass and C storage generally increased for the first five years after reclamation, but then declined, suggesting a nonlinear pattern to vegetation recovery. Three 2007 Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus images were used to assess the potential to predict biomass from raw red and near infrared radiance, the tasseled cap transformation (TC), and four vegetation indices [normalized difference vegetation index, enhanced vegetation index (EVI), triangular vegetation index, and the soil adjusted vegetation index]. TC greenness and EVI were most strongly correlated with biomass and illustrate a modest potential for monitoring vegetation recovery in reclaimed minelands. Additionally, a number of regression models that included age since reclamation and spectral indices were statistically significant suggesting a temporal recovery pattern amongst minesites in this study

Assessment of Carbon Storage and Biomass on Minelands Reclaimed to Grassland Environments Using Landsat Spectral Indices

This study investigated carbon (C) storage and biomass in grasslands of West Virginia reclaimed surface minesites. Mine-related disturbance and subsequent reclamation may be an important component of C cycling. Biomass and C storage generally increased for the first five years after reclamation, but then declined, suggesting a nonlinear pattern to vegetation recovery. Three 2007 Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus images were used to assess the potential to predict biomass from raw red and near infrared radiance, the tasseled cap transformation (TC), and four vegetation indices [normalized difference vegetation index, enhanced vegetation index (EVI), triangular vegetation index, and the soil adjusted vegetation index]. TC greenness and EVI were most strongly correlated with biomass and illustrate a modest potential for monitoring vegetation recovery in reclaimed minelands. Additionally, a number of regression models that included age since reclamation and spectral indices were statistically significant suggesting a temporal recovery pattern amongst minesites in this study

Satellite data for surface-mine inventory

To determine the feasibility of satellite data for surface-mine inventory, particularly as it applies to coal, a case study was conducted in Maryland. A band-ratio method was developed to measure disturbed surface areas, and it proved to be extendible both temporally and geographically. This method was used to measure area changes in the region over three time periods from September 1972 through July 1974 and to map the entire two-county area for 1973. For mines ranging between 31 and 244 acres (12 to 98 hectares) the measurement accuracy of total affected acreage was determined to be 92%. Mines of 120 acres (50 hectares) and larger were measured with greater accuracy, some within one percent of the actual area. The ability to identify, classify, and measure strip-mine surfaces in a two-county area (1,541 square kilometers - 595 square miles) of western Maryland was demonstrated through the use of computer processing. On the basis of these results the use of LANDSAT satellite data and multilevel sampling of aircraft and field verification inspections, multispectral analysis of digital data is shown to be an effective, rapid, and accurate means of monitoring the surface mining cycle

Using Unmanned Aerial Vehicles to Quantify Erosion Control Measures on a Reclaimed Central Utah Coal Mine

For certain landscape reclamation efforts surrounding, the Utah Division of Oil Gas and Mining (UDOGM) utilizes a surface roughing technique called “pocking”. The process of pocking establishes closely spaced gouges approximately 1.2 meters in diameter and 0.5 meters deep across a reclaimed landscape in order to reduce surface erosion and promote plant growth on steep terrain in arid regions. Pocks are designed as a series of micro watersheds that trap water to aid in plant establishment and reduces overland flow of water. Over time vegetation grows within the pocks as they infill with sediment. While this method is considered an effective reclamation technique, its effectiveness has, to date, relied on observation only. This research will utilize consumer grade unmanned aerial systems (UASs) commonly known as “drones”, to develop a technique by which pocks can be monitored and the effectiveness of pocking can be quantified. To this end, UAS overflights spanning two years (2019-2020) resulted in high-resolution (2.5cm) ortho imagery as well as digital terrain data at the same resolution. A comparison of the data collected across these two years identified erosion and deposition within and between pocks as well as the establishment and spread of seeded vegetation. The results also identified a spatial pattern of landscape subsidence as the reclaimed landscape settled. We found that, with effective geographic control, low-cost, off-the-shelf, consumer grade drones are an effective tool to monitor and quantify changes in reclaimed landscapes

Utilizing Skylab data in on-going resources management programs in the state of Ohio

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process