Reclaiming the Appalachian Landscape: Understanding Surface Mine Reclamation Through Remote Sensing
| dc.contributor.author | Putnam, Daniel Jacob | en |
| dc.contributor.committeechair | Wynne, Randolph H. | en |
| dc.contributor.committeechair | Thomas, Valerie Anne | en |
| dc.contributor.committeemember | Coulston, John W. | en |
| dc.contributor.committeemember | Bessac, Julie | en |
| dc.contributor.department | Forest Resources and Environmental Conservation | en |
| dc.date.accessioned | 2026-01-13T09:00:15Z | en |
| dc.date.available | 2026-01-13T09:00:15Z | en |
| dc.date.issued | 2026-01-12 | en |
| dc.description.abstract | Surface mining has been the dominant driver of land cover change in the Central Appalachian region for decades, fundamentally altering the topography and ecological trajectory of over one million acres of forest. While the Surface Mining Control and Reclamation Act (SMCRA) of 1977 mandated the restoration of mined land to an equal or better land use, the ecological integrity of these reclaimed landscapes remains uncertain, often characterized by "arrested succession" and the proliferation of invasive species. This dissertation integrates multi source remote sensing, time-series analysis, and causal inference modeling to comprehensively evaluate the status, history, and structural quality of post-mining land cover in Virginia and Tennessee. To establish a baseline of current reclamation status, we first developed a novel classification framework integrating Sentinel-2 spectral data, CCDC phenological metrics, and 3DEP LiDAR structure to map seven land cover classes, with a specific focus on the invasive shrub Autumn olive (Elaeagnus umbellata). The classification achieved area-weighted overall accuracies between 74.3% and 76.8%, revealing that Autumn olive occupies approximately 9.8% of mined land in Virginia, peaking in prevalence on sites 14 years post-disturbance. Expanding this analysis temporally, we reconstructed a 37-year history (1984–2021) of land cover dynamics, documenting a region-wide decline in barren land from 60% to roughly 14–22%. However, succession trajectories diverged significantly by state; while Tennessee mines transitioned largely to coniferous and herbaceous cover, Virginia mines experienced a progressive expansion of Autumn olive to 14% of the landscape by 2021. Spatially explicit ANN-CA-MC simulations project autumn olive presence will persist into the near future, and that historical expansion is driven by transitions from herbaceous and shrub/scrub cover. Finally, to assess the quality of successfully reforested mines, we utilized causal random forests and sample balancing to quantify the effect of mining on forest structure compared to non-mine disturbances. We found that while canopy cover on mines converges with reference forests after 15–20 years, mine forests in Virginia remain significantly stunted, averaging 2.28 m shorter than forests recovering from non mining disturbances. Furthermore, standard spectral recovery metrics derived from Landsat may not full capture these structural deficits, highlighting the necessity of structural data for reclamation monitoring. Collectively, these findings demonstrate that while surface mines have successfully revegetated, they frequently fail to restore structural attributes and desired land cover composition, often diverting into stable, invasive-dominated states that require active management intervention | en |
| dc.description.abstractgeneral | Surface mining in the Appalachian mountains fundamentally alters the landscape by remov ing all plants, and soil to access underground resources like coal. Under federal law, mining companies are required to "reclaim" this land, a process intended to reverse the damage and restore it to a condition equal to or better than its condition before mining. However, after decades of reclamation efforts, it remains unclear whether these landscapes are returning to forests similar to the surrounding land or are becoming dominated by non-native and highly competitive plants that can harm the local ecosystem. This work investigates the condition and composition of vegetation on reclaimed mines in Virginia and Tennessee using techno logical approaches that enable long-term and large-scale analysis. By combining satellite imagery with laser scanning technology, we identified and mapped the different types of plants growing on surface mines. We specifically focused on Autumn olive, a non-native shrub historically planted to stabilize soil that has since spread aggressively, potentially impeding forest regrowth. We found that while most mined land has successfully grown veg etation again, the type of vegetation varies significantly by state. In Virginia, Autumn olive has taken over approximately 10% of mined land , whereas Tennessee mines are dominated more by grasses and pine trees. Using historical satellite data from 1985 to 2021, we tracked these changes over time and used computer simulations to predict that this invasive shrub will continue to persist in Virginia if left unmanaged. Finally, we evaluated the quality of the forests that have grown on these mines. By comparing them to forests that were disturbed not by mines, we found that forests on reclaimed mines are shorter, but establish a forest canopy similar to non-mine forests. On average, trees growing on former mines in Virginia are over 2 meters (about 7 feet) shorter than trees in non-mining areas of similar age. These findings demonstrate that while reclamation has succeeded in making the land green again, they may lack some qualities of neighboring unmined areas, resulting in landscapes that are highly fragmented and invaded by aggressive non-native shrubs | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:45603 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/140762 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | cloud computing | en |
| dc.subject | change detection | en |
| dc.subject | machine learning | en |
| dc.title | Reclaiming the Appalachian Landscape: Understanding Surface Mine Reclamation Through Remote Sensing | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Forestry | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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