This dissertation describes research investigating the potential for using Landsat data to identify and characterize woody canopy cover on reclaimed coal-mined lands through three separate studies. The objective of the first study was to assess whether surface coal mines in the forested central Appalachian regions of the US can be separated from the other prevalent forest-replacing disturbances through analysis of an interannual chronosequence of Landsat images. Disturbances were classified using descriptors of the disturbance/recovery trajectories: disturbance minimum, recovery slope and recovery maximum. Three vegetation indices (VIs) (normalized difference vegetation index, NDVI; tasseled cap greenness/brightness ratio, TC G/B; and inverse of Landsat band 3, B3I) were used to analyze multitemporal trajectories generated using both pixels and objects. Classification accuracies using objects were better than those obtained using pixels for all VIs. The highest object-based classification accuracy was achieved using TC G/B (89%), followed by NDVI (88%) and B3I (80%). The objective of the second study was to evaluate performance of a woody canopy cover (including both native and invasive species) estimation method based on the 2011 National Land Cover Database (NLCD) protocol for both mined and non-mined areas of the central Appalachians. Potential explanatory variables included raw and derived bands from leaf-on and leaf-off Landsat scenes plus terrain descriptors. Results show that the model developed to estimate canopy cover for mines (R2 = 0.78, Adj. R2 = 0.77, RMSE = 16%) is more robust than the models developed for non-mines, mixed, and all areas combined. The objective of the third study was to determine whether four disturbance/recovery parameters (recovery time, disturbance minimum, recovery slope, and recovery maximum), alone or in combination with variables identified in the second study, enable robust estimation of woody canopy cover on reclaimed surface coal mines. Of the disturbance/recovery parameters, only recovery time made a significant contribution to the model (R2 0.45, Adj. R2 0.44, RMSE 14%). Addition of leaf-on and leaf-off NDVI improved the R2 to 0.54 (Adj. R2 0.53, RMSE 13%). Analysis of Landsat data has strong potential for identifying reclaimed mines and characterizing the extent to which woody canopy has recovered post-reclamation. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/38737 |
| Date | 19 August 2011 |
| Creators | Sen, Susmita |
| Contributors | Geospatial and Environmental Analysis, Thomas, Valerie A., Masek, Jeffrey G., Campbell, James B. Jr., Zipper, Carl E., Wynne, Randolph H. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | Sen_S_D_2011.pdf |
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