Identifying woody vegetation on coal surface mines using phenological indicators with multitemporal Landsat imagery
- 1Dept. of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
- 2China University of Mining & Technology, Beijing, China
- 3Dept. of Crop and Soil and Environmental Sciences, Smyth Hall, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24061, USA
- *currently at: Dept. of Crop and Soil and Environmental Sciences, Smyth Hall, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24061, USA
Keywords: Afforestation, Ecosystem Recovery, Elaeagnus umbellata, Landsat, Land Cover Change, Mine Reclamation, Exotic Plants
Abstract. Surface mining for coal has disturbed large land areas in the Appalachian Mountains. Better information on mined lands' ecosystem recovery status is necessary for effective environmental management in mining-impacted regions. Because record quality varies between state mining agencies and much mining occurred prior to widespread use of geospatial technologies, accurate maps of mining extents, durations, and land cover effects are often not available. Landsat data are well suited to mapping and characterizing land cover and forest recovery on former coal surface mines. Past mine reclamation techniques have often failed to restore premining forest vegetation but natural processes may enable native forests to re-establish on mined areas with time. However, the invasive species autumn olive (Elaeagnus umbellate) is proliferating widely on former coal surface mines, often inhibiting reestablishment of native forests. Autumn olive outcompetes native vegetation because it fixes atmospheric nitrogen and benefits from a longer growing season than native deciduous trees. This longer growing season, along with Landsat 8's high signal to noise ratio, has enabled species-level classification of autumn olive using multitemporal Landsat 8 data at accuracy levels usually only obtainable using higher spatial or spectral resolution sensors. We have used classification and regression tree (CART®) and support vector machine (SVM) to classify five counties in the coal mining region of Virginia for presence and absence of autumn olive. The best model found was a CART® model with 36 nodes which had an overall accuracy of 84% and kappa of 0.68. Autumn olive had conditional kappa of 0.65 and a producers and users accuracy of 86% and 83% respectively. The best SVM model used a second order polynomial kernel and had an overall accuracy of 77%, an overall kappa of 0.54 and a producers and users accuracy of 60% and 90% respectively.