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.