We evaluated several approaches for the automated detection and mapping of trees and treeline in an alpine environment. Using multiple remote sensing platforms and software programs, we evaluated both pixel-based and object-based classification approaches in combination with high-resolution multispectral imagery and LiDAR-derived tree height data. The study area in North Cascades National Park included over 10,000 hectares of some of the most rugged terrain in the conterminous U.S. Through the use of the Normalized Difference Vegetation Index (NDVI), differences in illumination conditions created by steep slopes and tall trees were minimized. Data fusion of the multispectral imagery, NDVI, and LiDAR-derived tree height data produced the highest percent accuracies using both the pixel-based (88.4%) and the object-based classifications (92.9%). These results demonstrate that either method will produce an acceptable level of accuracy, and that the availability of a near-infrared band to calculate NDVI is extremely important. The NDVI used in conjunction with the multispectral imagery helped to minimize issues with shadows caused by rugged terrain. Furthermore, LiDAR-derived tree heights were used to augment classification routines to achieve even greater accuracy; where shadows were too dark to produce meaningful NDVI values, the LiDAR-derived tree height data was instrumental in helping to distinguish trees from other land cover types. Both the pixel-based and the object-based approaches hold considerable promise for automated mapping and monitoring of the treeline ecotone; however, the pixel-based approach may be superior because it is more straightforward and easily replicable compared to the object-based approach. These treeline mapping efforts will enhance future ecological treeline research by producing more accurate detections of trees and estimations of treeline position, and will be instrumental in building time series of imagery for future scientists conducting change detection studies at treeline
