Mapping riparian habitat using a combination ofremote-sensing techniques

摘要: Light detection and ranging lidar object-oriented classification OOC can be used to overcome the shortcomings of traditional pixel-based PBC coarse spatial resolution data, such as Landsat for habitat mapping in riparian zones. The purposes this study were investigate methods classify multispectral data mapping, identify major components two target species. based on Decision Tree Classification DTC was carried out by merging vertical from spectral high-resolution imagery. Our results showed an overall accuracy 88.2%. In particular, small continuous types, short tall grasses, rock outcrop gravel, riffles, improved compared with methods. patches paths each species identified incorporating point field survey outcomes image classification. demonstrated that proposed methodology successfully identification restoration fragmented habitats, offer opportunity obtain high accuracies microhabitat dynamic riverine areas.