Mapping riparian habitat using a combination ofremote-sensing techniques

Abstract

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