期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - 4/C7
出版社：Copernicus Publications
摘要：This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e. digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of generalizing classification rules, which may lead to the development of automated mapping approaches. The LiDAR data were captured in May 2005 with 1.6 m point spacing and included first and last returns and an intensity layer. The returns were classified as ground and non-ground points by the data provider. The data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were obtained in April 2008. The field data were used both for calibration of the mapping routines and to validate the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e. urbanised areas, agricultural areas, and hilly forested areas. Results show that mapping streambed extent (R 2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R 2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA. This work lays the foundation for automatic feature extraction of biophysical properties of riparian zones to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies
