Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia
Open Access
- 30 May 2011
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 3 (6), 1139-1156
- https://doi.org/10.3390/rs3061139
Abstract
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 developing transferable and automated mapping approaches. LiDAR 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 used for both calibration of the mapping routines and validation of 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., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies.This publication has 29 references indexed in Scilit:
- Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costsRemote Sensing of Environment, 2010
- Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysisEcological Indicators, 2010
- Object based image analysis for remote sensingISPRS Journal of Photogrammetry and Remote Sensing, 2010
- Quantifying indicators of riparian condition in Australian tropical savannas: integrating high spatial resolution imagery and field survey dataInternational Journal of Remote Sensing, 2008
- High spatial resolution remote sensing for environmental monitoring and management preface:Journal of Spatial Science, 2008
- Comparison of image and rapid field assessments of riparian zone condition in Australian tropical savannasForest Ecology and Management, 2007
- IKONOS imagery for the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA)Remote Sensing of Environment, 2003
- A multi-scale segmentation/object relationship modelling methodology for landscape analysisEcological Modelling, 2003
- Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley catchment, Queensland, AustraliaLandscape and Urban Planning, 2002
- The Ecology of Interfaces: Riparian ZonesAnnual Review of Ecology and Systematics, 1997