Open Access
Knowl. Managt. Aquatic Ecosyst.
Number 410, 2013
Article Number 10
Number of page(s) 15
Published online 11 September 2013
  • Aguiar F.C., Fernandes M.R. and Ferreira M.T., 2011. Riparian vegetation metrics as tools for guiding ecological restoration in riverscape set. Knowl. Managt. Aquatic Ecosyst., 402, 21. [CrossRef] [EDP Sciences] [Google Scholar]
  • Antonarakis A.S., Richards K.S. and Brasington J., 2008a. Object-based land cover classification using airborne LiDAR. Remote Sens. Environ., 112, 2988–2998. [CrossRef] [Google Scholar]
  • Antonarakis A.S., Richards K.S., Brasington J., Bithell M. and Muller E., 2008b. Retrieval of vegetative fluid resistance terms for rigid stems using airborne LiDAR. J. Geophys. Res., 113, G02S07. [CrossRef] [Google Scholar]
  • Arroyo LA., Johansen K., Armston J. and Phinn S., 2010. Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas. For. Ecol. Manag., 259, 598–606. [CrossRef] [Google Scholar]
  • Axelsson P., 1999. Processing of laser scanner data—algorithms and applications. ISPRS-J. Photogramm. Remote Sens., 54, 138–147. [Google Scholar]
  • Brooks R., McKenney-Easterling M., Brinson M., Rheinhardt R., Havens K., O’Brien D., Bishop J., Rubbo J., Armstrong B. and Hite J., 2009. A Stream–Wetland–Riparian (SWR) index for assessing condition of aquatic ecosystems in small watersheds along the Atlantic slope of the eastern U.S. Environ. Monit. Assess., 150, 101–117. [CrossRef] [PubMed] [Google Scholar]
  • Carbonneau P.E. and Piégay H., 2012. Fluvial Remote Sensing for Science and Management. John Wiley & Sons, 440 p. [Google Scholar]
  • Carbonneau P.E., Dugdale S.J. and Clough S., 2009. An Automated georeferencing tool for watershed scale fluvial remote sensing. River Res. App., 26, 650–658. [Google Scholar]
  • Carbonneau P.E., Piégay H., Lejot J., Dunford R. and Michel K., 2012. Hyperspatial imagery in riverine environments. In: Carbonneau R.E. and Piégay H. (eds.), Fluvial Remote Sensing for Science and Management, Wiley, 163–191. [Google Scholar]
  • Coroi M., Sheehy Skeffington M., Giller P., Gormally M. and O’Donovan G., 2006. Using GIS in the mapping and analysis of the landscape and vegetation patterns along streams in Southern Ireland. Biol. Environ.-Proc. R. Irish Acad., 106 B, 287–300. [CrossRef] [Google Scholar]
  • Debruxelles N., Claessens H., Lejeune P. and Rondeux J., 2009. Design of a watercourse and riparian strip monitoring system for environmental management. Environ. Monit. Assess., 156, 435–450. [CrossRef] [PubMed] [Google Scholar]
  • Dowling R. and Accad A., 2003. Vegetation classification of the riparian zone along the Brisbane River, Queensland, Australia, using light detection and ranging (LiDAR) data and forward looking digital video. Can. J. Remote Sens., 29, 556–563. [CrossRef] [Google Scholar]
  • Dufour S. and Piégay H., 2009. From the myth of a lost paradise to targeted river restoration: forget natural references and focus on human benefits. River Res. App., 25, 568–581. [Google Scholar]
  • Dufour S., Muller E., Straastma M. and Corgne S., 2012. Image uses for riparian vegetation study and management. In: Carbonneau R.E. and Piégay H. (eds.), Imagery and river management: Recent advances and challenging issues, Wiley, 215–239. [Google Scholar]
  • Dunford R., Michel K., Gagnage M., Piégay H. and Trémelo M.L., 2009. Potential and constraints of UAV technology for the characterisation of Mediterranean riparian forest. Int. J. Remote Sens., 30, 4915–4935. [CrossRef] [Google Scholar]
  • Farid A., Goodrich D.C. and Sorooshian S., 2006. Using Airborne Lidar to Discern Age Classes of Cottonwood Trees in a Riparian Area. West. J. Appl. For., 21, 149–158. [Google Scholar]
  • Feld C.K., 2012. Response of three lotic assemblages to riparian and catchment-scale land use: implications for designing catchment monitoring programmes. Freshwater Biol., 58, 715–729. [CrossRef] [Google Scholar]
  • Fernandes M.R., Aguiar F.C., Ferreira M.T. and Cardoso Pereira J.M., 2013. Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery. Int. J. Remote Sens., 33, 2375–2401. [CrossRef] [Google Scholar]
  • Forget G., Carreau C., Le Coeur D. and Bernez I., 2013. Ecological Restoration of Headwaters in a Rural Landscape (Normandy, France): A Passive Approach Taking Hedge Networks into Account for Riparian Tree Recruitment. Restor. Ecol., 21, 96–104. [Google Scholar]
  • Forzieri G., Moser G., Vivoni E., Castelli F. and Canovaro F., 2010. Riparian Vegetation Mapping for Hydraulic Roughness Estimation Using Very High Resolution Remote Sensing Data Fusion. J. Hydraul. Eng., 136, 855–867. [CrossRef] [Google Scholar]
  • Fritz T., Eineder M., Lachaise M., Roth A., Breit H., Schättler B. and Huber M., 2007. TerraSAR-X Ground Segment. Level 1b Product Format Specification. Annex B. DLR. Doc. TX-GS-DD-3307. [Google Scholar]
  • Geerling G.W., Vreeken-Buijs M.J., Jesse P., Ragas A.M.J. and Smits A.J.M., 2009. Mapping river floodplain ecotopes by segmentation of spectral (CASI) and structural (LiDAR) remote sensing data. River Res. App., 25, 895–813. [CrossRef] [Google Scholar]
  • Goetz S.J., 2006. Remote sensing of riparian buffers: past progress and future prospects. J. Am. Water Resour. Assoc., 42, 133–143. [CrossRef] [Google Scholar]
  • Hansen A. and Rotella J.J., 2000. Bird responses to forest fragmentation. In: Knight R.L., Smith F.W., Romme W.H. and Buskirk S.W. (eds.), Forest Fragmentation in the Southern Rockies. Boulder, University Press of Colorado, 201–219. [Google Scholar]
  • Hardin P.J. and Jensen R.R., 2011. Introduction–Small-Scale Unmanned Aerial Systems for Environmental Remote Sensing. GISci. Remote Sens., 48, 1–3. [CrossRef] [Google Scholar]
  • Hervouet A., Dunford R., Piegay H., Belletti B. and Tremelo M.L., 2011. Analysis of Post-flood Recruitment Patterns in Braided Channel Rivers at Multiple Scales Based on an Image Series Collected by Unmanned Aerial Vehicles, Ultra-light Aerial Vehicles, and Satellites. GISci. Remote Sens., 48, 50–73. [CrossRef] [Google Scholar]
  • Johansen K., Tiede D., Blaschke T., Arroyo L.A. and Phinn S., 2011. Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia. Remote Sens., 3, 1139–1156. [CrossRef] [Google Scholar]
  • Johansen K., Coops N.C., Gergel S.E. and Stange Y., 2007a. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens. Environ., 110, 29–44. [Google Scholar]
  • Johansen K., Phinn S. and Witte C., 2010. Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costs. Remote Sens. Environ., 114, 2679–2691. [CrossRef] [Google Scholar]
  • Johansen K., Phinn S., Dixon I., Douglas M. and Lowry J., 2007b. Comparison of image and rapid field assessments of riparian zone condition in Australian tropical savannas. For. Ecol. Manag., 240, 42–60. [CrossRef] [Google Scholar]
  • Keller P., Kreylos O., Vanco M., Hering-Bertram M., Cowgill E.S., Kellogg L.H., Hamann B. and Hagen H., 2011. Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets. In: Pascucci V., Tricoche X., Hagen H. and Tierny J. (eds.), Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications, Springer-Verlag, Heidelberg, Germany, 179–192. [Google Scholar]
  • Laliberte A.S. and Rango A., 2011. Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands. GISci. Remote Sens., 48, 4–23. [CrossRef] [Google Scholar]
  • Lee J.S. and Pottier E., 2009. Polarimetric Radar Imaging: From basics to applications. CRC Press, Taylor & Francis, 397 p. [Google Scholar]
  • Lee J.S., 1981. Refined filtering of image noise using local statistics. Comput. Graph. Image Process., 4, 380–389. [CrossRef] [Google Scholar]
  • Lefsky M.A., Cohen W.B., Parker G.G. and Harding D.J., 2002. Lidar remote sensing for ecosystem studies. Bioscience, 52, 19–30. [CrossRef] [Google Scholar]
  • Lejot J., Delacourt C., Piégay H., Trémélo M.L. and Fournier T., 2007. Very high spatial resolution imagery for reconstructing channel bathymetry and topography from an unmanned controlled platform. Earth Surf. Process. Landf., 32, 1705–1725. [CrossRef] [Google Scholar]
  • Lelong C.D., Burger P., Jubelin G., Roux B., Labbé S. and Baret F., 2008. Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots. Sensors, 8, 3557–3585. [CrossRef] [Google Scholar]
  • Lobo A., 2009. Testing low-altitude infrared digital photography from a mini-UAV to retrieve information for biological conservation. Reports on Environmental Sciences 9, available on [Google Scholar]
  • Lopez-Sanchez J.M., Ballester-Berman J.D. and Hajnsek I., 2009. Rice monitoring in Spain by means of time series of TerraSAR-X Dual-Pol images. Proc. of the 4th Int. Workshop on Science and Applications of SAR Polarimetry – PolInSAR 2009, Frascati, Italy (ESA SP-668, April 2009). [Google Scholar]
  • Malanson G.P., 1993. Riparian landscapes. Cambridge University Press, Cambridge, UK, 296 p. [Google Scholar]
  • Marechal C., Pottier E., Hubert-Moy L. and Rapinel S., 2012. One Year Wetland Survey Investigations from Quad-Pol RADARSAT-2 Time-Series SAR Images. Can. J. Remote Sens., 38, 240–252. [CrossRef] [Google Scholar]
  • Munné A., Prat N., Sola C. and Bonada N., and Rieradevell M., 2003. A simple field method for assessing the ecological quality of riparian habitat in rivers and streams: a QBR index. Aquat. Conserv. Mar. Freshw. Ecosyst., 13, 147–163. [CrossRef] [Google Scholar]
  • Naiman R.J., Deìcamps H. and McCLain M., 2005. Riparia, ecology, conservation, and management of streamside communities. Academic Press, Elsevier, San Diego 430 p. [Google Scholar]
  • Ormerod SJ., 2004. A golden age of river restoration science? Aquat. Conserv. Mar. Freshw. Ecosyst., 14, 543–549. [CrossRef] [Google Scholar]
  • Palmer M.A., Bernhardt E.S., Allan J.D., Lake P.S., Alexander G., Brooks S., Carr J., Clayton S., Dahm C., Follstad Shah J., Galat D.J., Gloss S., Goodwin P., Hart D.H., Hassett B., Jenkinson R., Kondolf G.M., Lave R., Meyer J.L., O’Donnell T.K., Pagano L., Srivastava P. and Sudduth E., 2005. Standards for ecologically successful river restoration. J. Appl. Ecol., 42, 208–217. [Google Scholar]
  • Raven P.J., Holmes N.T.H., Dawson F.H. and Everard M., 1998. Quality assessment using River Habitat Survey data. Aquat. Conserv., 8, 477–499. [Google Scholar]
  • Reitberger J., Schnörr C., Krzystek P., and Stilla U., 2009. 3D segmentation of single trees exploiting full waveform LiDAR data. ISPRS-J. Photogramm. Remote Sens., 64, 561–574. [CrossRef] [Google Scholar]
  • Rheinhardt R., Brinson M., Brooks R., McKenney-Easterling M., Masina Rubbo J., Hite J., Armstrong B., 2007. Development of a reference-based method for identifying and scoring indicators of condition for coastal plain riparian reaches. Ecol. Indic., 7, 339–361. [CrossRef] [Google Scholar]
  • Stefanik K.S., Gassaway J.C., Kochersberger K. and Abbott A.L., 2011. UAV-Based Stereo Vision for Rapid Aerial Terrain Mapping. GISci. Remote Sens., 48, 24–49. [CrossRef] [Google Scholar]
  • St-Onge B., 2004. L’altimétrie laser à balayage. Revue Internationale de Géomatique, 14, 531–558. [CrossRef] [Google Scholar]
  • Tormos T., Kosuth P., Durrieu S., Dupuy S., Villeneuve B. and Wasson J.G., 2012. Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data. Int. J. Remote Sens., 33, 4603–4633. [CrossRef] [Google Scholar]
  • Tormos T., Kosuth P., Durrieu S., Villeneuve B. and Wasson J.G., 2011. Improving the quantification of land cover pressure on stream ecological status at the riparian scale using High Spatial Resolution Imagery. Phys. Chem. Earth, 36, 549–559. [Google Scholar]
  • Töyrä J., and Pietroniro A., 2005. Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sens. Environ., 97, 174–191. [CrossRef] [Google Scholar]
  • Turner D.P., Cohen W.B., Kennedy R.E., Fassnacht K.S. and Briggs J.M., 1999. Relationship between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens. Environ., 70, 52–68. [CrossRef] [Google Scholar]
  • Vierling K.T., Vierling L.A., Gould W.A., Martinuzzi S. and Clawges R.M., 2008. Lidar: shedding new light on habitat characterization and modeling. Front. Ecol. Environ., 6, 90–98. [CrossRef] [Google Scholar]
  • Wang S.Y., Cui X.M., Yuan D.B., Jin J.J. and Zhang Q., 2012. Classification of Airborne Lidar Data by Echo. Key Eng. Mater., 500, 696–700. [CrossRef] [Google Scholar]
  • Wasson J.G., Villeneuve B., Lital A., Murray-Bligh J., Dobiasova M., Bacikova S., Timm H., Pella H., Mengin N. and Chandesris A., 2010. Large-scale relationships between basin and riparian land cover and the ecological status of European rivers. Freshwater Biol., 55, 1465–1482. [Google Scholar]
  • Wiederkehr E., Dufour S. and Piégay H., 2010. Localisation et caractérisation des géomorphosites fluviaux à l’échelle des réseaux hydrographiques, exemples d’applications géomatiques dans le bassin de la Drôme. Géomorphologie : relief, processus, environnement, 2, 175–188. [CrossRef] [Google Scholar]
  • Yang X., 2007. Integrated use of remote sensing and geographic information systems in riparian vegetation delineation and mapping. Int. J. Remote Sens., 28, 353–370. [CrossRef] [Google Scholar]

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