Use of remote sensing for investigating riparian shrub structures

• Autorzy: Kaluza T. , Tymkow P. , Strzelinski P.
• Języki publikacji: EN

Abstrakty

EN

Our paper presents a comparison of remote-sensing methods for evaluation of riparian shrub structures. Investigation involved three methods: leaf area index measurements (LAI-2000), hemispheric photography, and terrestrial laser scanning. Direct measurements using a digital slide caliper were chosen as a reference method. This paper firstly reviews the methodology of laboratory and field research. Additionally, an original method of calculating the volume and surface area of plants on the basis of laser scanning data has been proposed. The second part of paper concentrates on the comparison of plant structure coefficients determined with all investigated devices. In the case of the LAI Ring 5 index from LAI-2000 measurement as well as canopy openness of shrub (P), obtained on the basis of hemispheric photos, high linear dependence between them and cross-section covering coefficient ϖp from direct investigation were obtained. Volume and surface area of plants calculated on the basis of laser scanning in micro- and macrostructural approach were also compared to the analogous parameters obtained from direct measurements. In this case, the results strongly depend on the modeling parameters, but the proposed method seems to be prospective in this task.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2012
• Tom: 21
• Numer: 1
• Opis fizyczny: p.115-122,fig.,ref.

Twórcy

autor

Kaluza T.

• Department of Hydraulic Engineering, Faculty of Reclamation and Environmental Engineering, Poznan University of Life Sciences, Piatkowska 94, 60-649 Poznan, Poland

autor

Tymkow P.

autor

Strzelinski P.

Bibliografia

• 1. DARBY S.E. Effect of riparian vegetation on resistance and flood potential. Journal of Hydraulic Engineering, 125, 443, 1999.
• 2. SAND-JENSEN K. Drag forces on common plant species in temperate streams: consequences of morphology, velocity and biomass. Hydrobiologia 610, 307, 2008.
• 3. HUTHOFF F., AUGUSTIJN D. Sensitivity analysis of floodplain roughness in 1d flow. [In:] Proceedings of the 6th International Conference on Hydroinformatics, pp. 301-308, 2004.
• 4. PASCHE E. Turbulence mechanisms in natural rivers and the mathematical possibilities of their detection. Mitt. Institut für Wasserbau und Wasserwirtschaft, RWTH Aachen, Heft 52, Aachen, 1984 [In German].
• 5. DVWK. Hydraulic calculation of rivers. DVWK-Merkblatter 220 zur Wasserwirtschaft. Verlag Paul Parey, 1991 [In German].
• 6. KAISER W. Flow resistance in channels with flow through wooded banks. Wasserbau-Mitteilungen der TH Darmstadt, 1984 [In German].
• 7. BAPTIST M.J., BABOVIC V., RODRÍGUEZ UTHURBURU J., KEIJZER M., UITTENBOGAARD R.E., MYNETT A., VERWEY A. On inducing equations for vegetation resistance. Journal of Hydraulic Research, 45, (4), 435, 2007.
• 8. CHOPPING M. Terrestrial Applications of Multiangle Remote Sensing. [In:] Advances in Land Remote Sensing: System, Modeling, Inversion and Applications. Liang S. ed. Springer-Verlag, pp. 95-144, 2008.
• 9. JONCKHEERE I., FLECK S., NACKAERTS K., MUYS B., COPPIN P., WEISS M., BARET F. Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology 121, 19, 2004.
• 10. STRAATSMA M.W., MIDDELKOOP H. Airborne laser scanning as a tool for lowland floodplain vegetation monitoring. Hydrobiologia 565, 87, 2006.
• 11. DONCKER L., TROCH P., VERHOEVEN R., BAL K., DESMET N., MEIRE P. Relation between resistance characteristics due to aquatic weed growth and the hydraulic capacity of the river AA. River Research and Applications 25, 1287, 2009.
• 12. HOUGHTON R.A. Aboveground forest biomass and the global carbon balance. Glob. Chang. Biol. 11, 945, 2005.
• 13. HESE S., LUCHT W., SCHMULLIUS C., BARNSLEY M., DUBAYAH R., KNORR D., NEUMANN K., RIEDEL T., SCHRÖTER K. Global biomass mapping for an improved understanding of the CO₂ balance. The Earth observation mission carbon-3D. Remote Sens. Environ. 94, 94, 2005.
• 14. NÆSSET E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 80, 88, 2002.
• 15. LICHTI D., GORDON D.J., STEWART M.P. Groundbased laser scanners: operation, systems and applications. Geomatica, 56, 21, 2002.
• 16. ZHAO K., POPESCU S., NELSON R. Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113, 182, 2009.
• 17. HAWBAKER T.J., KEULER N.S., LESAK A.A., GOBAKKEN T., CONTRUCCI K., RADELOFF V.C. Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. J. Geophys. Res., 114, G00E04. 2009.
• 18. NÆSSET E. Practical large-scale forest stand inventory using a small footprint airborne scanning laser. Scand. J. Forest Res. 19, 164, 2004.
• 19. POPESCU S.C. Estimating biomass of individual pine trees using airborne lidar. Biomass Bioenerg. 31, 646, 2007.
• 20. HOSOI F., OMASA K. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning Lidar. IEEE Trans. Geosci. Remote Sens. 44, 3610, 2006.
• 21. KAASALAINEN S., KROOKS A., KUKKO A., KAARTINEN H. Radiometric calibration of terrestrial laser scanners with external reference targets. Remote Sens. 1, 144, 2009.
• 22. LOVELL J.L., JUPP D.L.B., CULVENOR D.S., COOPS N.C. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can. J. Remote Sens. 29, 607, 2003.
• 23. JUPP D.L.B., CULVENOR D.S., LOVELL J.L., NEWNHAM G.J., STRAHLER A.H., WOODCOCK C.E. Estimating forest LAI profiles and structural parameters using a ground based laser called “Echidna”. Tree Physiol. 29, 171, 2008.
• 24. WELLES J.M., COHEN S. Canopy structure measurement by gap fraction analysis using commercial instrumentation. Journal of Experimental Botany 47, 1335, 1996.
• 25. TRICHON V., WALTER J-M.N., LAUMONIER Y. Identifying spatial patterns in the tropical rain forest structure using hemispherical photographs. Plant Ecology 137, 1998.
• 26. GORTE B., PFEIFER N. Structuring laser-scanned trees using 3D mathematical morphology. International Archives of Photogrammetry and Remote Sensing. Vol. XXXV, B5, pp. 929-933, 2004.
• 27. THIES M., PFEIFER N., WINTERHALDER D., GORTE B.G.H. Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scandinavian Journal of Forest Research, 19, 571, 2004.
• 28. GORTE B. Skeletonization of laser-scanned trees in the 3d raster domain. [In:] Lecture notes in geoinformation and cartography. Innovations in 3D Geo Information Systems. Springer Berlin Heidelberg, pp. 371-380, 2006.
• 29. O’ROURKE J. Computional Geometry in C. Cambridge University Press, 1988.
• 30. CHEN J.M., GOVIND A., SONNENTAG O., ZHANG Y ., BARR A., AMIRO B. Leaf area index measurements at Fluxnet-Canada forest sites. Agricultural and Forest Meteorology. 140, 257, 2006.
• 31. ZHANG Y., CHEN J.M., MILLER J. Determining exposure of digital hemispherical photographs for leaf area index estimation. Agric. For. Meteorol. 133, 166, 2005.
• 32. SANG W.G., CHEN S., LI G.Q. Dynamics of leaf area index and canopy openness of three forest types in a warm temperate zone. Frontiers of Forestry in China 4, 416, 2008.
• 33. LOUDERMILK E.L., HIERS J.K., O’BRIEN J.J., MITCHELL R.J., SINGHANIA A., FERNANDEZ J.C., CROPPER W.C., SLATTON K.C. Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics. International Journal of Wildland Fire. 18, (6) 676, 2009.