Analysis of the factors affecting lidar dtm accuracy in a steep shrub area

International Journal of Digital Earth

Volumn 4, Issue 6, 2011, Pages 521-538

  Estornell, J ^a   Ruiz, L A ^a   Velazquez Marti B ^a   Hermosilla, T ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

3 d representation; Digital earth; Digital terrain model (dtm); Filtering; Lidar

Indexed keywords

FILTRATION; INPUT OUTPUT PROGRAMS; ITERATIVE METHODS; LANDFORMS; MEAN SQUARE ERROR; VEGETATION;

3-D REPRESENTATION; CONTINUOUS IMPROVEMENTS; DIGITAL EARTH; DIGITAL TERRAIN MODEL; ITERATIVE ALGORITHM; LIGHT DETECTION AND RANGING; OPTIMAL PARAMETER; ROOT MEAN SQUARE ERRORS;

OPTICAL RADAR;

ACCURACY ASSESSMENT; DETECTION METHOD; ERROR ANALYSIS; LIDAR; SATELLITE DATA; SATELLITE IMAGERY; SHRUB;

EID: 80052652888     PISSN: 17538947     EISSN: 17538955     Source Type: Journal    
DOI: 10.1080/17538947.2010.533201     Document Type: Article

References (38)

1. Ahokas, E., Kaartinen, H., and Hyyppä, J., 2003. A quality assessment of airborne laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34 (3/W13), 6.
2. Baltsavias, E.P., 1999. Airborne laser scanning: existing systems and firms and other resources. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 164-198.
3. Clark, M.L., Clark, D.B., and Roberts, D.A., 2004. Small-footprint LiDAR estimation of subcanopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment, 91, 68-89.
4. Cobby, D.M., Mason, D.C., and Davenport, I.J., 2001. Image processing of airborne scanning laser altimetry data for improved river flood modeling. ISPRS Journal of Photogrammetry and Remote Sensing, 56, 121-138.
5. Davenport, I.J., et al., 2000. Improving bird population models using airborne remote sensing. International Journal of Remote Sensing, 21, 2705-2717.
6. a Semana Geomática Barcelona.
7. Estornell, J., Ruiz, L.A., and Velazquez-Marti, B., 2010. Study of shrub cover and height using Lidar data in a mediterranean area. Forest Science (in press).
8. Goodchild, M.F., 2008. The use cases of digital earth. International Journal of Digital Earth, 1 (1), 31-42.
9. Hodgson, M.E., et al., 2005. An evaluation of LiDAR-derived elevation and terrain slope in leaf-off conditions. Photogrammetric Engineering and Remote Sensing, 71 (7), 817-823.
10. Hollaus, M., et al., 2006. Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing, 60, 323-338.
11. Hopkinson, C., et al., 2005. Vegetation class dependent errors in LiDAR ground elevation and canopy height estimates in a boreal wetland environment. Canadian Journal of Remote Sensing, 31, 191-206.
12. Hyyppä, H., et al., 2005. Factors affecting the quality of DTM generation in forested areas. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, 3/W19, 85-90.
13. Hyyppä, J., et al., 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29, 1339-1366.
14. Hyyppä, J., et al., 2000. Elevation accuracy of laser scanning-derived digital terrain and target models in forest environment. In: Proceedings of 20th EARSeL symposium and workshops, 14-17 June 2000, Dresden, Germany, 139-147.
15. Kraus, K. and Pfeifer, N., 1998. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53, 193-203.
16. Lefsky, M., et al., 1999. LiDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment, 70, 339-361.
17. Maltamo, M., et al., 2004. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90, 319-330.
18. Mutlu, M., et al., 2008. Mapping surface fuel models using LiDAR and multispectral data fusion for fire behavior. Remote Sensing of Environment, 112, 274-285.
19. Næsset, E., 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80, 88-99.
20. Næsset, E., 2004. Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19, 554-557
21. Patenaude, G., et al., 2004. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment, 93, 368-380.
22. Popescu, S.C., 2007. Estimating biomass of individual pine trees using airborne LiDAR. Biomass & Bioenergy, 31, 646-655.
23. Popescu, S.C., Wynne, R.H., and Nelson, R.F., 2002. Estimating plot-level tree heights with LiDAR: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71-95.
24. Raber, G.T., et al., 2002. Creation of digital terrain models using an adaptive lidar vegetation point removal process. Photogrammetric Engineering and Remote Sensing, 68, 1307-1315
25. Rango, A., et al., 2000. Morphological characteristics of shrub coppice dunes in desert grasslands of southern New Mexico derived from scanning LIDAR. Remote Sensing of Environment, 74, 26-44.
26. Riaño, D., et al., 2007. Estimation of shrub height for fuel-type mapping combining airborne lidar and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16, 341-348.
27. Shrestha, R., et al., 2005. Airborne laser swath mapping: quantifying changes in sandy beaches over time scales of weeks to years. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 222-232.
28. Sithole, G. and Vosselman, G., 2004. Experimental comparison of filter algorithms for bare- Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 85-101.
29. Sohn, G. and Dowman, I., 2007. Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 43-63.
30. Streutker, D.R. and Glenn, N.F., 2006. Lidar measurement of sagebrush steppe vegetation heights. Remote Sensing of Environment, 102, 135-145.
31. Su, J. and Bork, E., 2006. Influence of vegetation, slope, and Lidar sampling angle on DEM accuracy. Photogrammetric Engineering & Remote Sensing, 72, 1265-1274.
32. Suárez, J.C., et al., 2005. Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Computers & Geosciences, 31 (2), 253-262.
33. Velázquez-Martí, B., et al., 2010. Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, 259, 875-882.
34. Wack, R. and Wimmer, A., 2002. Digital terrain models from airborne laser scanner data - a grid based approach. International Archives of Photogrammetry and Remote Sensing, 35 (3B), 293-296.
35. Wang, C., et al., 2009. Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments. IEEE Transactions on Geoscience and Remote Sensing, 47, 2014-2023.
36. Yu, X., et al., 2004. Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90, 451-462.
37. Zhang, K., et al., 2003. A progressive morphological filter for removing nonground measurements from airborne LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41, 872-882.
38. Zhang, Y. and Guindon, B., 2009. Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover. International Journal of Digital Earth, 2 (2), 89-108.