Abstract

Remote sensing data with high spatial resolution can potentially be used to estimate the biomass of individual tree crowns. As a first step, the location of each tree must be identified; this step is particularly important in dense forests. Total station (Pentax R-400) and a handheld differential Global Positioning System (GPS) were used in tandem to determine accurate locations of single trees. The locations were then combined with high spatial resolution remote sensing data to extract additional forest vegetation information. Tree location data were interpreted visually and were also the inputs for a ray-based automated method of crown delineation. Compared to field-collected crown data, the mean accuracy of the visually interpreted data was 65% in plot B3 (RMSE = 0.60) and 79% in plot B15; plot B15 did not have a dense canopy and had a Smaller Error Statistic (RMSE = 0.32). The ray model was less accurate. Crown size estimated from both the visually interpreted data and the ray-based model was usually smaller than that estimated from field data. One conclusion of this study is that crowns with a narrower density distribution are correlated with a higher error rate. The crown delineation method proposed in this study was shown to be feasible.

Keywords:

Canopy, GPS, individual tree, remote sensing, total station,

References

1. Ardila, J.P., W. Bijker, V.A. Tolpekin and A. Stein. 2012a. Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. Int. J. Appl. Earth Obs., 15: 57-69.
   CrossRef    
2. Ardila, J.P., W. Bijker, V.A. Tolpekin and A. Stein, 2012b. Quantification of crown changes and change uncertainty of trees in an urban environment. Isprs. J. Photogramm., 74: 41-55.
   CrossRef    
3. Brandtberg, T. and F. Walter, 1998. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Mach. Vision Appl., 11(2): 64-73./> CrossRef    
4. Brandtberg, T., T.A. Warner, R.E. Landenberger and J.B. McGraw, 2003. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens. Environ., 85(3): 290-303.
   CrossRef    
5. Chen, Q., D. Baldocchi, P. Gong and M. Kelly, 2006. Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm. Eng. Remote Sens., 72(8): 923-932.
   CrossRef    
6. Chopping, M., 2011. Remote Sensing Letters. Taylor and Francis Ltd.
   
7. Culvenor, D.S., 2003. Remote Sensing of Forest Environments. Springer, pp: 255-277.
   CrossRef    
8. Dralle, K. and M. Rudemo, 1996. Stem number estimation by kernel smoothing of aerial photos. Canadian J. Forest Res., 26(7): 1228-1236.
   CrossRef    
9. Dralle, K. and M. Rudemo, 1997. Automatic estimation of individual tree positions from aerial photos. Can. J. Forest Res., 27(11): 1728-1736.
   CrossRef    
10. Feng, Z.K. and S. Yao, 2007. Method on tree heights measurement using total station. Chinese Patent 101021416.
    
11. Feng, Z.K. and J. Wang, 2012. A SmartStation applied to tree measurements and method introduction. Chinese Patent 102466476A.
    
12. Fransson, J.E.S., F. Walter and L.M.H. Ulander, 2000. Estimation of forest parameters using CARABAS-II VHF SAR data. IEEE T. Geosci. Remote Sens., 38(2): 720-727.
    CrossRef    
13. Gobakken, T. and E. Naesset, 2005. Weibull and percentile models for lidar-based estimation of basal area distribution. Scand. J. Forest Res., 20(6): 490-502.
    CrossRef    
14. Gonzalez, P., G.P. Asner, J.J. Battles, M.A. Lefsky, K.M. Waring and M. Palace, 2010. Forest carbon densities and uncertainties from Lidar, QuickBird and field measurements in California. Remote Sens. Environ., 114(7): 1561-1575.
    CrossRef    
15. Gougeon, F.A., 1995. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote. Sens., 21: 274-288.
    CrossRef    
16. Gougeon, F.A. and D.G. Leckie, 1998. Forest regeneration: Individual tree crown detection techniques for density and stocking assessments. In Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry. Forest Service: Pacific Forestry Centre, Victoria, British Columbia, Canadia.
    PMCid:PMC508982    
17. Gougeon, F.A. and D.G. Leckie, 1999. Forest regeneration: Individual tree crown detection techniques for density and stocking assessments. Proceedings of the International Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry. Pacific Forestry Centre, Victoria, British Columbia, Canada.
    PMCid:PMC2326826    
18. Gougeon, F.A. and D.G. Leckie, 2006. The individual tree crown approach applied to Ikonos images of a coniferous plantation area. Photogramm. Eng. Remote Sens., 72(11): 1287-1297.
    CrossRef    
19. Holmgren, J. and A. Persson, 2004. Identifying species of individual trees using airborne laser scanner. Remote Sens. Environ., 90(4): 415-423.
    CrossRef    
20. Ke, Y.H. and L.J. Quackenbush, 2011. A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int. J. Remote. Sens., 32(17): 4725-4747.
    CrossRef    
21. Larsen, M. and M. Rudemo, 1998. Optimizing templates for finding trees in aerial photographs. Pattern Recogn. Lett., 19(12): 1153-1162.
    CrossRef    
22. Leckie, D.G., X. Yuan, D.P. Ostaff, H. Piene and D.A. MacLean, 1992. Analysis of high resolution multispectral MEIS imagery for spruce budworm damage assessment on a single tree basis. Remote Sens. Environ., 40(2): 125-136.
    CrossRef    
23. Leckie, D., F. Gougeon, D. Hill, R. Quinn, L. Armstrong and R. Shreenan, 2003. Combined high-density lidar and multispectral imagery for individual tree crown analysis. Can. J. Remote Sens., 29(5): 633-649.
    CrossRef    
24. Levesque, J. and D.J. King, 2003. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health. Remote. Sens. Environ., 84(4): 589-602.
    CrossRef    
25. Li, S., J.T. Kwok and Y. Wang, 2002. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inform. Fusion, 3(1): 17-23.
    CrossRef    
26. Liu, X.S., J.W. Huang and H.B. Ju, 2010. Research progress in the methods and applications of individual tree crown's automatic extraction by high spatial resolution remote sensing. J. Zhejiang Forestry College, 27(1): 126-133. (In Chinese)
    
27. Lu, D.S., S. Hetrick and E. Moran, 2010. Land cover classification in a complex urban-rural landscape with quickbird imagery. Photogramm. Eng. Remote Sens., 76(10): 1159-1168.
    CrossRef    
28. Lu, D.S., M. Batistella, E. Moran and E.E. de Miranda, 2008. A comparative study of landsat TM and SPOT HRG images for vegetation classification in the Brazilian amazon. Photogramm. Eng. Remote Sens., 74(3): 311-321.
    CrossRef    PMid:19789716 PMCid:PMC2752889    
29. Mallinis, G., N. Koutsias, M. Tsakiri-Strati and M. Karteris, 2008. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. Isprs J. Photogramm., 63(2): 237-250.
    CrossRef    
30. Maltamo, M., K. Eerikainen, P. Packalen and J. Hyyppa, 2006. Estimation of stem volume using laser scanning-based canopy height metrics. Forestry, 79(2): 217-229.
    CrossRef    
31. Næsset, E., 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ., 80(1): 88-99.
    CrossRef    
32. Pouliot, D.A., D.J. King, F.W. Bell and D.G. Pitt, 2002. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sens Environ., 82(2-3): 322-334.
    CrossRef    
33. Santoso, H., T. Gunawan, R.H. Jatmiko, W. Darmosarkoro and B. Minasny, 2011. Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precis Agr., 12(2): 233-248.
    CrossRef    
34. Song, C.H., M.B. Dickinson, L.H. Su, S. Zhang and D. Yaussey, 2010. Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons. Remote Sens. Environ., 114(5): 1099-1107.
    CrossRef    
35. Ulfarsson, M.O., J.A. Benediktsson and J.R. Sveinsson, 2003. Data fusion and feature extraction in the wavelet domain. Int. J. Remote Sens., 24(20): 3933-3945.
    CrossRef    
36. Uuttera, J., A. Haara, T. Tokola and M. Maltamo, 1998. Determination of the spatial distribution of trees from digital aerial photographs. Forest Ecol. Manage., 110(1-3): 275-282.
    CrossRef    
37. Wang, L., P. Gong and G.S. Biging. 2004. Individual tree-crown delineation and treetop detection high-spatial-resolution aerial imagery. Photogrammetric Eng. Remote Sens., 70(3): 351-357.
    CrossRef    
38. Wu, J.A. and D.L. Peng. 2010. Tree-crown information extraction of farmland returned to forests using quickbird image based on object-oriented approach. Spectrosc Spect Anal., 30(9): 2533-2536.
    
39. Wulder, M., K.O. Niemann and D.G. Goodenough. 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sens Environ., 73(1): 103-114.
    CrossRef    
40. Xu, W.H., Z.K. Feng and Z.F. Su, 2013. Development and experiment of handheld digitalized multi-functional forest measurement gun. Trans. Chinese Soc. Agri. Eng., 29(3): 90-99 (In Chinese).
    
41. Yan, F., M.R. Ullah, Y.X. Gong, Z.K. Feng, Y. Chowdury and L.L. Wu. 2012. Use of a no prism total station for field measurements in Pinus tabulaeformis Carr. stands in China. Biosyst. Eng., 113(3): 259-265.
    CrossRef    
42. Yu, H.H., S.S. Chen and Y. He, 2004. Study on GPS orientation experiment and method of improving location precision. J. Zhejiang Univ., 30(6): 662-667. (In Chinese)
    
43. Zhang, H.C., J.Q. Zheng and H.P. Zhou, 2011. Positioning accuracy analysis of RBN DGPS applied in precision forestry. Trans. CSAE, 27(7): 210-214.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.