A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 110, Issue , 2015, Pages 34-47

  Liu, Tao ^a   Im, Jungho ^b   Quackenbush, Lindi J ^c  

^a UNIVERSITY OF FLORIDA   (United States)

^b Ulsan National Institute of Science and Technology   (South Korea)

^c STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Accuracy assessment; ITCD; LiDAR; Pseudo waveform; Random forest; Wavelet analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; FISHERIES; FORESTRY; LEARNING SYSTEMS; OPTICAL RADAR; WAVELET ANALYSIS;

ACCURACY ASSESSMENT; BOUNDARY CLASSIFICATION; BOUNDARY REFINEMENTS; INDIVIDUAL TREE CROWN; ITCD; LIGHT DETECTION AND RANGING; RANDOM FORESTS; WAVE FORMS;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; BOUNDARY LAYER; CANOPY ARCHITECTURE; CLASSIFICATION; FOREST CANOPY; GAUSSIAN METHOD; LIDAR; MACHINE LEARNING; WATERSHED; WAVEFORM ANALYSIS; WAVELET ANALYSIS;

ADIRONDACK PARK; NEW YORK [UNITED STATES]; UNITED STATES;

EID: 84945250026     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2015.10.002     Document Type: Article

References (72)

1. Becker, G., 2001. Precision forestry in Central Europe - new perspectives for a classical management concept. Keynote Presentation, First International Precision Forestry Symposium. University of Washington, Seattle, WA, June 17-20, pp. 7-9.
2. Berger G., Coifman R.R., Goldberg M. Removing noise from music using local trigonometric bases and wavelet packets. J. Audio Eng. Soc. 2012, 42.
3. Brandtberg T. Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets Syst. 2002, 132(3):371-387.
4. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
5. Burnett C., Blaschke T. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol. Model. 2003, 168:233-249.
6. Chen Q., Baldocchi D., Gong P., Kelly M. Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm. Eng. Remote Sens. 2006, 72:923-932.
7. Chen Y., Zhu X. An integrated GIS tool for automatic forest inventory estimates of Pinus radiate from LiDAR data. GISci. Remote Sens. 2013, 50:667-689.
8. Dralle K., Rudemo M. Automatic estimation of individual tree positions from aerial photos. Can. J. Forest Res. 1997, 27(11):1728-1736.
9. Duncanson L.I., Cook B.D., Hurtt G.C., Dubayah R.O. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sens. Environ. 2014, 154:378-386.
10. Ene L., Næsset E., Gobakken T. Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. Int. J. Remote Sens. 2012, 33:5171-5193.
11. Estornell J., Velazquez-Marti B., Lopez-Cortes I., Salazar D., Fernandez-Sarria A. Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data. GISci. Remote Sens. 2014, 51:17-29.
12. Eysn L., Hollaus M., Lindberg E., Berger F., Monnet J.M., Dalponte M., Pfeifer N. A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the Alpine space. Forests 2015, 6(5):1721-1747.
13. FAO & JRC, 2012. In: Lindquist, E.J., D'Annunzio, R., Gerrand, A., MacDicken, K., Achard, F., Beuchle, R., Brink, A., Eva, H.D., Mayaux, P., San-Miguel-Ayanz, J., Stibig, H.-J. (Eds.), Global Forest Land-use Change 1990-2005. FAO Forestry Paper No. 169. Food and Agriculture Organization of the United Nations and European Commission Joint Research Centre, Rome, FAO.
14. Ferreira M.P., Zanotta D.C., Zortea M., Korting T.S., Fonseca L.M., Shimabukuro Y.E., Souza Filho C.R. Automatic tree crown delineation in tropical forest using hyperspectral data. 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2014, 784-787. IEEE.
15. Farnum, P., 2001. Precision forestry - finding the context. First International Precision Forestry Symposium, June 17-20, 2001, Seattle, Washington, USA.
16. Ghimire B., Rogan J., Rodriguez Galinao V., Panday P., Neeti N. An evaluation of bagging, boosting, and random forests using land cover classification in Cape Cod, Massachusetts, USA. GISci. Remote Sens. 2012, 49:623-643.
17. Gleason C.J., Im J. A review of remote sensing of forest biomass and biofuel: options for small area applications. GISci. Remote Sens. 2011, 48:141-170.
18. Gleason C.J., Im J. A fusion approach for tree crown delineation from LiDAR data. Photogramm. Eng. Remote Sens. 2012, 78:679-692.
19. Gleason C., Im J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens. Environ. 2012, 125:80-91.
20. Gong B., Im J., Jensen J.R., Coleman M., Rhee J., Nelson E. Characterization of forest crops with a range of nutrient and water treatments using AISA hyperspectral imagery. GISci. Remote Sens. 2012, 49:463-497.
21. Gougeon F.A. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote Sens. 1995, 21(3):274-284.
22. Gougeon, F.A., Leckie, D.G., 2001. Individual tree crown image analysis - a step towards precision forestry. First International Precision Forestry Symposium, June 17-20, 2001, Seattle, Washington, USA.
23. Guo, L., Rivero, D., Seoane, J.A., Pazos, A., 2009. Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 177-184.
24. Hay, G.J., Marceau, D.J., 2006. Multiscale object-specific analysis (MOSA): an integrative approach for multiscale landscape analysis. In: Remote Sensing Image Analysis: Including the Spatial Domain, pp. 71-92.
25. Heinzel J., Koch B. Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int. J. Appl. Earth Obs. Geoinf. 2012, 18:101-110.
26. Hyyppä J., Inkinen M. Detecting and estimating attributes for single trees using laser scanner. Photogramm. J. Finland 1999, 16(2):27-42.
27. Hyyppä J., Kelle O., Lehikoinen M., Inkinen M. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans. Geosci. Remote Sens. 2001, 39(5):969-975.
28. Jing L., Hu B., Noland T., Li J. An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS J. Photogramm. Remote Sens. 2012, 70:88-98.
29. Jung J., Kim S., Hong S., Kim K., Kim E., Im J., Heo J. Effects of national forest inventory location error on forest carbon stock estimation using k-nearest neighbor algorithm. ISPRS J. Photogramm. Remote Sens. 2013, 81:82-92.
30. Kaartinen H., Hyyppä J., Yu X., Vastaranta M., Hyyppä H., Kukko A., et al. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens. 2012, 4:950-974.
31. Ke, Y., Quackenbush, L.J., 2007. Forest species classification and tree crown delineation using Quickbird imagery. In: Proceedings of the ASPRS 2007 Annual Conference, May 2007, pp. 7-11.
32. Ke, Y., Quackenbush, L.J., 2009. Individual tree crown detection and delineation from high spatial resolution imagery using active contour and hill-climbing methods. In: Proceedings of 2009 ASPRS Annual Conference, March 2009, pp. 9-13.
33. Ke Y., Quackenbush L.J. A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int. J. Remote Sens. 2011, 32:4725-4747.
34. Kim M., Im J., Han H., Kim J., Lee S., Shin M., Kim H. Landfast sea ice monitoring using multisensor fusion in the Antarctic. GISci. Remote Sens. 2015, 52:239-256.
35. Kim Y., Im J., Ha H., Choi J., Ha S. Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GISci. Remote Sens. 2014, 51:158-174.
36. Lahivaara T., Seppanen A., Kaipio J.P., Vauhkonen J., Korhonen L., Tokola T., Maltamo M. Bayesian approach to tree detection based on airborne laser scanning data. IEEE Trans. Geosci. Remote Sens. 2014, 52(5):2690-2699.
37. Leckie D., Jay C., Gougeon F., Sturrock R., Paradine D. Detection and assessment of trees with Phellinus weirii (laminated root rot) using high resolution multi-spectral imagery. Int. J. Remote Sens. 2004, 25:793-818.
38. Leckie D., Gougeon F., Hill D., Quinn R., Armstrong L., Shreenan R. Combined high-density lidar and multispectral imagery for individual tree crown analysis. Can. J. Remote Sens. 2003, 29:633-649.
39. 2 storage using high resolution airborne lidar data. GISci. Remote Sens. 2013, 50:154-171.
40. Li M., Im J., Beier C. Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GISci. Remote Sens. 2013, 50:418-436.
41. Li M., Im J., Quackenbush L.J., Liu T. Forest biomass and carbon stock quantification using airborne lidar data: a case study over huntington wildlife forest in the adirondack park. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2014, 7(7):3143-3156.
42. Li W., Guo Q., Jakubowski M.K., Kelly M. A new method for segmenting individual trees from the lidar point cloud. Photogramm. Eng. Remote Sens. 2012, 78(1):75-84.
43. Long J., Lawrence R., Greenwood M., Marshall L., Miller P. Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest. GISci. Remote Sens. 2013, 50:418-436.
44. Lovell J., Jupp D.L., Culvenor D., Coops N. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can. J. Remote Sens. 2003, 29:607-622.
45. Lu Z., Im J., Quackenbush L.J., Yoo S. Remote sensing based house value estimation using an optimized regional regression model. Photogramm. Eng. Remote Sens. 2013, 79:809-820.
46. Lu X., Guo Q., Li W., Flanagan J. A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data. ISPRS J. Photogramm. Remote Sens. 2014, 94:1-12.
47. Mallat S. A Wavelet Tour of Signal Processing 1999, Academic Press, San Diego, (Chapter 5). second ed.
48. Mallat S. A Wavelet Tour of Signal Processing: The Sparse Way 2008, Academic Press, San Diego, (Chapter 7). third ed.
49. Maxwell A., Strager M., Warner T., Zegre N., Yuill C. Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation. GISci. Remote Sens. 2014, 51:301-320.
50. Meyer F. Topographic distance and watershed lines. Signal Process. 1994, 38(1):113-125.
51. Morsdorf F., Meier E., Kötz B., Itten K.I., Dobbertin M., Allgöwer B. LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sens. Environ. 2004, 92(3):353-362.
52. Muss J.D., Mladenoff D.J., Townsend P.A. A pseudo-waveform technique to assess forest structure using discrete lidar data. Remote Sens. Environ. 2011, 115:824-835.
53. Persson A., Holmgren J., Söderman U. Detecting and measuring individual trees using an airborne laser scanner. Photogramm. Eng. Remote Sens. 2002, 68(9):925-932.
54. Pitkänen J., Maltamo M., Hyyppä J., Yu X. Adaptive methods for individual tree detection on airborne laser based canopy height model. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 2004, 36:187-191.
55. Popescu S.C., Zhao K. A voxel-based lidar method for estimating crown base height for deciduous and pine trees. Remote Sens. Environ. 2008, 112:767-781.
56. Popescu S.C., Wynne R.H. Seeing the trees in the forest: using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm. Eng. Remote Sens. 2004, 70:589-604.
57. Popescu S.C., Wynne R.H., Nelson R.F. Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Comput. Electron. Agric. 2002, 37(1):71-95.
58. Rahman, M., Gorte, B., Bucksch, A., 2009. A new method for individual tree measurement from airborne LiDAR. Silvilaser 2009 Proceedings, pp. 14-16.
59. Reitberger J., Schnörr C., Krzystek P., Stilla U. 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS J. Photogramm. Remote Sens. 2009, 64(6):561-574.
60. Rhee J., Park S., Lu Z. Relationship between land cover patterns and surface temperature in urban areas. GISci. Remote Sens. 2014, 51:521-536.
61. Solberg S., Naesset E., Bollandsas O.M. Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogramm. Eng. Remote Sens. 2006, 72(12):1369-1378.
62. Tochon G., Féret J.B., Valero S., Martin R.E., Knapp D.E., Salembier P., Asner G.P. On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images. Remote Sens. Environ. 2015, 159:318-331.
63. Tang S., Dong P., Buckles B.P. Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method. Int. J. Remote Sens. 2013, 34(4):1373-1385.
64. Unger D., Hung I., Brooks R., Williams H. Estimating number of trees, tree height and crown width using Lidar data. GISci. Remote Sens. 2014, 51:227-238.
65. Vauhkonen J., Ene L., Gupta S., Heinzel J., Holmgren J., Pitkänen J., et al. Comparative testing of single-tree detection algorithms under different types of forest. Forestry 2012, 85:27-40.
66. Wang L. A multi-scale approach for delineating individual tree crowns with very high resolution imagery. Photogramm. Eng. Remote Sens. 2010, 76:371-378.
67. Wang L., Gong P., Biging G.S. Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogramm. Eng. Remote Sens. 2004, 70:351-358.
68. Wulder M., Niemann K.O., Goodenough D.G. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sens. Environ. 2000, 73:103-114.
69. Yang J., He Y., Caspersen J. A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image. 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2014, 1588-1591. IEEE.
70. Yoo S., Im J., Wagner J.E. Variable selection for hedonic modeling using machine learning approaches: a case study in Onondaga County, NY, USA. Landscape Urban Plann. 2012, 107:293-306.
71. Yu X., Hyyppä J., Vastaranta M., Holopainen M., Viitala R. Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J. Photogramm. Remote Sens. 2011, 66:28-37.
72. Zhen Z., Quackenbush L.J., Stehman S.V., Zhang L. Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data. Int. J. Remote Sens. 2015, 36(7):1965-1993.