Mapping Robinia pseudoacacia forest health conditions by using combined spectral, spatial, and textural information extracted from IKONOS imagery and random forest classifier

Remote Sensing

Volumn 7, Issue 7, 2015, Pages 9020-9044

  Wang, Hong ^a   Zhao, Yu ^a   Pu, Ruiliang ^b   Zhang, Zhenzhen ^a  

^a HOHAI UNIVERSITY   (China)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Forest health condition; Getis statistic; GLCM; Random forest; Robinia pseudoacacia

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; HEALTH; IMAGE REGISTRATION; INFORMATION ANALYSIS; INFRARED DEVICES; PHOTOMAPPING; REMOTE SENSING;

FOREST HEALTH; GETIS STATISTICS; GLCM; RANDOM FORESTS; ROBINIA PSEUDO-ACACIA;

SATELLITE IMAGERY;

EID: 84937820069     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70709020     Document Type: Article

References (78)

1. Raffa, K.F.; Aukema, B.H.; Bentz, B.J.; Carroll, A.L.; Hicke, J.A.; Turner, M.G.; Romme, W.H. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: The dynamics of bark beetle eruptions. Bioscience 2008, 58, 501-517.
2. Van Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; Daniels, L.D.; Franklin, J.F.; Fulé, P.Z.; Harmon, M.E.; Larson, A.J.; Smith, J.M.; Taylor, A.H.; et al. Widespread increase of tree mortality rates in the western United States. Science 2009, 323, 521-524.
3. Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetierf, M.; Kitzbergerg, T.; Riglingh, A.; Breshearsi, D.D.; Hoggj, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks of forests. For. Ecol. Manag. 2010, 259, 660-684.
4. McDowell, N.; Pockman, W.T.; Allen, C.D.; Breshears, D.D.; Cobb, N.; Kolb, T.; Sperry, J.; West, A.; Williams, D.; Yepez, E.A. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought? New Phytol. 2008, 178, 719-739.
5. Manion, P.D. Tree Disease Concepts, 2nd ed.; Prentice-Hall Inc.: Upper Saddle River, NJ, USA, 1991; p. 416.
6. Manion, P.D.; Lachance, D. Forest Decline Concepts; APS Press: St. Paul, MN, USA, 1992; p. 249.
7. Haywood, A.; Stone, C. Mapping eucalypt forest susceptible to dieback associated with bell miners (Manorina melanophys) using laser scanning, SPOT 5 and ancillary topographical data. Ecol. Model. 2011, 222, 1174-1184.
8. Rock, B.N.; Vogelmann, J.E.; Williams, D.L.; Vogelmann, A.F.; Hoshizaki, T. Remote detection of forest damage. BioScience 1986, 36, 439-445.
9. Wulder, M.A.; Dymond, C.C.; White, J.C.; Leckie, D.G.; Carroll, A.L. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecol. Manag. 2006, 221, 27-41.
10. Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; Allen, C.D.; Balice, R.G.; Romme, W.H.; Kastens, J.H.; Floyd, M.L.; Belnap, J.; et al. Regional vegetation die-offin response to global-change-type drought. Proc. Natl. Acad. Sci. USA 2005, 102, 15144-15148.
11. Dorigo, W.; Lucieer, A.; Podobnikar, T.; Čarni, A. Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. Int. J. Appl. Earth Obs. Geoinf. 2012, 19, 185-195.
12. Joria, P.E.; Ahearn, S.C. A comparison of the SPOT and Landsat Thematic Mapper satellite systems for detecting gypsy moth defoliation in Michigan. Photogramm. Eng. Remote Sens. 1991, 57, 1605-1612.
13. Royle, D.D.; Lathrop, R.G. Monitoring hemlock forest health in New Jersey using Landsat TM Data and change detection techniques. For. Sci. 1997, 43, 327-335.
14. Franklin, S.E.; Wulder, M.A.; Skakun, R.S.; Carroll, A.L. Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada. Photogramm. Eng. Remote Sens. 2003, 69, 283-288.
15. Wang, C.Z.; Lu, Z.Q.; Haithcoat, T.L. Using Landsat images to detect oak decline in the Mark Twain National Forest, Ozark Highlands. Forest Ecol. Manag. 2007, 240, 70-78.
16. Coops, N.C.; Waring, R.H.; Wulder, M.A.; White, J.C. Prediction and assessment of bark beetle-induced mortality of lodgepole pine using estimates of stand vigour derived from remotely sensed data. Remote Sens. Environ. 2009, 113, 1058-1066.
17. Negrón-Juárez, R.I.; Chambers, J.Q.; Guimaraes, G.; Zeng, H.; Raupp, C.F.M.; Marra, D.M.; Ribeiro, G.H.P.M.; Saatchi, S.S.; Nelson, B.W.; Higuchi, N. Widespread Amazon forest tree mortality from a single cross basin squall line event. Geophys. Res. Lett. 2010, 37, L16701.
18. Ingram, J.C.; Dawson, T.P.; Whittaker, R.J. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens. Environ. 2005, 94, 491-507.
19. Lee, S.H.; Cho, H.K. Detection of the pine trees damaged by pine wilt disease using high spatial remote sensing data. In Proceedings of the ISPRS Commission VII Symposium "Remote Sensing: From Pixels to Processes", Enschede, The Netherlands, 8-11 May 2006.
20. Coops, N.C.; Johnson, M.; Wulder, M.A.; White, J.C. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 2006, 103, 67-80.
21. Huang, X.; Zhang, L.; Li, P. An adaptive multiscale information fusion approach for feature extraction and classification of IKONOS multispectral imagery over urban areas. IEEE Geosci. Remote Sens. Lett. 2007, 4, 654-658.
22. Radeloff, V.C.; Mladenoff, D.J.; Boyce, M.S. Detecting jack pine budworm defoliation using spectral mixture analysis: Separating effects from determinants. Remote Sens. Environ. 1999, 69, 156-169.
23. Franklin, S.E.; Wulder, M.A.; Gerylo, G.R. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. Int. J. Remote Sens. 2001, 22, 2627-2632.
24. Gong, P.; Howarth, P. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogramm. Eng. Remote Sens. 1990, 56, 67-73.
25. Huang, X.; Liu, X.; Zhang, L. A multichannel gray level co-occurrence matrix for multi/hyperspectral image texture representation. Remote Sens. 2014, 6, 8424-8445.
26. Huang, X.; Zhang, L. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans. Remote Sens. 2013, 51, 257-272.
27. St-Onge, B.A.; Cavayas, F. Automated forest structure mapping from high resolution imagery based on directional semivariogram estimates. Remote Sens. Environ. 1997, 61, 82-95.
28. Hsu, S.Y. Texture-tone analysis for automated land-use mapping. Photogramm. Eng. Remote Sens. 1978, 44, 1393-1404.
29. Franklin, S.; Maudie, A.; Lavlgne, M. Using spatial co-occurrence texture to increase forest structure and species composition classification accuracy. Photogramm. Eng. Remote Sens. 2001, 67, 849-855.
30. Lévesque, J.; King, D.J. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health. Remote Sens. Environ. 2003, 84, 589-602.
31. Kayitakire, F.; Hamel, C.; Defourny, P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ. 2006, 102, 390-401.
32. Johansen, K.; Coops, N.C.; Gergel, S.E.; Stange, Y. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens. Environ. 2007, 110, 29-44.
33. Wang, H.; Pu, R.; Zhu, Q.; Ren, L.; Zhang, Z.Z. Mapping Health Levels of Robinia pseudoacacia Forests in the Yellow River Delta, China, Using IKONOS and Landsat 8 OLI Imagery. Int. J. Remote Sens. 2015, 36, 1114-1135.
34. Getis, A.; Ord, J.K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 1992, 24, 189-206.
35. Ord, J.K.; Getis, A. Local spatial autocorrelation statistics: Distributional issues and an application. Geogr. Anal. 1995, 27, 286-306.
36. Anselin, L. Local indicators of spatial association-LISA. Geogr. Anal. 1995, 27, 93-115.
37. Wulder, M.; Boots, B. Local spatial autocorrelation characteristics of Landsat TM imagery of managed forest area. Can. J. Remote Sens. 2001, 27, 67-75.
38. Myint, S.W.; Wentz, E.A.; Purkis, S.J. Employing spatial metrics in urban land-use/land-cover mapping: Comparing the Getis and Geary indices. Photogramm. Eng. Remote Sens. 2007, 73, 1403-1415.
39. Ghimire, B.; Rogan, J.; Miller, J. Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens. Lett. 2010, 1, 45-54.
40. LeDrew, E.F.; Holden, H.; Wulder, M.A.; Derksen, C.; Newman, C. A spatial statistical operator applied to multidate satellite imagery for identification of coral reef stress. Remote Sens. Environ. 2004, 91, 271-279.
41. Jin, Y.Q.; Yan, F. Monitoring sandstorms and desertification in northern China using SSM/I data and Getis statistics. Int. J. Remote Sens. 2004, 25, 2053-2060.
42. Merzouki, A.; McNairn, H.; Pacheco, A. Mapping soil moisture using RADARSAT-2 data and local autocorrelation statistics. IEEE J. Sel. Top. App. Earth Obs. Remote Sens. 2011, 4, 128-137.
43. Odongo, V.O.; Hamm, N.A.S.; Milton, E.J. Spatio-temporal assessment of Tuz Gölü, Turkey as a potential radiometric vicarious calibration site. Remote Sens. 2014, 6, 2494-2513.
44. Goetz, S.J.; Wright, R.; Smith, A.J.; Zinecker, E.; Schaub, E. Ikonos imagery for resource management: Tree cover, impervious surfaces and riparian buffer analyses in the mid-Atlantic region. Remote Sens. Environ. 2003, 88, 195-208.
45. Wulder, M.A.; Hall, R.J.; Coops, N.; Franklin, S. High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 2004, 54, 511-521.
46. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
47. Dye, M.; Mutanga, O.; Ismail, R. Combining spectral and textural remote sensing variables using random forests: Predicting the age of Pinus patula forests in KwaZulu-Natal, South Africa. J. Spat. Sci. 2012, 57, 193-211.
48. Grinand, C.; Rakotomalala, F.; Gond, V.; Vaudry, R.; Bernoux, M.; Vieilledent, G. Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sens. Environ. 2013, 139, 68-80.
49. Abdel-Rahman, E.M.; Mutanga, O.; Adam, E.; Ismail, R. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS J. Photogramm. Remote Sens. 2014, 88, 48-59.
50. Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294-300.
51. Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecology 2007, 88, 2783-2792.
52. Chan, J.C.W.; Paelinckx, D. Evaluation of Random forest and adaboost tree based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens. Environ. 2008, 112, 2999-3011.
53. Wulder, M.; Boots, B. Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic. Int. J. Remote Sens. 1998, 19, 2223-2231.
54. Xu, X.G.; Guo, H.H.; Chen, X.L.; Lin, H.P.; Du, Q.L. A multi-scale study on land use and land cover quality change: The case of the Yellow River Delta in China. GeoJournal 2002, 56, 177-183.
55. Schomaker, M.E.; Zarnoch, S.J.; Bechtold, W.A.; Latelle, D.J.; Burkman, W.G.; Cox, S.M. Crown Condition Classification: A Guide to Data Collection and Analysis; Gen. Tech. Rep. SRS-102; U.S. Department of Agriculture, Forest Service, Southern Research Station: Asheville, NC, USA, 2007.
56. Jensen, J.R. Atmospheric correction. In Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2005; pp. 210-213.
57. Haralick, R.M.; Shanmugan, K.; Dinstein, I. Texture features for image classification. IEEE Trans. Syst. Man Cyern. 1973, 3, 610-621.
58. Hall-Beyer, M. GLCM Texture: A Tutorial, Version 2.10. Available online: http://www.fp.ucalgary.ca/mhallbey/tutorial.htm (accessed on 22 February 2015).
59. Pu, R.; Cheng, J. Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US. Int. J. Appl. Earth Obs. 2015, 42, 11-23.
60. Moskal, L.M.; Franklin, S.E. Classifying multilayer forest structure and composition using high resolution, compact airborne spectrographic imager image texture. In Proceedings of the American Society of Remote Sensing and Photogrammetry Annual Conference, St. Louis, MO, USA, 23-27 April 2001.
61. Puissant, A.; Hirsch, J.; Weber, C. The utility of texture analysis to improve per pixel classification for high to very high spatial resolution imagery. Int. J. Remote Sens. 2005, 26, 733-745.
62. Aplin, P. On scales and dynamics in observing the environment. Int. J. Remote Sens. 2006, 27, 2123-2140.
63. ITTVIS. ITT Visual Information Solutions-Data Analysis and Visualization Software, Training and Consulting; February 2009. Available online: http://www.ittvis.com (accessed on 16 February 2009).
64. Lawrence, R.L.; Wood, S.D.; Sheley, R.L. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forest). Remote Sens. Environ. 2006, 100, 356-362.
65. Ismail, R.; Mutanga, O. A comparison of regression tree ensembles: Predicating Sirex noctilio induced water stress in Pinus patula forest of KwaZulu-Natal, South Africa. Int. J. Appl. Earth Obs. Geoinf. 2010, 12, 45-51.
66. Mascaro, J.; Asner, G.P.; Knapp, D.E.; Kennedy-Bowdoin, T.; Martin, R.E.; Anderson, C.; Higgins, M.; Chadwick, K.D. A tale of two "Forests": Random forest machine learning aids tropical forest carbon mapping, PLoS One 2014, 9, e85993.
67. Waske, B.; van der Linden, S.; Oldenburg, C.; Jakimow, B.; Rabe, A.; Hostert, P. imageRF-A user-oriented implementation for remote sensing image analysis with Random Forests. Environ. Model. Softw. 2012, 35, 192-193.
68. Congalton, R.G.; Mead, R.A. A quantitative method to test for consistency and correctness in photointerpretation. Photogramm. Eng. Remote Sens. 1983, 49, 69-74.
69. Story, M.; Congalton, R.G. Accuracy assessment-A user's perspective. Photogramm. Eng. Remote Sens. 1986, 52, 397-399.
70. Franklin, S.; Wulder, M.; Lavigne, M. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Comput. Geosci. 1996, 22, 665-673.
71. Su, W.; Li, J.; Chen, Y.; Liu, Z.; Zhang, J.; Low, T.M.; Suppiah, I.; Hashim, S.A.M. Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery. Int. J. Remote Sens. 2008, 29, 3105-3117.
72. Griffith, P.; Getis, A.; Griffin, E. Regional patterns of affirmative action compliance costs. Ann. Reg. Sci. 1996, 30, 321-340.
73. Treits, P.; Hwarth, P. High spatial resolution remote sensing data for forest ecosystem classification: An examination of spatial scale. Remote Sens. Environ. 2000, 76, 268-289.
74. Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2012, 67, 93-104.
75. Liang, Y.; Long, Z. Theory and Technology on Robinia Pseudoacacia Cultivation; China Forestry Publishing: Beijing, China, 2010.
76. Zhang, J.; Xing, S. Research on soil degradation of Robinia pseudoacacia plantation under environmental stress. Chin. J. Soil Sci. 2009, 40, 1086-1091.
77. Wang, H.; Gong, P.; Liu, G. Multi-scale spatial variations in soil salt in the Yellow River Delta. Geogr. Res. 2006, 25, 649-658.
78. Huang, X.; Zhang, L.; Wang, L. Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS Imagery. IEEE Geosci. Remote Sens. Lett. 2009, 6, 393-397.