Forest types classification based on multi-source data fusion

Remote Sensing

Volumn 9, Issue 11, 2017, Pages

  Lu, Ming ^a,b   Chen, Bin ^c   Liao, Xiaohan ^a   Yue, Tianxiang ^a,b   Yue, Huanyin ^a   Ren, Shengming ^d   Li, Xiaowen ^c   Nie, Zhen ^e   Xu, Bing ^c,e  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c TSINGHUA UNIVERSITY   (China)

^d Key Laboratory of Precise Engineering and Industry Surveying   (China)

^e BEIJING NORMAL UNIVERSITY   (China)

Author keywords

Classification; Data fusion; Forest types

Indexed keywords

CARBON; CHARGE COUPLED DEVICES; CONSERVATION; DATA FUSION; ECOLOGY; ECOSYSTEMS; FORESTRY; IMAGE ENHANCEMENT; IMAGE FUSION; MAPPING; PHOTOMAPPING; RADIOMETERS; SATELLITE IMAGERY; SPECTROSCOPY;

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; ECOLOGICAL RESTORATION; FOREST RESOURCE INVENTORY; FOREST TYPE; HYPERSPECTRAL IMAGERS; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER DATUM; TEMPORAL AND SPECTRAL RESOLUTIONS;

CLASSIFICATION (OF INFORMATION);

EID: 85034755403     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9111153     Document Type: Article

References (54)

1. Pan, Y.; Birdsey, R.A.; Phillips, O.L.; Jackson, R.B. The structure, distribution, and biomass of the world's forests. Annu. Rev. Ecol. Evol. Syst. 2013, 44, 593-622
2. Masek, J.G.; Hayes, D.J.; Hughes, M.J.; Healey, S.P.; Turner, D.P. The role of remote sensing in process-scaling studies of managed forest ecosystems. For. Ecol. Manag. 2015, 355, 109-123
3. McKinley, D.C.; Ryan, M.G.; Birdsey, R.A.; Giardina, C.P.; Harmon, M.E.; Heath, L.S.; Houghton, R.A.; Jackson, R.B.; Morrison, J.F.; Murray, B.C. A synthesis of current knowledge on forests and carbon storage in the United States. Ecol. Appl. 2011, 21, 1902-1924
4. Young, R.A. Introduction to Forest Science; John Wiley & Sons: Hoboken, NJ, USA, 1982
5. Qin, Y.; Xiao, X.; Dong, J.; Zhang, G.; Roy, P.S.; Joshi, P.K.; Gilani, H.; Murthy, M.S.R.; Jin, C.; Wang, J. Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010. Sci. Rep. 2016, 6
6. Molinier, M.; López-Sánchez, C.A.; Toivanen, T.; Korpela, I.; Corral-Rivas, I.J.; Tergujeff, R.; Häme, T. Relasphone-Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping. Remote Sens. 2016, 8, 869
7. Pratihast, A.K.; DeVries, B.; Avitabile, V.; Bruin, S.; Kooistra, L.; Tekle, M.; Herold, M. Combining Satellite Data and Community-Based Observations for Forest Monitoring. Forests 2014, 5, 2464-2489
8. Wulder, M.; Franklin, S. Remote Sensing of Forest Environments: Concepts and Case Studies; Springer Science & Business Media: Berlin, Germany, 2012
9. Ren, C.; Ju, H.; Zhang, H.; Huang, J.; Zheng, Y. Multi-source data for forest land type precise classification. Sci. Silvae Sin. 2016, 52, 54-65
10. Torresan, C.; Corona, P.; Scrinzi, G.; Marsal, J.V. Using classification trees to predict forest structure types from lidar data. Ann. For Res. 2016, 59, 281-298
11. Gorgens, E.B.; Soares, C.P.B.; Nunes, M.H.; Rodriguez, L.C.E. Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning. Appl. Veg. Sci. 2016, 19, 518-527
12. Chen, C.; Bian, Z.; Li, S.; Tang, P.; Wu, H. Improving forest type classification using the vegetation local difference index. Int. J. Remote Sens. 2015, 36, 3701-3713
13. Castilla, G.; Rodrigue, S.; Skakun, R.S.; Hall, R.J. Four national maps of broad forest type provide inconsistent answers to the question of what burns in Canada. Remote Sens. 2016, 8, 539
14. Connette, G.; Oswald, P.; Songer, M.; Leimgruber, P. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery for Myanmar's Tanintharyi region. Remote Sens. 2016, 8, 882
15. Chen, B.; Huang, B.; Xu, B. Comparison of spatiotemporal fusion models: A review. Remote Sens. 2015, 7, 1798-1835
16. Zhang, L.P.; Shen, H.F. Progress and future of remote sensing data fusion. J. Remote Sens. 2016, 20, 1050-1061
17. Shen, H. Integrated fusion method for multiple temporal-spatial-spectral images. In Proceedings of the XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012; pp. 407-410
18. Zhang, Y.F.; He, M.Y. Multi-spectral and hyperspectral image fusion using 3-D wavelet transform. J. Electron. 2007, 24, 208-244
19. Chen, Z.; Pu, H.Y.; Wang, B. Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1418-1422
20. Vivone, G.; Alparone, L.; Chanussot, J.; Garzelli, A.; Licciardi, G.A.; Restaino, R.; Wald, L. A Critical Comparison among Pansharpening Algorithms. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2565-2586
21. Chen, B.; Huang, B.; Xu, B. A hierarchical spatiotemporal adaptive fusion model using one image pair. Int. J. Digit. Earth 2016, 10, 1-17
22. Wang, F.; Hu, X.; Liu, Y.; Zhou, F.; Zhou, H.; Liu, Z. Community composition and spatial distribution of coniferous-broadleaved mixed forest in subtropical region, Jiangxi province. Acta Agric. Univ. Jiangxiensis 2013, 35, 761-768
23. China Center for Resources Satellite and Application. Available online: http://www.cresda.com/CN/(accessed on 2 August 2017)
24. LAADS DAAC. Available online: https://ladsweb.modaps.eosdis.nasa.gov/(accessed on 2 August 2017)
25. Wulder, M.A.; Coops, N.C. Make Earth observations open access. Nature 2014, 513, 30-31
26. Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 2013, 342, 850-853
27. Lehmann, E.A.; Wallace, J.F.; Caccetta, P.A.; Furby, S.L.; Zdunic, K. Forest cover trends from time series Landsat data for the Australian continent. Int. J. Appl. Earth Obs. Geoinf. 2013, 21, 453-462
28. Zhu, Z.;Woodcock, C.E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 2014, 144, 152-171
29. Bureau, S.F. Brief Introduction of Forest Resources in China-The Eighth National Forestry Inventory. Available online: http://211.167.243.162:8085/8/chengguobaogao/showpageinit?lm=xxxz (accessed on 2 August 2017)
30. Chavez, P.S.; Sides, S.C.; Anderson, J.A. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic, Photogram. Eng. Remote Sens. 1991, 57, 265-303
31. Haydn, R.; Dalke, G.W.; Henkel, J.; Bare, J.E. Application of the IHS color transform to the processing of multi sensor data and image enhancement. In Proceedings of the International Symposium on Remote Sensing of Environment, First Thematic Confrence: "Remote Sensing of Arid and Semi-Arid Lands", Cairo, Egypt, 19-25 January1982; pp. 599-616
32. Aiazzi, B.; Alparone, L.; Baronti, S.; Garzelli, A. Context-driven fusion of high spatial and spectral resolution images based on over sampled multiresolution analysis. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2300-2312
33. Zhang, L.P.; Shen, H.F.; Gong, W.; Zhang, H.Y. Adjustable model-based fusion method for multispectral and panchromatic images. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2012, 42, 1693-1704
34. Jiang, C.; Zhang, H.Y.; Shen, H.F.; Zhang, L.P. A practical compressed sensing-based pan-sharpening method. IEEE Geosci. Remote Sens. Lett. 2012, 9, 629-633
35. Fauvel, M.; Chanussot, J. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 2008, 46, 3804-3814
36. Xu, B.; Gong, P. Land-use/land-cover classification with multispectral and hyperspectral E0-1 data. Photogramm. Eng. Remote Sens. 2007, 73, 955-965
37. Chen, B.; Chen, L.; Lu, M.; Xu, B. Wetland mapping by fusing fine spatial and hyperspectral resolution images. Ecol. Model. 2017, 353, 95-106
38. Acerbi-Junior, F.W.; Clevers, J.G.P.W.; Schaepman, M.E. The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna. Int. J. Appl. Earth Obs. Geoinf. 2006, 8, 278-288
39. Hilker, T.; Wulder, M.A.; Coops, N.C.; Linke, J.; McDermid, G.; Masek, J.G.; Gao, F.; White, J.C. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 2009, 113, 1613-1627
40. Gao, F.; Masek, J.; Schwaller, M.; Hall, F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2207-2218
41. Zhu, X.L.; Chen, J.; Gao, F.; Chen, X.H.; Masek, J.G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 2010, 114, 2610-2623
42. Michishita, R.; Chen, L.F.; Chen, J.; Zhu, X.L.; Xu, B. Spatiotemporal reflectance blending in a wetland environment. Int. J. Digit. Earth 2015, 8, 364-382
43. Zhukov, B.; Oertel, D.; Lanzl, F.; Reinhackel, G. Unmixing-based multi sensor multiresolution image fusion. IEEE Trans. Geosci. Remote Sens. 1999, 37, 1212-1226
44. Zurita-Milla, R.; Clevers, J.G.P.W.; Schaepman, M.E. Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geosci. Remote Sens. Lett. 2008, 5, 453-457
45. Zhu, X.L.; Helmer, E.H.; Gao, F.; Liu, D.S.; Chen, J.; Lefsky, M.A. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 2016, 172, 165-177
46. Huang, B.; Song, H. Spatiotemporal reflectance fusion via sparse representation. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3707-3716
47. Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247-259
48. Burges, C.J. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 1998, 2, 121-167
49. Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 1-27
50. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80, 185-201
51. Ren, C.; Ju, H.; Zhang, H.; Huang, J. Forest land type precise classification based on SPOT5 and GF-1 images. In Proceedings of the 2016 IEEE International Geoscience and Remote sensing Symposium, Beijing, China, 10-15 July 2016; pp. 894-897
52. Xu, B.; Gong, P. Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration. Can. J. Remote Sens. 2008, 34, 271-286
53. Acito, N.; Diani, M.; Corsini, G. Subspace-based striping noise reduction in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1325-1342
54. Lu, M.; Hu, L.J.; Yue, T.X.; Chen, Z.Y.; Chen, B.; Lu, X.Q.; Xu, B. Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal. IEEE Geosci. Remote Sens. Lett. 2017, 14, 359-363