Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna, Yunnan Province, China

Chinese Science Bulletin

Volumn 52, Issue SUPPL. 2, 2007, Pages 232-243

  Zhang, Zhiming ^a,b   Verbeke, Lieven ^b   De Clercq, Eva ^b   Ou, Xiaokun ^a   De Wulf, Robert ^b  

^a YUNNAN UNIVERSITY   (China)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

ANN change detection; Elevation data; NDVI differencing; Post classification; Vegetation change

Indexed keywords

EID: 43049120676     PISSN: 10016538     EISSN: 18619541     Source Type: Journal    
DOI: 10.1007/s11434-007-0711-1     Document Type: Article

References (27)

1. Lambin E F, Geist H J, Lepers E. Dynamics of land use and land cover change in tropical regions. Ann Rev Environ Resour, 2003, 28: 205-241
2. Liu J, Zhang Z, Zhuang D, et al. A study on the spatial - temporal dynamic changes of land - use and driving forces analyses of China in the 1990s. Geogr Res, 2003, 22(1): 1-12
3. Ren Z, Zhang Y. Land Use Change and Ecological Security Assessment (in Chinese). Beijing: Science Press, 2003. 3-4
4. Shi P, Jiang Y, Wang J, et al. LUCC and the Mechanism of Ecological Security Responses to LUCC (in Chinese). Beijing: Science Press, 2003. 80-151
5. Wu J. The key research topics in landscape ecology. Acta Ecol Sin (in Chinese), 2004, 24(9): 2074-2076
6. Singh A. Digital change detection techniques using remotely sensed data. Int J Remote Sens, 1989, 10(6): 989-1003
7. Green K, Kempka D, Lackey L. Using remote sensing to detect and monitor land-cover and land-use change. Photogr Eng Remote Sens, 1994, 60(3): 331-337
8. Pat S C Jr, MacKinnon D J. Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogr Eng Remote Sens, 1994, 60(5): 571-583
9. Coppin P, Jonckheere I, Nackaerts K, et al. Digital change detection methods in ecosystem monitoring: A review. Int J Remote Sens, 2004, 25(9): 1565-1596
10. Lu D, Mausel P, Brondízio E, et al. Change detection techniques. Int J Remote Sens, 2004, 25(12): 2365-2407
11. Dai X L, Khorram S. Remotely sensed change detection based on artificial neural networks. Photogr Eng Remote Sens, 1999, 65(10): 1187-1194
12. Abuelgasim A A, Ross W D, Gopal S, et al. Change detection using adaptive fuzzy neural networks: Environmental damage assessment after the Gulf War. Remote Sens Environ, 1999, 70: 208-223
13. Chan J C-W, Chan K-P, Yeh A. G-O. Detecting the nature of change in an urban environment: A comparison of machine learning algorithms. Photogr Eng Remote Sens, 2001, 67(2): 213-225
14. Xiao P, Li D. Land use/cover change detection based on artificial neural network. Geomatics Info Sci Wuhan Univ, 2003, 27(6): 586-590
15. Gopal S, Woodcock C E. Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens, 1996, 28(4): 540-551
16. Woodcock C E, Macomber S A, Pax-Lenney M, et al. Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors. Remote Sens Environ, 2001, 78: 194-203
17. Liu X, Lathrop R G Jr. Urban change detection based on an artificial neural network. Int J Remote Sens, 2002, 23: 2513-2518
18. Bruzzone L, Conese C, Maselli F, et al. Multisource classification of complex rural areas by statistical and neural-network approaches. Photogr Eng Remote Sens, 1997, 63(5): 523-533
19. th ed. New York: John Wiley & Sons, Inc. 2000. 572-575
20. Lyon J G, Yuan D, Lunetta R S, et al. A change detection experiment using vegetation indices. Photogr Eng Remote Sens, 1998, 64(2): 143-150
21. th International Symposium on Remote Sensing of Urban Areas (Regensburg, Germany), 2003
22. Atkinson P M, Tatnall A R L. Neural networks in remote sensing. Int J Remote Sens, 1997, 18(4): 699-709
23. Benediktsson J A, Swain P H, Ersoy O K. Neural network approaches versus statistical methods in classification of mutisource remote sensing data. IEEE Trans Geosci Remote Sens, 1990, 28(4): 540-551
24. Benediktsson J A, Sveinsson J R. Feature extraction for multisource data classification with artificial neural networks. Int J Remote Sens, 1997, 8: 727-740
25. Foody G M, Arora M K. Evaluation of some factors affecting the accuracy of classification by an artificial neural network. Int J Remote Sens, 1997, 18: 799-810
26. Lowell K, Richards G, Woodgates P, et al. Fuzzy reliability assessment of multi-period land-cover change maps. Photogr Eng Remote Sens, 2005, 71(8): 939-945
27. Congalton R G, Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton: Lewis Publisher, 1999