Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Remote Sensing

Volumn 7, Issue 1, 2015, Pages 865-881

  Wang, Jie ^a   Li, Congcong ^b   Hu, Luanyun ^b   Zhao, Yuanyuan ^b   Huang, Huabing ^a   Gong, Peng ^a,b,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b TSINGHUA UNIVERSITY   (China)

^c JOINT CENTER FOR GLOBAL CHANGE STUDIES   (China)

Author keywords

Entropy; Image sequence; Land cover; Markov random field; Random forest

Indexed keywords

DECISION TREES; ENTROPY; MARKOV PROCESSES; WATER RESOURCES;

IMAGE SEQUENCE; LAND COVER; MARKOV RANDOM FIELD MODELS; MARKOV RANDOM FIELDS; OVERALL ACCURACIES; RANDOM FORESTS; SPATIO-TEMPORAL CONSISTENCIES; THERMAL INFRARED BANDS;

INFORMATION USE;

EID: 84980000594     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70100865     Document Type: Article

References (37)

1. Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja, L.E., Meehl, G.A., Washington, W.M. The importance of land-cover change in simulating future climates. Science 2005, 310, 1674-1678.
2. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., et al. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747-756.
3. Bontemps, S., Herold, M., Kooistra, L., van Groenestijn, A., Hartley, A., Arino, O., Moreau, I., Defourny, P. Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 2012, 9, 2145-2157.
4. Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., Dickinson, R. The role of satellite remote sensing in climate change studies. Nat. Clim. Change 2013, 3, 875-883.
5. Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 2002, 83, 287-302.
6. Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168-182.
7. Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R., Weber, J.L. GlobCover the Most Detailed Portrait of Earth. Available online: http://www.esa.int/esapub/bulletin/bulletin136/ bul136d_arino.pdf (accessed on 16 November 2014).
8. Arino, O., Ramos, J., Kalogirou, V., Defourny, P. and Achard, F. Globcover 2009. In Proceedings of the Living Planet Symposium, Bergen, Norway, 28 June-2 July 2010.
9. Gong, P., Wang, J., Yu, L., Zhao, Y.C., Zhao, Y.Y., Liang, L., Niu, Z.G., Huang, X.M., Fu, H.H., Liu, S., et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 2013, 34, 2607-2654.
10. Sexton, J.O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with LiDAR-based estimates of error. Int. J. Digit. Earth 2013, 6, 427-448.
11. 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.
12. Kennedy, R.E., Yang, Z.G., Cohen, W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. Landtrendr-Temporal segmentation algorithms. Remote Sens. Environ. 2010, 114, 2897-2910.
13. Huang, C.Q., Coward, S.N., Masek, J.G., Thomas, N., Zhu, Z.L., Vogelmann, J.E. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 2010, 114, 183-198.
14. Sexton, J.O., Urban, D.L., Donohue, M.J., Song, C.H. Long-term land cover dynamics by multi-temporal classification across the landsat-5 record. Remote Sens. Environ. 2013, 128, 246-258.
15. Zhu, Z., Woodcock, C.E., Olofsson, P. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 2012, 122, 75-91.
16. 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.
17. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 2010, 114, 106-115.
18. Sun, F.D., Zhao, Y.Y., Gong, P., Ma, R.H., Dai, Y.J. Monitoring dynamic changes of global land cover types: Fluctuations of major lakes in China every 8 days during 2000-2010. Chin. Sci. Bull. 2014, 59, 171-189.
19. Loth, B., Graf, H.F., Oberhuber, J.M. Snow cover model for global climate simulations. J. Geophys. Res. Atmos. 1993, 98, 10451-10464.
20. Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154-172.
21. Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Type III Final Report, NASA/GSFC: Greenbelt, MD, USA, 1974.
22. Haralick, R.M., Shanmuga.K, Dinstein, I. Textural features for image classification. IEEE Syst. Man Cy. Soc. 1973, Smc3, 610-621.
23. Gong, P., Marceau, D.J., Howarth, P.J. A comparison of spatial feature-extraction algorithms for land-use classification with SPOT HRV data. Remote Sens. Environ. 1992, 40, 137-151.
24. Caridade, C.M.R., Marcal, A.R.S., Mendonca, T. The use of texture for image classification of black & white air photographs. Int. J. Remote Sens. 2008, 29, 593-607.
25. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
26. 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.
27. Li, C., Wang, J., Wang, L., Hu, L., Gong, P. Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sens. 2014, 6, 964-983.
28. Rodriguez-Galiano, V.F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.M., Jeganathan, C. Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 2012, 121, 93-107.
29. Liu, D., Cai, S. A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Ann. Assoc. Am. Geogr. 2012, 102, 1329-1347.
30. Wang, J., Zhao, Y., Li, C., Yu, L., Liu, D., Gong, P. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution. ISPRS J. Photogramm. Remote Sens. 2014, in press.
31. Liu, D., Kelly, M., Gong, P. A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sens. Environ. 2006, 101, 167-180.
32. Liu, D., Song, K., Townshend, J.R.G., Gong, P. Using local transition probability models in markov random fields for forest change detection. Remote Sens. Environ. 2008, 112, 2222-2231.
33. Besag, J. On the statistical-analysis of dirty pictures. J. Roy. Stat. Soc. B Met. 1986, 48, 259-302.
34. Tuia, D., Volpi, M., Copa, L., Kanevski, M., Munoz-Mari, J. A survey of active learning algorithms for supervised remote sensing image classification. IEEE J. Sel. Top. Signal Process. 2011, 5, 606-617.
35. Huang, H.B., Li, Z., Gong, P., Cheng, X.A., Clinton, N., Cao, C.X., Ni, W.J., Wang, L. Automated methods for measuring DBH and tree heights with a commercial scanning LiDAR. Photogramm Eng. Remote Sens. 2011, 77, 219-227.
36. Zhu, X.L., Liu, D.S. Map-MRF approach to Landsat ETM plus SLC-off image classification. IEEE Trans. Geosci. Remote Sens. 2014, 52, 1131-1141.
37. Bolin, D., Lindstrom, J., Eklundh, L., Lindgren, F. Fast estimation of spatially dependent temporal vegetation trends using gaussian markov random fields. Comput. Stat. Data Anal. 2009, 53, 2885-2896.