A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data

Remote Sensing of Environment

Volumn 166, Issue , 2015, Pages 78-90

  Li, Xuecao ^a   Gong, Peng ^a,b,c   Liang, Lu ^a,b  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c JOINT CENTER FOR GLOBAL CHANGE STUDIES   (China)

Author keywords

Change detection; Temporal consistency check; Time series; Urban land

Indexed keywords

CLASSIFICATION (OF INFORMATION); MAPPING; SIGNAL DETECTION; TIME SERIES;

AVERAGE GROWTH RATE; CHANGE DETECTION; HEURISTIC REASONING; INDEPENDENT SAMPLES; MAPPING OF URBAN AREAS; RAPID URBANIZATIONS; SPECTRAL INFORMATION; TEMPORAL CONSISTENCY;

URBAN GROWTH;

ACCURACY ASSESSMENT; LAND USE CHANGE; LANDSAT; MAPPING METHOD; NDVI; SATELLITE DATA; SPECTRAL ANALYSIS; TIME SERIES ANALYSIS; URBAN AREA; URBAN DEVELOPMENT; URBAN GROWTH; URBANIZATION;

BEIJING [CHINA]; CHINA;

EID: 84931271975     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.06.007     Document Type: Article

References (72)

1. Adler-Golden S.M., Matthew M.W., Bernstein L.S., Levine R.Y., Berk A., Richtsmeier S.C., et al. Atmospheric correction for shortwave spectral imagery based on MODTRAN4. SPIE's International Symposium on Optical Science, Engineering, and Instrumentation 1999, 61-69. International Society for Optics and Photonics.
2. Batty M., Torrens P.M. Modelling and prediction in a complex world. Futures 2005, 37:745-766.
3. Breiman L. Random Forests. Machine Learning 2001, 45:5-32.
4. Cai S., Liu D., Sulla-Menashe D., Friedl M.A. Enhancing MODIS land cover product with a spatial-temporal modeling algorithm. Remote Sensing of Environment 2014, 147:243-255.
5. Castrence M., Nong D.H., Tran C.C., Young L., Fox J. Mapping urban transitions using multi-temporal Landsat and DMSP-OLS night-time lights imagery of the Red River Delta in Vietnam. Land 2014, 3:148-166.
6. Chander G., Markham B.L., Helder D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 2009, 113:893-903.
7. Chen J., Gong P., He C., Pu R., Shi P. Land use/cover change detection using improved change vector analysis. Photogrammetric Engineering & Remote Sensing 2003, 69:369-379.
8. Chen Y., Li X., Liu X., Ai B. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science 2014, 28:234-255.
9. DeFries R.S., Rudel T., Uriarte M., Hansen M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience 2010, 3:178-181.
10. Du J., Thill J.-C., Peiser R.B., Feng C. Urban land market and land-use changes in post-reform China: A case study of Beijing. Landscape and Urban Planning 2014, 124:118-128.
11. Gao F., de Colstoun E.B., Ma R., Weng Q., Masek J.G., Chen J., et al. Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the Yangtze River Delta, China. International Journal of Remote Sensing 2012, 33:7609-7628.
12. Georgescu M., Morefield P.E., Bierwagen B.G., Weaver C.P. Urban adaptation can roll back warming of emerging megapolitan regions. Proceedings of the National Academy of Sciences 2014, 111:2909-2914.
13. Ghimire B., Rogan J., Galiano V.R., Panday P., Neeti N. An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing 2012, 49:623-643.
14. Gong P., Howarth P.J. The use of structural information for improving land-cover classification accuracies at the rural-urban fringe. Photogrammetric Engineering & Remote Sensing 1990, 56:67-73.
15. Gong P., Howarth P.J. Frequency-based contextual classification and gray-level vector reduction for land-use identification. Photogrammetric Engineering & Remote Sensing 1992, 58:423-437.
16. Gong P., Liang S., Carlton E.J., Jiang Q., Wu J., Wang L., et al. Urbanisation and health in China. Lancet 2012, 379:843-852.
17. Gong P., Wang J., Yu L., Zhao Y., Zhao Y., Liang L., et al. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing 2013, 34:2607-2654.
18. Gray J., Song C. Consistent classification of image time series with automatic adaptive signature generalization. Remote Sensing of Environment 2013, 134:333-341.
19. 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 Sensing of Environment 2013, 139:68-80.
20. Hansen M.C., Loveland T.R. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment 2012, 122:66-74.
21. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A., et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342:850-853.
22. He C., Okada N., Zhang Q., Shi P., Li J. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning 2008, 86:79-91.
23. Im J., Lu Z., Rhee J., Quackenbush L.J. Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sensing of Environment 2012, 117:102-113.
24. Jia K., Liang S., Zhang N., Wei X., Gu X., Zhao X., et al. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing 2014, 93:49-55.
25. Latifovic R., Rouliot D. Multitemporal landcover mapping for Canada: Methodology and products. Canadian Journal of Remote Sensing 2005, 31:347-363.
26. Li X., Liu X., Gong P. Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model. International Journal of Geographical Information Science 2015, 10.1080/13658816.2014.997237.
27. Li X., Liu X., Yu L. Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM images. International Journal of Remote Sensing 2014, 35:1481-1495.
28. Li X., Liu X., Yu L. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science 2014, 28:1317-1335.
29. Li G., Lu D., Moran E., Hetrick S. Mapping impervious surface area in the Brazilian Amazon using Landsat imagery. GIScience & Remote Sensing 2013, 50:172-183.
30. 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 Sensing 2014, 6(2):964-983.
31. Li X., Yeh A.G.-O. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science 2000, 14(2):131-152.
32. Liang L., Xu B., Chen Y.L., Liu Y., Cao W.C., Fang L.Q., et al. Combining spatial temporal and phylogenetic analysis approaches for improved understanding on global H5N1 transmission. PloS One 2010, 5:e13575.
33. Liu D., Cai S. A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers 2012, 102:1329-1347.
34. Liu D., Kelly M., Gong P. A spatio-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sensing of Environment 2006, 101:167-180.
35. Long Y., Gu Y., Han H. Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing. Landscape and Urban Planning 2012, 108:103-111.
36. Lu D., Mausel P., Brondízio E., Moran E. Change detection techniques. International Journal of Remote Sensing 2004, 25:2365-2401.
37. Lu D., Weng Q. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing 2004, 70:1053-1062.
38. Lunetta R.S., Johnson D.M., Lyon J.G., Crotwell J. Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sensing of Environment 2004, 128:246-258.
39. Masek J.G., Vermote E.F., Saleous N.E., Wolfe R., Hall F.G., Huemmrich K.F., et al. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters 2006, 3:68-72.
40. Mertes C.M., Schneider A., Sulla-Menashe D., Tatem A.J., Tan B. Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment 2015, 158:331-347.
41. Olofsson P., Foody G.M., Herold M., Stehman S.V., Woodcock C.E., Wulder M.A. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 2014, 148:42-57.
42. Ridd M.K. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing 1995, 16:2165-2185.
43. 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 Sensing of Environment 2012, 121:93-107.
44. Roy D.P., Wulder M.A., Loveland T.R., C.E.W., Allen R.G., Anderson M.C., et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment 2014, 145:154-172.
45. Schneider A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment 2012, 124:689-704.
46. Schneider A., Friedl M.A., Potere D. Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. Remote Sensing of Environment 2010, 114:1733-1746.
47. Schneider A., Mertes C.M. Expansion and growth in Chinese cities, 1978-2010. Environmental Research Letters 2014, 9:024008.
48. Seto K.C., Fragkias M. Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology 2005, 20:871-888.
49. Seto K.C., Kaufmann R.K., Woodcock C.E. Landsat reveals China's farmland reserves, but they're vanishing fast. Nature 2000, 406. 121-121.
50. Seto K.C., Woodcock C.E., Song C., Huang X., Lu J., Kaufmann R.K. Monitoring land-use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing 2002, 23:1985-2004.
51. Sexton J.O., Song X.-P., Huang C., Channan S., Baker M.E., Townshend J.R. Urban growth of the Washington, D.C.-Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sensing of Environment 2013, 129:42-53.
52. Sexton J.O., Urban D.L., Donohue M.J., Song C. Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record. Remote Sensing of Environment 2013, 128:246-258.
53. Singh A. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 1989, 10:989-1003.
54. Solecki W., Seto K.C., Marcotullio P.J. It's time for an urbanization science. Environment 2013, 55:12-17.
55. Stehman S.V. Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing 2009, 30:5243-5272.
56. Turner B.L., Lambin E.F., Reenberg A. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences 2007, 104:20666-20671.
57. Vieira M.A., Formaggio A.R., Rennó C.D., Atzberger C., Aguiar D.A., Mello M.P. Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas. Remote Sensing of Environment 2012, 123:553-562.
58. Wang L., Gong P., Ying Q., Yang Z., Cheng X., Ran Q. Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm. International Journal of Remote Sensing 2010, 31:1411-1426.
59. Wang L., Li C.C., Ying Q., Cheng X., Wang X.Y., Li X.Y., et al. China's urban expansion from 1990 to 2010 determined with satellite remote sensing. Chinese Science Bulletin 2012, 57:2802-2812.
60. Wang J., Zhao Y.Y., Li C.C., Yu L., Liu D., Gong P. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250m resolution. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 103:38-47.
61. Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment 2012, 117:34-49.
62. White R., Engelen G. Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land-use patterns. Environment & Planning A 1993, 25:1175-1199.
63. Woodcock C.E., Allen R., Anderson M., Belward A., Bindschadler R., Cohen W., et al. Free access to Landsat imagery. Science (New York, NY) 2008, 320:1011.
64. Wu Q., Li H.-q., Wang R.-s., Paulussen J., He Y., Wang M., et al. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning 2006, 78:322-333.
65. Xian G., Homer C., Dewitz J., Fry J., Hossain N., Wickham J. The change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogrammetric Engineering & Remote Sensing 2011, 77:758-762.
66. Yang J., Huang C.H., Zhang Z.Y., Wang L. The temporal trend of urban green coverage in major Chinese cities between 1990 and 2010. Urban Forestry & Urban Greening 2014, 13:19-27.
67. Yu L., Wang J., Gong P. Improving 30m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach. International Journal of Remote Sensing 2013, 34:5851-5867.
68. Yu L., Wang J., Li X., Li C., Zhao Y., Gong P. A multi-resolution global land cover dataset through multisource data aggregation. Science China Earth Sciences 2014, 44:1646-1660.
69. Zhang Z., Wang X., Zhao X., Liu B., Yi L., Zuo L., Wen Q., Liu F., Xu J., Hu S. A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sensing of Environment 2014, 149:142-154.
70. Zhao Y., Gong P., Yu L., Hu L., Li X., Li C., et al. Towards a common validation sample set for global land cover mapping. International Journal of Remote Sensing 2014, 35:4795-4814.
71. Zhong L., Gong P., Biging G.S. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment 2014, 140:1-13.
72. Zhu Z., Woodcock C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 2012, 118:83-94.