Spatiotemporal Analysis of Land Cover Change Based on Local Climate Zones

Xuan XIE; Chao-min CHEN; Yun DU; Ba-gan HASI

doi:10.3969/j.issn.1672-8785.2021.06.007

[1] Turner B L, Skole D, Sanderson S, et al. Land-use and land-cover change in science/research plan[R]. Stockholm: Royal Swedish Academy of Sciences, 1995.

[4] Bagan H, Yamagata Y. Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells[J]. Environmental Research Letters, 2014, 9(6): 064015.

[5] Nolè G, Murgante B, Calamita G, et al. Evaluation of Urban Sprawl from space using open source technologies[J]. Ecological Informatics, 2015, 26(1): 151-161.

[8] Smidt S J, Tayyebi A, Kendall A D, et al. Agricultural implications of providing soil-based constraints on urban expansion: Land use forecasts to 2050[J]. Journal of Environmental Management, 2018, 217(6): 677-689.

[9] Bagan H, Millington A, Takeuchi W, et al. Spatiotemporal analysis of deforestation in the Chapare region of Bolivia using LANDSAT images[J]. Land Degradation & Development, 2020, 31(18): 3024-3039.

[15] Anderson J R, Hardy E E， Roach J T, et al. A land use and land cover classification system for use with remote sensor data[M]. Washington: US Government Printing Office, 1976.

[16] Liu J Y, Zhuang D F, Luo D, et al. Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data[J]. International Journal of Remote Sensing, 2003, 24(12): 2485-2500.

[18] Stewart I D, Oke T R. Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012, 93(12): 1879-1900.

[19] Bechtel B, Daneke C. Classification of local climate zones based on multiple earth observation data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(4): 1191-1202.

[20] Xu Y, Ren C, Cai M, et al. Classification of local climate zones using ASTER and Landsat data for high-density cities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7): 3397-3405.

[23] Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends[J]. Remote Sensing of Environment, 2012, 117(3): 34-49.

[27] Amarsaikhan D, Ganzorig M, Ache P, et al. The integrated use of optical and InSAR data for urban land-cover mapping[J]. International Journal of Remote Sensing, 2007, 28(6): 1161-1171.

[28] Zhu Z, Woodcock C E, Rogan J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data[J]. Remote Sensing of Environment, 2012, 117(2): 72-82.

[29] La Y, Bagan H, Yamagata Y. Urban land cover mapping under the Local Climate Zone scheme using Sentinel-2 and PALSAR-2 data[J]. Urban Climate, 2020, 33(1): 100661.

[30] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python[J]. Journal of Machine Learning Research, 2011, 12(2): 2825-2830.

[31] Breiman L. Random forests[J]. Machine learning, 2001, 45(1): 5-32.

[32] Breiman L. Bagging predictors[J]. Machine learning, 1996, 24(2): 123-140.

[33] Wolpert D H, Macready W G. An efficient method to estimate bagging′s generalization error[J]. Machine Learning, 1999, 35(1): 41-55.