587
Views
0
CrossRef citations to date
0
Altmetric
Articles

Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

ORCID Icon, , & ORCID Icon
Pages 2487-2502 | Received 20 Feb 2023, Accepted 10 Jun 2023, Published online: 03 Jul 2023

References

  • Braaten, J. D., W. B. Cohen, and Z. Yang. 2015. “Automated Cloud and Cloud Shadow Identification in Landsat MSS Imagery for Temperate Ecosystems.” Remote Sensing of Environment 169: 128–138. doi:10.1016/j.rse.2015.08.006.
  • Chen, M., H. Zhang, W. Liu, and W. Zhang. 2014. “The Global Pattern of Urbanization and Economic Growth: Evidence from the Last Three Decades.” PLoS ONE 9 (8): e103799. doi:10.1371/journal.pone.0103799.
  • Congalton, R. G., R. G. Oderwald, and R. A. Mead. 1983. “Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques.” Photogrammetric Engineering & Remote Sensing 49 (12): 1671–1678.
  • Crippen, R., S. Buckley, P. Agram, E. Belz, E. Gurrola, S. Hensley, M. Kobrick, et al. 2016. “Nasadem Global Elevation Model: Methods and Progress.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4: 125–128. doi:10.5194/isprs-archives-XLI-B4-125-2016.
  • Feng, S., and F. Fan. 2021. “Impervious Surface Extraction Based on Different Methods from Multiple Spatial Resolution Images: A Comprehensive Comparison.” International Journal of Digital Earth 14 (9): 1148–1174. doi:10.1080/17538947.2021.1936227.
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of new Datasets.” Remote Sensing of Environment 114 (1): 168–182. doi:10.1016/j.rse.2009.08.016.
  • Ghorbanian, A., M. Kakooei, M. Amani, S. Mahdavi, A. Mohammadzadeh, and M. Hasanlou. 2020. “Improved Land Cover map of Iran Using Sentinel Imagery Within Google Earth Engine and a Novel Automatic Workflow for Land Cover Classification Using Migrated Training Samples.” ISPRS Journal of Photogrammetry and Remote Sensing 167: 276–288. doi:10.1016/j.isprsjprs.2020.07.013.
  • Gong, P., X. Li, J. Wang, Y. Bai, B. Chen, T. Hu, X. Liu, et al. 2020. “Annual Maps of Global Artificial Impervious Area (GAIA) Between 1985 and 2018.” Remote Sensing of Environment 236: 111510. doi:10.1016/j.rse.2019.111510.
  • Gong, P., H. Liu, M. Zhang, C. Li, J. Wang, H. Huang, N. Clinton, et al. 2019. “Stable Classification with Limited Sample: Transferring a 30-m Resolution Sample set Collected in 2015 to Mapping 10-m Resolution Global Land Cover in 2017.” Science Bulletin 64 (6): 370–373. doi:10.1016/j.scib.2019.03.002.
  • Gurney, C. M. 1981. “The use of Contextual Information to Improve Land Cover Classification of Digital Remotely Sensed Data.” International Journal of Remote Sensing 2 (4): 379–388. doi:10.1080/01431168108948372.
  • Herold, M., N. C. Goldstein, and K. C. Clarke. 2003. “The Spatiotemporal Form of Urban Growth: Measurement, Analysis and Modeling.” Remote Sensing of Environment 86 (3): 286–302. doi:10.1016/S0034-4257(03)00075-0.
  • Huang, H., J. Wang, C. Liu, L. Liang, C. Li, and P. Gong. 2020. “The Migration of Training Samples Towards Dynamic Global Land Cover Mapping.” ISPRS Journal of Photogrammetry and Remote Sensing 161: 27–36. doi:10.1016/j.isprsjprs.2020.01.010.
  • Huang, C., J. Yang, N. Clinton, L. Yu, H. Huang, I. Dronova, and J. Jin. 2021. “Mapping the Maximum Extents of Urban Green Spaces in 1039 Cities Using Dense Satellite Images.” Environmental Research Letters 16 (6): 064072. doi:10.1088/1748-9326/ac03dc.
  • Karra, K., C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. M. Mathis, and S. P. Brumby. 2021. “Global land use / land cover with Sentinel 2 and deep learning.” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4704–4707. doi:10.1109/IGARSS47720.2021.955349.
  • Li, C., J. Wang, L. Wang, L. Hu, and P. Gong. 2014. “Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery.” Remote Sensing 6 (2): 964–983. doi:10.3390/rs6020964.
  • Liu, H., P. Gong, J. Wang, N. Clinton, Y. Bai, and S. Liang. 2020. “Annual Dynamics of Global Land Cover and its Long-Term Changes from 1982 to 2015.” Earth System Science Data 12 (2): 1217–1243. doi:10.5194/essd-12-1217-2020.
  • Liu, X., G. Hu, Y. Chen, X. Li, X. Xu, S. Li, F. Pei, and S. Wang. 2018. “High-resolution Multi-Temporal Mapping of Global Urban Land Using Landsat Images Based on the Google Earth Engine Platform.” Remote Sensing of Environment 209: 227–239. doi:10.1016/j.rse.2018.02.055.
  • Melchiorri, M., A. J. Florczyk, S. Freire, M. Schiavina, M. Pesaresi, and T. Kemper. 2018. “Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer.” Remote Sensing 10 (5): 768. doi:10.3390/rs10050768.
  • Mellor, A., S. Boukir, A. Haywood, and S. Jones. 2015. “Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin.” ISPRS Journal of Photogrammetry and Remote Sensing 105: 155–168. doi:10.1016/j.isprsjprs.2015.03.014.
  • Mi, H., G. Qiao, T. Li, and S. Qiao. 2015. “Declassified Historical Satellite Imagery from 1960s and Geometric Positioning Evaluation in Shanghai, China.” In Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2014. Communications in Computer and Information Science, Vol 482, edited by F. Bian, and Y. Xie, 283–292. Berlin, Germany: Springer, Berlin, Heidelberg.
  • Naboureh, A., A. Li, H. Ebrahimy, J. Bian, M. Azadbakht, M. Amani, G. Lei, and X. Nan. 2021. “Assessing the Effects of Irrigated Agricultural Expansions on Lake Urmia Using Multi-Decadal Landsat Imagery and a Sample Migration Technique Within Google Earth Engine.” International Journal of Applied Earth Observation and Geoinformation 105: 102607. doi:10.1016/j.jag.2021.102607.
  • Nowosad, J., T. F. Stepinski, and P. Netzel. 2019. “Global Assessment and Mapping of Changes in Mesoscale Landscapes: 1992–2015.” International Journal of Applied Earth Observation and Geoinformation 78: 332–340. doi:10.1016/j.jag.2018.09.013.
  • Pesaresi, M., and P. Politis. 2022. “GHS Built-up Surface Grid, Derived from Sentinel2 Composite and Landsat, Multitemporal (1975-2030).” European Commission, Joint Research Centre. Accessed July 20, 2023. doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
  • Phan, D. C., T. H. Trung, V. T. Truong, T. Sasagawa, T. P. T. Vu, D. T. Bui, M. Hayashi, T. Tadono, and K. N. Nasahara. 2021. “First Comprehensive Quantification of Annual Land use/Cover from 1990 to 2020 Across Mainland Vietnam.” Scientific Reports 11 (1): 9979. doi:10.1038/s41598-021-89034-5.
  • Qiu, C., M. Schmitt, C. Geiß, T. K. Chen, and X. X. Zhu. 2020. “A Framework for Large-Scale Mapping of Human Settlement Extent from Sentinel-2 Images via Fully Convolutional Neural Networks.” ISPRS Journal of Photogrammetry and Remote Sensing 163: 152–170. doi:10.1016/j.isprsjprs.2020.01.028.
  • Rahman, A., H. M. Abdullah, M. T. Tanzir, M. J. Hossain, B. M. Khan, M. G. Miah, and I. Islam. 2020. “Performance of Different Machine Learning Algorithms on Satellite Image Classification in Rural and Urban Setup.” Remote Sensing Applications: Society and Environment 20: 100410. doi:10.1016/j.rsase.2020.100410.
  • Saleem, A., R. Corner, and J. Awange. 2018. “On the Possibility of Using CORONA and Landsat Data for Evaluating and Mapping Long-Term LULC: Case Study of Iraqi Kurdistan.” Applied Geography 90: 145–154. doi:10.1016/j.apgeog.2017.12.007.
  • Schneider, A., M. A. Friedl, and D. Potere. 2010. “Mapping Global Urban Areas Using MODIS 500-m Data: New Methods and Datasets Based on ‘Urban Ecoregions’.” Remote Sensing of Environment 114 (8): 1733–1746. doi:10.1016/j.rse.2010.03.003.
  • Seto, K. C., and M. Fragkias. 2005. “Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics.” Landscape Ecology 20 (7): 871–888. doi:10.1007/s10980-005-5238-8.
  • Toll, D. L. 1985a. “Analysis of Digital LANDSAT MSS and SEASAT SAR Data for use in Discriminating Land Cover at the Urban Fringe of Denver, Colorado.” International Journal of Remote Sensing 6 (7): 1209–1229. doi:10.1080/01431168508948273.
  • Toll, D. L. 1985b. “Landsat-4 Thematic Mapper scene characteristics of a suburban and rural area (USA).” Palaeogeography, Palaeoclimatology, Palaeoecology 49 (9): 355–382. doi:10.1016/0031-0182(85)90061-6.
  • Wang, M., D. Mao, Y. Wang, K. Song, H. Yan, M. Jia, and Z. Wang. 2022. “Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine.” Remote Sensing 14 (13): 3191. doi:10.3390/rs14133191.
  • Witt, R. G., T. B. Minor, and R. S. Sekhon. 2007. “Use of HCMM Thermal Data to Improve Accuracy of MSS Land-Surface Classification Mapping.” International Journal of Remote Sensing 6 (10): 1623–1636. doi:10.1080/01431168508948310.
  • Yan, X., and Z. Niu. 2021. “Reliability Evaluation and Migration of Wetland Samples.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 8089–8099. doi:10.1109/JSTARS.2021.3102866.
  • Zhang, X., L. Liu, X. Chen, Y. Gao, S. Xie, and J. Mi. 2021. “GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 m Using Time-Series Landsat Imagery.” Earth System Science Data 13 (6): 2753–2776. doi:10.5194/essd-13-2753-2021.
  • Zhang, H. K., and D. P. Roy. 2017. “Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification.” Remote Sensing of Environment 197: 15–34. doi:10.1016/j.rse.2017.05.024.
  • Zhu, Q., Y. Wang, J. Liu, X. Li, H. Pan, and M. Jia. 2021. “Tracking Historical Wetland Changes in the China Side of the Amur River Basin Based on Landsat Imagery and Training Samples Migration.” Remote Sensing 13 (11): 2161. doi:10.3390/rs13112161.