Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data

References

• Bouziani, M., K. Goïta, and D.-C. He. 2010. “Automatic Change Detection of Buildings in Urban Environment from Very High Spatial Resolution Images Using Existing Geodatabase and Prior Knowledge.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 143–153. doi:10.1016/j.isprsjprs.2009.10.002.
   Web of Science ®Google Scholar
• Bundesamt für Kartographie und Geodäsie. 2013. “Digitales Basis-Landschaftsmodell (AAA-Modellierung) Basis-DLM (AAA).” Acessed March 5. http://www.geodatenzentrum.de/docpdf/basis-dlm-aaa.pdf
   Google Scholar
• Chen, X., J. Chen, Y. Shi, and Y. Yamaguchi. 2012. “An Automated Approach for Updating Land Cover Maps Based on Integrated Change Detection and Classification Methods.” ISPRS Journal of Photogrammetry and Remote Sensing 71: 86–95. doi:10.1016/j.isprsjprs.2012.05.006.
   Web of Science ®Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Coops, N. C., S. N. Gillanders, M. A. Wulder, S. E. Gergel, T. Nelson, and N. R. Goodwin. 2010. “Assessing Changes in Forest Fragmentation following Infestation Using Time Series Landsat Imagery.” Forest Ecology and Management 259 (12): 2355–2365. doi:10.1016/j.foreco.2010.03.008.
   Web of Science ®Google Scholar
• Coppin, P., I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin. 2004. “Digital Change Detection Methods in Ecosystem Monitoring: A Review.” International Journal of Remote Sensing 25 (9): 1565–1596. doi:10.1080/0143116031000101675.
   Web of Science ®Google Scholar
• Erbek, F. S., C. Özkan, and M. Taberner. 2004. “Comparison of Maximum Likelihood Classification Method with Supervised Artificial Neural Network Algorithms for Land Use Activities.” International Journal of Remote Sensing 25 (9): 1733–1748. doi:10.1080/0143116031000150077.
   Web of Science ®Google Scholar
• Friedl, M. A., D. S. Menashe, B. Tan, A. Schneider, N. Ramankutty, and A. Sibley. 2010. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets.” Remote Sensing of Environment 114: 168–182. doi:10.1016/j.rse.2009.08.016.
   Web of Science ®Google Scholar
• Gopal, S., C. E. Woodcock, and A. Strahler. 1999. “Fuzzy Neural Network Classification of Global Land cover from a 1° AVHRR Data Set.” Remote Sensing of Environment 67: 230–243. doi:10.1016/S0034-4257(98)00088-1.
   Web of Science ®Google Scholar
• Healey, S. P., W. B. Cohen, Z. Yang, and O. N. Krankina. 2005. “Comparison of Tasseled Cap-Based Landsat Data Structure for Use in Forest Disturbance Detection.” Remote Sensing of Environment 97: 301–310. doi:10.1016/j.rse.2005.05.009.
   Web of Science ®Google Scholar
• Henits, L., and L. Mucsi. 2012. “Analysis of the Connection between Urban Land Cover and Census Districts Using Geoinformatical Methods.” Acta Geographica Debrecina Landscape and Environment 6 (2): 52–67.
   Google Scholar
• Huang, C., L. S. Davis, and J. R. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Huang, C., S. N. Goward, J. G. Masek, N. Thomas, Z. Zhu, and J. E. Vogelmann. 2010. “An Automated Approach for Reconstructing Recent Forest Disturbance History Using Dense Landsat Time Series Stacks.” Remote Sensing of Environment 114: 183–198. doi:10.1016/j.rse.2009.08.017.
   Web of Science ®Google Scholar
• Hussain, M., D. Chen, A. Cheng, H. Wie, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches.” ISPRS Journal of Photogrammetry and Remote Sensing 80: 91–106. doi:10.1016/j.isprsjprs.2013.03.006.
   Web of Science ®Google Scholar
• Jensen, J. R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. Toronto: Prentice Hall.
   Google Scholar
• Liu, Q. J., T. Takamura, N. Takeuchi, and G. Shao. 2002. “Mapping of Boreal Vegetation of a Temperate Mountain in China by Multitemporal Landsat TM Imagery.” International Journal of Remote Sensing 23 (17): 3385–3405. doi:10.1080/01431160110076171.
   Web of Science ®Google Scholar
• Lu, D., P. Mausel, E. Brondízio, and E. Moran. 2004. “Change Detection Techniques.” International Journal of Remote Sensing 25 (12): 2365–2401. doi:10.1080/0143116031000139863.
   PubMed Web of Science ®Google Scholar
• Lu, D., E. Moran, and S. Hetrick. 2011. “Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban-Rural Frontier.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 298–306. doi:10.1016/j.isprsjprs.2010.10.010.
   PubMed Web of Science ®Google Scholar
• Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28 (5): 823–870. doi:10.1080/01431160600746456.
   PubMed Web of Science ®Google Scholar
• Lunetta, R. S., and M. Balogh. 1999. “Application of Multi-Temporal Landsat 5 TM Imagery for Wetland Identification.” Photogrammetric Engineering and Remote Sensing 65 (11): 1303–1310.
   Web of Science ®Google Scholar
• Masek, J. G., C. Huang, R. Wolfe, W. Cohen, F. Hall, and J. Kutler. 2008. “North American Forest Disturbance Mapped from Decadal Landsat Record.” Remote Sensing of Environment 112: 2914–2926. doi:10.1016/j.rse.2008.02.010.
   Web of Science ®Google Scholar
• Mucsi, L., B. Muladi, L. Henits, A. Farsang, and V. Albrecht. 2014. “Large Scale UHI Mapping Based on Spatial Iinformation Provided by Young Volunteers.” Carpathian Journal of Earth and Environmental Sciences 9 (2): 31–43.
   Web of Science ®Google Scholar
• Nackaerts, K., K. Vaesen, B. Muys, and P. Coppin. 2005. “Comparative Performance of a Modified Change Vector Analysis in Forest Change Detection.” International Journal of Remote Sensing 26 (5): 839–852. doi:10.1080/0143116032000160462.
   Web of Science ®Google Scholar
• Schneider, A. 2012. “Monitoring Land Cover Change in Urban and Peri-Urbanareas Using Dense Time Stacks of Landsat Satellite Data and a Data Mining Approach.” Remote Sensing of Environment 124: 689–704. doi:10.1016/j.rse.2012.06.006.
   Web of Science ®Google Scholar
• Singh, A. 1989. “Digital Change Detection Techniques Using Remotely Sensed Data.” International Journal of Remote Sensing 10 (6): 989–1003. doi:10.1080/01431168908903939.
   Web of Science ®Google Scholar
• Strahler, A. H. 1980. “The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data.” Remote Sensing of Environment 10: 135–163. doi:10.1016/0034-4257(80)90011-5.
   Web of Science ®Google Scholar
• Townshend, J. R. G., and C. O. Justice. 1995. “Spatial Variability of Images and the Monitoring of Changes in the Normalized Difference Vegetation Index.” International Journal of Remote Sensing 16: 2187–2195. doi:10.1080/01431169508954550.
   Web of Science ®Google Scholar
• Unger, J., T. Gál, J. Rakonczai, L. Mucsi, J. Szatmári, Z. Tobak, B. Van Leeuwen, and K. Fiala. 2010. “Modeling of the Urban Heat Island Pattern Based on the Relationship between Surface and Air Temperatures.” Időjárás/Quartely Journal of the Hungarian Meteorological Service 114: 287–302.
   Google Scholar
• Vogelmann, J. E., B. Tolk, and Z. Zhu. 2009. “Monitoring Forest Changes in the Southwestern United States Using Multitemporal Landsat Data.” Remote Sensing of Environment 113: 1739–1748. doi:10.1016/j.rse.2009.04.014.
   Web of Science ®Google Scholar
• Ward, D., S. R. Phinn, and A. T. Murray. 2000. “Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data.” The Professional Geographer 52: 371–386. doi:10.1111/0033-0124.00232.
   Web of Science ®Google Scholar
• Wolter, P. T., D. J. Mladenoff, G. E. Host, and T. R. Crow. 1995. “Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat Imagery.” Photogrammetric Engineering and Remote Sensing 61 (9): 1129–1143.
   Web of Science ®Google Scholar
• Yuan, F., K. E. Sawaya, B. C. Loeffelholz, and M. E. Bauer. 2005. “Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing.” Remote Sensing of Environment 98 (2–3): 317–328. doi:10.1016/j.rse.2005.08.006.
   Web of Science ®Google Scholar
• Yuan, F., C. Wu, and M. E. Bauer. 2008. “Comparison of Spectral Analysis Techniques for Impervious Surface Estimation Using Landsat Imagery.” Photogrammetric Engineering & Remote Sensing 74 (8): 1045–1055. doi:10.14358/PERS.74.8.1045.
   Web of Science ®Google Scholar
• Zhu, Z., and C. E. Woodcock. 2014. “Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data.” Remote Sensing of Environment 144: 152–171. doi:10.1016/j.rse.2014.01.011.
   Web of Science ®Google Scholar