Selection of Imagery Change Detection Methods Concerning Seasonal Difference

Abstract:

Updating land cover with imagery change detection methods is a hot topic. Many imagery change detection methods have been developed from different views; however, these methods often have their specific applicability, indicating that there is no single method can be universally applicable for land cover updating especially in large area. The main challenge is that not all remote sensing images used in change detection would be aquired from the same season. In this paper, two typical change detection methods SGD and PCC were analyzed and selected respectively for land cover updating concerning the seasonal difference of multi-temporal remote sensed images. Experiment in a case study of Shandong province was conducted by using the two methods. Results indicated that SGD is suitable for consistent seasonal phase images, while PCC can be used for those inconsistent seasonal phase images. It can achieve better accuracy in land cover updating using suitable change detection method concerning seasonal difference.

• Periodical: Advanced Materials Research (Volumes 1010-1012)
• Pages: 1248-1253
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1010-1012.1248
• Online since: August 2014
• Authors: Hua Qiao Xing*, Miao Lu
• Keywords: Change Detection, Landsat, Seasonal Difference

References

[1] Foley J., et al. Global consequences of land use. Science, 2005 (309) 570-574.

[2] Feranec, J., Hazeu, G., Christensen., S., 2007. Corine land cover change detection in Europe (case studies of the Netherlands and Slovakia). Land Use Policy 24 (2007) 234–247.

DOI: 10.1016/j.landusepol.2006.02.002

[3] Chen, J., Gong, P., He, C., Pu, R., Shi, P., 2003. Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering &Remote Sensing 69 (4) 369–379.

DOI: 10.14358/pers.69.4.369

[4] Xian, G., & Homer, C., 2009. Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sensing of Environment, 114, 1676–1686.

DOI: 10.1016/j.rse.2010.02.018

[5] Xian, G., Collin, H., Fry, J., 2009. Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment 113 (6) 1133–1147.

DOI: 10.1016/j.rse.2009.02.004

[6] Jin, S., Yang, L., Danielson, P., 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132, 159–175.

DOI: 10.1016/j.rse.2013.01.012

[7] Lucas, R., Medcalf, K., Brown, A., 2011. Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data. ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 81–102.

DOI: 10.1016/j.isprsjprs.2010.09.004

[8] Were, K., Dick, Ø., Singh, B., 2013. Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya. Applied Geography 41 (2013) 75–86.

DOI: 10.1016/j.apgeog.2013.03.017

[9] Hulley, G., Veraverbeke, G., Hook, S., 2014. Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21). Remote Sensing of Environment 140 (2014) 755–765.

DOI: 10.1016/j.rse.2013.10.014

[10] Li, D., 2010. Remotely sensed images and GIS data fusion for automatic change detection. International Journal of Image and Data Fusion 1 (1) 99–108.

DOI: 10.1080/19479830903562074

[11] Singh, A., 1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 10 (6) 989–1003.

DOI: 10.1080/01431168908903939

[12] Chen, J., Lu, M., Chen X., 2013. A spectral gradient difference based approach for land cover change detection. ISPRS Journal of Photogrammetry and Remote Sensing 85 (2013) 1–12.

DOI: 10.1016/j.isprsjprs.2013.07.009

[13] Chen, X., Chen, J., Shi, Y., Yamaguchi, Y., 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 (7) 86–95.

DOI: 10.1016/j.isprsjprs.2012.05.006

[14] Hansen, M., Loveland, T., 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 68-74.

DOI: 10.1016/j.rse.2011.08.024