Multi-Temporal Independent Component Analysis and Landsat 8 for Delineating Maximum Extent of the 2013 Colorado Front Range Flood

Remote Sensing

Stephen M. Chignell³  Ryan S. Anderson⁴  Paul H. Evangelista³  Melinda J. Laituri³  David M. Merritt¹  George P. Petropoulos² 

[1] National Watershed, Fish and Wildlife Staff, Natural Resource Research Center, USDA Forest Service, Fort Collins, CO 80526, USA; E-Mail:Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA;;Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA; E-Mails:;Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; E-Mail:

关键词: change detection;    Colorado Front Range;    flood;    independent component analysis;    inundation mapping;    Landsat 8;   

DOI  :  10.3390/rs70809822

来源: mdpi

【 摘 要 】

Maximum flood extent—a key data need for disaster response and mitigation—is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre- and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region’s ongoing hydrologic research and mitigation planning.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.