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Article

Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery

1
Hazards & Vulnerability Research Institute, Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA
2
Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Received: 30 January 2020 / Revised: 27 February 2020 / Accepted: 7 March 2020 / Published: 10 March 2020
The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices’ thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster. View Full-Text
Keywords: urban land surface; Landsat imagery; change detection; urban index; earthquake; longitudinal study urban land surface; Landsat imagery; change detection; urban index; earthquake; longitudinal study
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MDPI and ACS Style

Derakhshan, S.; Cutter, S.L.; Wang, C. Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery. Remote Sens. 2020, 12, 895. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050895

AMA Style

Derakhshan S, Cutter SL, Wang C. Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery. Remote Sensing. 2020; 12(5):895. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050895

Chicago/Turabian Style

Derakhshan, Sahar, Susan L. Cutter, and Cuizhen Wang. 2020. "Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery" Remote Sensing 12, no. 5: 895. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050895

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