8 October 2015 Assessing rice crop damage and restoration using remote sensing in tsunami-affected areas, Japan
Chi-Farn Chen, Nguyen-Thanh Son, Cheng-Ru Chen, Kohei Cho, Ya-Yun Hsiao, Shou-Hao Chiang, Ly-Yu Chang
Author Affiliations +
Abstract
The tsunami that occurred on March 11, 2011, in Japan caused widespread devastation of infrastructure and rice cultivation areas. This study assessed the rice growing areas damaged due to this event and the restoration progress using moderate resolution imaging spectroradiometer data. The data were processed for 2010 to 2013, comprising four main steps: (1) data preprocessing to construct the smooth time-series enhanced vegetation index 2 data, (2) rice crop mapping using the dynamic time warping algorithm, (3) accuracy assessment, and (4) change analysis of rice cultivation areas. The mapping results validated using the ground reference data indicated overall accuracies and kappa coefficients generally >84.3% and 0.69, respectively. The results, also verified with the government’s rice area statistics, reaffirmed a close correlation between these two datasets (R2<0.77). When examining changes of rice cultivation between 2010 and 2013, the rice area damaged by the tsunami during 2010 to 2011 was ∼17,550  ha. The rice area restored after one year (2012) was ∼6663  ha, but this regressed slightly in 2013 (5656 ha). Such information could be used by officials to better understand the tsunami-damaged rice area and the restoration progress for crop management in the study region.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2015/$25.00 © 2015 SPIE
Chi-Farn Chen, Nguyen-Thanh Son, Cheng-Ru Chen, Kohei Cho, Ya-Yun Hsiao, Shou-Hao Chiang, and Ly-Yu Chang "Assessing rice crop damage and restoration using remote sensing in tsunami-affected areas, Japan," Journal of Applied Remote Sensing 9(1), 096002 (8 October 2015). https://doi.org/10.1117/1.JRS.9.096002
Published: 8 October 2015
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Associative arrays

Remote sensing

MODIS

Earth observing sensors

Image classification

Landsat

Vegetation

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