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Remote Sens. 2017, 9(9), 893; https://doi.org/10.3390/rs9090893

A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images

1
Department of Civil Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
2
Department of Soil and Water Conservation, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 25 June 2017 / Revised: 4 August 2017 / Accepted: 23 August 2017 / Published: 28 August 2017
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Abstract

Vegetation recovery monitoring is critical for assessing denudation areas after landslides have occurred. A long-term and broad area investigation using remote sensing techniques is an efficient and cost-effective approach incorporating the consideration of radiometric correction and seasonality variations across multi-date satellite images. This paper investigates long-term vegetation recovery using 14 SPOT satellite images spanning from 1999 to 2011 over the landslide area of Mt. Jou-Jou in central Taiwan, which was caused by the Chi-Chi earthquake in 1999. The vegetation status was evaluated by the Normalized Difference Vegetation Index (NDVI) with radiometric correction between multi-date images based on pseudoinvariant features, and subsequently a vegetation recovery rate (VRR) model was empirically established after seasonality adjustment was performed on the multi-date NDVI images. An increasing tendency of the vegetation recovery in the landslide area of Mt. Jou-Jou appeared based on the NDVI value rising to 0.367 in March 2011 from −0.044 right after the catastrophic earthquake. The vegetation recovery rate with seasonality adjustment approached 81.5% for the total area and 81.3% for the landslide area through 12 years succession. The seasonality adjustment also enhanced the VRR model with a determination coefficient that increased from 0.883 to 0.916 for the landslide area and from 0.584 to 0.915 for the total area, highlighting the necessity of seasonality adjustment in multi-date vegetation observations using satellite images. Furthermore, the association between precipitation and NDVI was discussed, and the inverse relationship with the reoccurrence of high-intensity short-duration rainfall and yearly heavy rainfall was observed, in agreement with the on-site investigation. View Full-Text
Keywords: landslide; Normalized Difference Vegetation Index (NDVI); vegetation recovery rate; seasonality adjustment; multi-date satellite images landslide; Normalized Difference Vegetation Index (NDVI); vegetation recovery rate; seasonality adjustment; multi-date satellite images
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, M.-D.; Chen, S.-C.; Tsai, H.P. A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images. Remote Sens. 2017, 9, 893.

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