A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images
AbstractVegetation 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
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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.
Yang M-D, Chen S-C, Tsai HP. A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images. Remote Sensing. 2017; 9(9):893.Chicago/Turabian Style
Yang, Ming-Der; Chen, Su-Chin; Tsai, Hui Ping. 2017. "A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images." Remote Sens. 9, no. 9: 893.
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