Assessment of MODIS-derived indices (2001–2013) to drought across Taiwan’s forests

• Published in: International journal of biometeorology 2018-05, Vol.62 (5), p.809-822
• Main Authors: Chang, Chung-Te, Wang, Hsueh-Ching, Huang, Cho-ying
• Format: Article
• Language: English
• Subjects: Animal Physiology; Bioclimatology; Biological and Medical Physics; Biophysics; Drought; Drought conditions; Drought index; Drought patterns; Droughts; Dry season; Earth and Environmental Science; Environment; Environmental Health; Feasibility studies; Forest ecosystems; Forests; High humidity; I.R. radiation; Imagery; Infrared Rays; Meteorology; MODIS; Near infrared radiation; Original Paper; Plant Physiology; Precipitation; Rain; Rainfall; Remote monitoring; Remote sensing; Satellite Imagery; Seasonal precipitation; Seasons; Standardized precipitation index; Taiwan; Temperature; Tropical Climate
• ISSN: 0020-7128; 1432-1254
• DOI: 10.1007/s00484-017-1482-2

Tropical and subtropical ecosystems, the largest terrestrial carbon pools, are very susceptible to the variability of seasonal precipitation. However, the assessment of drought conditions in these regions is often overlooked due to the preconceived notion of the presence of high humidity. Drought indices derived from remotely sensed imagery have been commonly used for large-scale monitoring, but feasibility of drought assessment may vary across regions due to climate regimes and local biophysical conditions. Therefore, this study aims to evaluate the feasibility of 11 commonly used MODIS-derived vegetation/drought index in the forest regions of Taiwan through comparison with the station-based standardized precipitation index with a 3-month time scale (SPI3). The drought indices were further transformed (standardized anomaly, SA) to make them better delineate the spatiotemporal variations of drought conditions. The results showed that the Normalized Difference Infrared Index utilizing the near-infrared and shortwave infrared bands (NDII6) may be more superior to other indices in delineating drought patterns. Overall, the NDII6 SA-SPI3 pair yielded the highest correlation (mean r  ± standard deviation = 0.31 ± 0.13) and was most significant in central and south Taiwan ( r  = 0.50–0.90) during the cold, dry season (January and April). This study illustrated that the NDII6 is suitable to delineate drought conditions in a relatively humid region. The results suggested the better performance of the NDII6 SA-SPI3 across the high climate gradient, especially in the regions with dramatic interannual amplifications of rainfall. This synthesis was conducted across a wide bioclimatic gradient, and the findings could be further generalized to a much broader geographical extent.