A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand

Abstract

Drought is a natural hazard which occurs in all climatic zones. The effect from drought can cause a serious problem for agricultural activities, economies and the environment. There is a need to characterize drought events in terms of drought severity, frequency and possibility of drought occurrence for better drought management. An examination of drought characteristics and drought severity using the Standardized Precipitation Index (SPI) and the Vegetation Condition Index (VCI) was carried out for different land cover types. The study examined how data mining techniques such as association rules could be used to elucidate the relationships between VCI and SPI in order to predict the possibility of drought occurrence. Rainfall datasets were collected from the Thai meteorological department for the period 1980–2009 and digitally encoded into a Geographic Information System database. SPI values were derived both temporally and spatially for quantitative measurement of drought events over the 30-year period. Monthly VCI values were calculated from NDVI data collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product (MOD13Q1). Data mining technique was introduced and applied to generate association rules between VCI and SPI to predict the possibility of drought occurrence. The results from multi-temporal SPI analysis shown drought event occurred more often for the 3- and 6-month SPI in October at the central and the northeastern part of the region. Spatial SPI revealed that high-drought-risk areas were in the southwest and extending to the central part of the region. The statistically significant correlations between monthly VCI and SPI at the multiple timescales were found for mixed deciduous forest in dry period. This result indicated vegetation condition for this forest type was sensitive for precipitation during dry period. Drought events were found to affect the rice crop in the central part of the region more, as observed from the negative correlation between VCI and SPI during growing season. The representative association rules from VCI and SPI revealed drought event also occurred for paddy field in the central part of the region. Drought periods within the growing season for this area are becoming more prevalent even with increase in annual rainfall. Shorter scale of SPI was found to be effective in characterizing drought conditions. This study combined the different level of software and dataset used which are able to predict future occurrence and severity of drought using the current condition. Results can be applied to assess drought severity and drought-affected areas for efficient drought management and planning

An Investigation of Drought Around Chi Watershed During Ten-Year Period Using Terra/Modis Data

“…In the area covered by plants, the reflective value in NIR wavelength would be higher than that in Red wavelength, thus making the NDVI value to be positive, whereas the soil surface, the open area, and the construction area would have similar reflective value between these two wavelengths thus making NDVI value to approach near zero (Park et al, 2008;Rotjanakusol & Laosuwan, 2019). In the case of water surface, the reflective value in NIR wavelength would be lower than Red wavelength thus making NDVI value to be negative (Thavorntam et al, 2015). The method in calculating NDVI is shown in Equation 1.…”

Reviewing the Potential of Sentinel-2 in Assessing the Drought

“…During the last decade, drought monitoring by using satellite images drastically increased [34,159,160]. The higher spectral and temporal resolution of satellite images is required for more precise and accurate results.…”

Drought Risk Assessment in Central Asia Using a Probabilistic Copula Function Approach

“…Secondly, the resolution and time series of CRU dataset are necessary for SPEI calculations and drought studies [22]. In addition, grid data have better spatial representation and continuous availability than traditional site observation data, playing a key role in the description of drought characteristics [23]. Lastly, the CRU dataset has been widely used in CA [4,24,25] and other regions [26][27][28][29][30].…”