Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI)

Dutta, Debojyoti; Kundu, Arnab; Patel, N. R.; Saha, Sudip; Siddiqui, Azizur Rahman

doi:10.1016/j.ejrs.2015.03.006

Cited by 259 publications

(159 citation statements)

References 38 publications

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“…Rainfall is a key factor that controls dynamic changes in vegetation growth in arid and semi-arid regions as vegetation cover in those regions are extremely susceptible to the quantity, duration and frequency of rainfall (Fravolini et al, 2005;Liu et al, 2012;Gamon et al, 2013;Dutta et al, 2015;Kundu et al, 2015c). Past studies revealed that fluctuation of NDVI in drylands is consistent with spatial and temporal variations in rainfall (Fabricante et al, 2009;Kundu & Dutta, 2011;Kundu et al, 2015aKundu et al, ,b, 2016Kundu et al, , 2017.…”

Section: Introductionmentioning

confidence: 89%

A Geo‐spatial study for analysing temporal responses of NDVI to rainfall

Kundu

Denis

Patel

et al. 2017

Singap J Trop Geogr

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Climate change has become a serious concern worldwide owing to its multifaceted impact upon the physical as well as socio-economic environment (IPCC, 2013). Vulnerability to climate change is much higher in the developing countries like India, where the economy is mainly agro-based and productivity from the agricultural sector is dependent upon summer monsoon rainfall. Hence, assessing the quantitative relationship between vegetation patterns and climatic influence has become an increasingly important study conducted on regional and global scales. As vegetation cover plays a key role in conserving the natural environment, studying the spatio-temporal trend of vegetation is crucial in identifying changes in the natural environment. We analysed the spatial responses of SPOT-VGT NDVI to TRMM based rainfall during a sixteen year period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) in the Bundelkhand region of Central India. The Normalized Difference Vegetation Index (NDVI) has proven to be a strong indicator of global vegetation productivity. Among climatic factors, rainfall robustly influences both spatial and temporal outline of NDVI. In this study, we used linear regression for analysing the statistical relationship among NDVI and rainfall and their trends. The study reveals a varying pattern of vegetation dynamics in response to rainfall over the area.

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Section: Introductionmentioning

confidence: 89%

A Geo‐spatial study for analysing temporal responses of NDVI to rainfall

Kundu

Denis

Patel

et al. 2017

Singap J Trop Geogr

Self Cite

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“…In addition, a decadal average (2002-2012) of the planting acreage and production weight of corn were also obtained from NASS at a county-scale. Using the decadal average, the yield anomaly index (YAI) [50] of every county was calculated for 2012. YAI is designed to identify deviation of yield for a particular year from its long term trend.…”

Section: Study Area and Datamentioning

confidence: 99%

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

et al. 2017

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Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US.

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“…NDVI is frequently analysed by calculating Vegetation Condition Index (VCI), which compares the current NDVI to the observed values of this index in previous years (Gebrehiwot et al, 2011;Ozelkan et al, 2016) and has a good correlation with the SPI values (Dutta et al, 2015). In different regions of the world, the relationship between the 3- (Gebrehiwot et al, 2011), 6-or 9-month SPI (Quiring and Ganesh, 2010) and VCI values were established.…”

Section: Introductionmentioning

confidence: 99%

Drought identification in the eastern Baltic region using NDVI

et al. 2017

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Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53-60 • N and 20-30 • E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1-to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5-6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3-and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

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Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI)

Cited by 259 publications

References 38 publications

A Geo‐spatial study for analysing temporal responses of NDVI to rainfall

A Geo‐spatial study for analysing temporal responses of NDVI to rainfall

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Drought identification in the eastern Baltic region using NDVI

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