AVHRR-based vegetation and temperature condition indices for drought detection in Argentina

Seiler, Roberto; Kogan, Felix; Sullivan, James A.

doi:10.1016/s0273-1177(97)00884-3

Cited by 76 publications

(32 citation statements)

References 11 publications

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“…In locations with a limited number of sampling gauges, remote sensing data may be the only available information source for drought monitoring [6,7]. Satellite-based drought indices such as the normalized difference vegetation index (NDVI)-based vegetation condition index (VCI) [8] have been widely used for detecting the onset of drought and measuring the intensity, duration, and impact of drought globally [8][9][10][11][12][13][14][15]. The obvious advantage of VCI is that it can be easily computed owing to the fact that it does not require station observation data, and as a satellite-based drought product it can provide near real-time data over the globe at a relatively high spatial resolution [16].…”

Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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“…SPI based drought identification has been utilized by [4] to monitor the 1996 drought in the United States of America. Although it is quite a recent index, the SPI was used in Turkey [5], Argentina [6], Canada [7], Spain [8], Korea [9], Hungary [10], China [11] and Europe [12] for real time monitoring or retrospective analysis of droughts. NDVI-rainfall relationship was also used in monitoring drought on a large scale in north east Thailand [13], East Asia [14] and the Central Great Plains of US [15] using NOAA/AVHRR data.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Drought Modeling Using Remote Sensing

Dodamani¹,

R²,

Mahajan³

2015

IJESD

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Abstract-Maharashtra is one of the worst drought affected states in India, and as a result the agricultural productions has kept low. In the present study Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) obtained from MODIS data (MOD13Q1) and sunspot number which represents the solar activity has been considered for drought modeling in that part of Krishna Basin which lies in Maharashtra. Two multiple linear regression models for predicting agricultural drought are developed for Kharif season, based on the data for a period of 13 years from 2000 to 2012. In the first model, previous NDVI and SPI values and in the second model Sunspot data along with NDVI and SPI values are used as variables. It was found that the coefficient of determination of the second model has improved over the first model, which suggests the high significance of solar activity in the occurrence of drought. The correlation between NDVI and SPI has been utilized in the study. Highly significant correlations were obtained between current NDVI and SPI of various time lags in the assured rainfall zone and the scarcity Zone. Crop yield model was developed from the predicted NDVI for the major crops and was validated with the actual yield. It was found that the predicted NDVI of both the fortnights of July is highly correlated with the yield of major crops in assured rainfall zone and scarcity zone.Index Terms-Agricultural drought, crop yield prediction model, MODIS NDVI, sunspot numbers.

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“…To monitor the signals of abiotic stress caused by drought a multitemporal NDVI data is derived index of vegetation condition -(VCI) [Kogan, 1987;Seiler, Kogan et al, 1998;Unganai, Kogan, 1998]. The formula for calculating the VCI is:…”

Section: Methodsmentioning

confidence: 99%

“…The VCI is based on the concept of the ecological potential on natural resources, climate, soil diversity, type and amount of vegetation, and topography of the region [Seiler, Kogan et al, 1998]. Index values range from 0 to extremely adverse conditions to 100 -optimum conditions.…”

Section: Methodsmentioning

confidence: 99%

Detection and Assessment of Abiotic Stress of Coniferous Landscapes Caused by Uranium Mining (Using Multitemporal High Resolution Landsat Data)

Filchev¹,

Roumenina²

2012

Geogr. environ. sustain.

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ABSTRACT. Remote sensing have become one of decisive technologies for detection and assessment of abiotic stress situations, such as snowstorms, forest fires, drought, frost, technogenic pollution etc. Present work is aiming at detection and assessment of abiotic stress of coniferous landscapes caused by uranium mining using high resolution satellite data from Landsat. To achieve the aim, ground-based geochemical data and were coupled with the satellite data for two periods, i.e. prior and after uranium mining decommissioning, into a file geodatabase in ArcGIS/ArcInfo 9.2, where spatial analyses were carried out. As a result, weak and very weak relationships were found between the factor of technogenic pollution -Z c and vegetation indices NDVI, NDWI, MSAVI, TVI, and VCI. The TVI performs better compared to other indices in terms of separability among classes, whereas the NDVI and VCI correlate well than other indices with Z c .

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AVHRR-based vegetation and temperature condition indices for drought detection in Argentina

Cited by 76 publications

References 11 publications

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Agricultural Drought Modeling Using Remote Sensing

Detection and Assessment of Abiotic Stress of Coniferous Landscapes Caused by Uranium Mining (Using Multitemporal High Resolution Landsat Data)

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