Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990

Ichii, Kazuhito; Kawabata, Ayaka; Yamaguchi, Yasushi

doi:10.1080/01431160110119416

Cited by 315 publications

(222 citation statements)

References 10 publications

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“…At macro-scale, our results are consistent with previous studies of global linkages between VIs and climatic factors [20,22]. Specifically, our results show that temperature is more influential at high latitudes [20] and that precipitation is limiting over broad regions of steppe, desert and grassland [10,12,21].…”

Section: Discussionsupporting

confidence: 82%

“…In tropical ecosystems, some studies have shown low or negative correlation to precipitation [16,17], possibly as a result of light limitation, though other studies have questioned this supposition [18], and recent drought conditions in the Amazon have been associated with decreased greenness [19]. In contrast, temperature has a positive, strong association with vegetation in Northern, mid-to high latitudes [20][21][22][23]. In temperate grasslands that are not water limited, long-term vegetation production was linked to average growing season temperature [10].…”

Section: Introductionmentioning

confidence: 95%

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Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Clinton

et al. 2014

Remote Sensing

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Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008-2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 95%

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Clinton

et al. 2014

Remote Sensing

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“…These studies and several others, conclude that there is a strong relationship between climate variability and fluctuations in satellite-derived vegetation indices at local, regional and continental scales Tateishi and Kajiwara, 1992;Myneni et al, 1997;Paruelo and Lauenroth, 1998;Schwartz and Reed, 1999;Ichii et al, 2002;Nemani et al, 2003;Jolly and Running, 2004;Tateishi and Ebata, 2004;Zhou et al, 2003;Karlsen et al, 2006;Suzuki et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…NDVI makes use of bio-physical interactions whereby healthy green plant canopies absorb much of the radiation in the visible wavelengths and are highly reflective in the infrared (Jensen, 2000). The NDVI computed as (near-infrared − red/near-infrared + red channels) is the most commonly used index for large-area phenology studies (Goward data relationship for understanding phenological changes has been attempted by several researchers (Farrar et al, 1994;Schultz and Halpert, 1995;Ichii et al, 2002). It is well established that there is a close relationship between vegetation distribution patterns and climatic parameters on a global scale (Borchert, 1998), and the major vegetation types of the world have been well correlated with minimum temperature resistance, growing season duration and temperature, and the hydrological budget (Woodward and McKee, 1991).…”

Section: Introductionmentioning

confidence: 99%

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

Prasad

Badarinath

Eaturu

2008

Intl Journal of Climatology

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Bi-weekly National Oceanic and Atmospheric Administration-advanced very high-resolution radiometer (NOAA-AVHRR) satellite data covering a fourteen-year time period (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with environmental variables covering tropical evergreen forests of the Western Ghats, India. NDVI values and corresponding environmental variables were extracted from 23 different forested sites using the NOAA-AVHRR global inventory monitoring and modelling studies (GIMMS) dataset. We specifically used the partial least square (PLS) multivariate regression technique that combines features from principal component analysis and multiple regression to link spatial patterns in NDVI with the environmental variables. PLS regression analysis suggested the two-component model to be the best model, explaining nearly 71% of the variance in the NDVI datasets with relatively good R 2 value of 0.78 and a predicted R 2 value of 0.74. The most important positive predictors for NDVI included Riva's continentality index, precipitation indicators summed over different quarters, average precipitation and elevation. Also, the results from PLS regression clearly suggested that bio-climatic indicators that relied only on precipitation parameters had much more positive influence than indicators that combined both temperature and precipitation together. These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio-spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India.

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“…However, most of these studies focused solely on either precipitation or temperature for vegetation models, thereby resulting in low precision and low correlation coefficients between vegetation indices and climatic variables [14]. For instance, Ichii et al (2002) found a weaker relationship between vegetation growth and precipitation [15], while Nicholson et al…”

Section: Introductionmentioning

confidence: 99%

Modeling NDVI Using Joint Entropy Method considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin

Zhang¹,

Su²,

Singh³

et al. 2017

Preprint

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Terrestrial vegetation dynamics are closely influenced by a multitude of factors.This study investigated the relationships between vegetation patterns and their main influencing factors. The joint entropy method was employed to evaluate the dependence between normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased with a rate of 0.005/a in recent 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities, such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. Outcomes of this study are suitable for ecosystem monitoring, especially under the realm of climate change.Further studies are necessary and should consider more factors, such as runoff and irrigation.

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Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990

Cited by 315 publications

References 10 publications

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

Modeling NDVI Using Joint Entropy Method considering Hydro-Meteorological Driving Factors in the Middle Reaches of Hei River Basin

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