Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation

Kawabata, Ayaka; Ichii, Kazuhito; Yamaguchi, Yasushi

doi:10.1080/01431160010028490

Cited by 84 publications

(76 citation statements)

References 10 publications

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“…5c). The data show that the NDVI is, statistically, more closely linked with surface temperature than precipitation, in agreement with previous findings (Ichii et al 2002;Kawabata et al 2001;Los et al 2001;Schultz and Halpert 1993). Schultz and Halpert (1993) found that the onset of precipitation generally appears to act as the stimulus for vegetation in regions where the magnitude of the annual temperature cycle is small, and vice versa.…”

Section: The Climate Factors Related To Ndvi (Amj)supporting

confidence: 90%

“…Vegetation activity is strongly influenced by the local weather variables, so researchers have identified the local meteorological variables associated with the NDVI, such as air temperature, precipitation and evapotranspiration (Di et al 1994;Kawabata et al 2001;Nemani and Running 1989;Wang et al 2003). However, large-scale climate systems also play essential roles in how the NDVI changes in response to global change (Gong and Shi 2003).…”

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confidence: 99%

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Links between the late wintertime North Atlantic Oscillation and springtime vegetation growth over Eurasia

Fan

2015

Clim Dyn

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Section: The Climate Factors Related To Ndvi (Amj)supporting

confidence: 90%

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confidence: 99%

Links between the late wintertime North Atlantic Oscillation and springtime vegetation growth over Eurasia

Fan

2015

Clim Dyn

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“…While other more recently developed indices for vegetation characteristics (such as the enhanced vegetation index or the net primary productivity) have been shown to yield advantages over the NDVI in terms of stress detection in plants, the NDVI is still in frequent use. Despite its lower sensitivity to changes of the vegetation cover, the NDVI is a conservative, robust measurement tool (Pettorelli, 2013) and has been comprehensively studied regarding its relationship with temperature variability (see, e.g., Halpert, 1993, 1995;Kawabata et al, 2001;Karnieli et al, 2010;Kim et al, 2010). The global data availability for a relatively long period of observations has made NDVI easily applicable to remote sensing studies without the need for exhaustive preprocessing.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, research on climatic drivers of NDVI variations has often concentrated on a global scale (e.g., the aforementioned Halpert, 1993, 1995;Kawabata et al, 2001;Karnieli et al, 2010;Kim et al, 2010), whereas regional studies like Wang et al (2003) and Hao et al (2012) are still rare. In the face of global warming, however, especially inter-regional climate disparities are projected to increase (Ciscar et al, 2011;Iglesias et al, 2012), thus emphasizing the need for regional research.…”

Section: Introductionmentioning

confidence: 99%

Impacts of temperature extremes on European vegetation during the growing season

et al. 2017

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Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the midlatitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

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“…In the whole study area, the nine atmospheric circulation factors influenced coupling features of spring NPP and precipitation more greatly in the areas southern to latitude 30N°. Gong et al (2002) and Kawabata et al (2001) have also proved that spring vegetation activity in the northern hemisphere is sensitive to climate changes.…”

Section: Regress Analysis Of Npp With Atmospheric Circulation Factorsmentioning

confidence: 90%

The role of atmospheric circulation system playing in coupling relationship between spring NPP and precipitation in East Asia area

Zhu

Pan

2007

Environ Monit Assess

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In many East Asia regions, spring (from March to May) precipitation is an important restricting factor to vegetation growth, and atmospheric circulation system may influence spring precipitation patter. It is helpful to under the response of ecosystem to climate change by studying the influence of atmospheric circulation system on the coupling relationship between spring net primary productivity and precipitation. Driving CASA (Carnegie-Ames-Stanford Approach) NPP (Net Primary Productivity) model, we estimated spring NPP for East Asia area (70 degrees E-1 70 degrees E, 10 degrees N-70 degrees N) from 1982 to 1999, and by the method of singular value decomposition we further analyzed the coupling features of spring NPP with precipitation. The result showed that the response features of NPP to precipitation were mainly embodied within the leading six NPP-precipitation paired-modes. The interpretation rates of the leading six paired-modes to the covariance of NPP-precipitation were 42.91, 23.29, 9.96, 5.60, 5.04 and 3.95%, respectively, and total to 90.75%. The temporal correlation coefficients of the leading six paired-modes were 0.830, 0.889, 0.841, 0.747, 0.912 and 0.923, respectively, and all the correlations were significant at significant level of 0.001. In some high latitude regions, there was no obviously corresponding relationship between NPP and precipitation in the leading two paired-modes, and the reason of it may be that spring temperature was the main restricting factor to NPP. In middle and low latitude regions, the effect of precipitation on NPP was relatively more notable. Nine atmospheric circulation factors in spring affected the patterns of NPP and precipitation greatly, and the regions with interpretation rate over 50% shared 60.41 and 65.58% of the whole study area, respectively.

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Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation

Cited by 84 publications

References 10 publications

Links between the late wintertime North Atlantic Oscillation and springtime vegetation growth over Eurasia

Links between the late wintertime North Atlantic Oscillation and springtime vegetation growth over Eurasia

Impacts of temperature extremes on European vegetation during the growing season

The role of atmospheric circulation system playing in coupling relationship between spring NPP and precipitation in East Asia area

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