Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011

Eastman, J. Ronald; Sangermano, Florencia; Oliveira, Elia Machado de; Rogan, John; Anyamba, Assaf

doi:10.3390/rs5104799

Cited by 220 publications

(156 citation statements)

References 79 publications

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“…Long-term remote sensing observations provide effective methods to investigate interannual vegetation dynamics. The Normalized Difference Vegetation Index (NDVI), which is correlated to Leaf Area Index (LAI) [21] and Gross Primary Production (GPP) [22], is a good indicator for vegetation coverage and biomass, and has been widely used to analyze vegetation dynamics [23][24][25][26][27]. Many researches have investigated NDVI trends in different regions, showing that despite great spatial heterogeneity, NDVI generally increased in national and regional scale in China [19,28], North America [2], east Asia [29] and Euraisa [30].…”

Section: Introductionmentioning

confidence: 99%

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Liu

Lei

2015

Remote Sensing

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This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results show that on average, the growing season NDVI significantly increased by 0.0007 year −1 , with 76.5% of the research area showed increasing NDVI from 1982 to 2011. Seasonally, NDVI increased at high rates during the spring and autumn while changed slightly during the summer. At a national scale, the growing season NDVI was significantly and positively correlated to temperature and precipitation, with temperature being the dominant factor. At regional scales, the growing season NDVI was dominated by increasing temperature in most forest-covered areas in southern China and dominated by precipitation in most grassland in northern China. Within the past three decades, the increasing trend of national mean NDVI abruptly changed in 1994, slowing down from 0.0008 year −1 to 0.0003 year −1 . To be regional specific, the growing season NDVI in forest covered southern China has accelerated together with temperature since mid 1990s, while parts of the grassland in northern China have undergone stalled NDVI trends corresponding to slowed temperature increment and dropped precipitation since around 2000. Typical region analysis suggested that apart from

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Section: Introductionmentioning

confidence: 99%

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Liu

Lei

2015

Remote Sensing

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“…three amplitudes and two phases), the zero frequency (mean annual NDVI) and the frequencies with time periods of 1 year (annual) were selected for the analysis. Amplitude 2 and phase 2 represent the semi-annual curve, but they are difficult to interpret (Eastman et al, 2013). This suite of harmonics (amplitude 0, amplitude 1 and phase 1) is sometimes called as the greenness parameters.…”

Section: Harmonic Analysis Of Ndvi Time Seriesmentioning

confidence: 99%

“…Detecting and characterizing trends in vegetation condition over time using remotely sensed data has received considerable attention in recent years (Bai et al, 2008;de Jong et al, 2011;Eastman et al, 2013;Verbesselt et al, 2010a). Recent interest in vegetation trend analysis arises for three reasons.…”

Section: Introductionmentioning

confidence: 99%

“…Long-term, remotely sensed normalized difference vegetation index (NDVI) data are suitable to detect and characterize trends in vegetation condition over time (de Jong et al, 2011;Eastman et al, 2013;Verbesselt et al, 2010a). Although NDVI can be computed from different multispectral satellite data, NDVI from the Advanced Very High Resolution Radiometer (AVHRR) is the only global vegetation data set which spans a time period of three decades.…”

Section: Introductionmentioning

confidence: 99%

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Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis

2015

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Abstract.A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay Basin. The Advanced Very High Resolution Radiometer (AVHRR)-based Global Inventory, Monitoring, and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) was used for long-term vegetation trend analysis at low spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) were used for medium-scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI and MODIS NDVI (2001-2011 data sets. Based on a robust trend estimator (Theil-Sen slope), most parts of the UBN ( ∼ 77 %) showed a positive trend in monthly GIMMS NDVI, with a mean rate of 0.0015 NDVI units (3.77 % yr −1 ), out of which 41.15 % of the basin depicted significant increases (p < 0.05), with a mean rate of 0.0023 NDVI units (5.59 % yr −1 ) during the period. However, the MODIS-based vegetation trend analysis revealed that about 36 % of the UBN showed a significant decreasing trend (p < 0.05) over the period 2001-2011 at an average rate of 0.0768 NDVI yr −1 . This indicates that the greening trend of the vegetation condition was followed by decreasing trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful to identify changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. Over half (60 %) of the Abay Basin was found to exhibit significant trends in seasonality over the 25-year period (1982-2006). About 17 and 16 % of the significant trends consisted of areas experiencing a uniform increase in NDVI throughout the year and extended growing season, respectively. These areas were found primarily in shrubland and woodland regions. The study demonstrated that integrated analysis of inter-annual and intra-annual trends based on GIMMS and MODIS enables a more robust identification of changes in vegetation condition.

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“…NDVI3g is appropriate for long-term studies of land surface trends in vegetation, seasonality and coupling between climate variability and vegetation over the last three decades [31,32].…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

et al. 2016

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Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann-Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine's land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36´0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities.

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Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011

Cited by 220 publications

References 79 publications

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

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