Shifts in Global Vegetation Activity Trends

Jong, Rogier de; Verbesselt, Jan; Zeileis, Achim; Schaepman, Michael E.

doi:10.3390/rs5031117

Cited by 219 publications

(195 citation statements)

References 51 publications

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“…The estimates reflect the percentage of the trend type within the dominant class, using the land cover information from the 1990 reference year. Generally, the interpretation of these trends is challenging as the trend shifts can be explained by several factors [47]. The reversal trend can be attributed to long-term increasing human pressures [48], particularly the cultivation of abandoned croplands, which was also observed by Smaliychuk et al [7].…”

Section: Trends Of Ndvi Seriesmentioning

confidence: 99%

“…These breakpoints were assumed to catch the most influential trend shifts in the time series, which represent drastic changes in ecosystem functioning [47,48]. We classified the trends based on the modified methodology described by de Jong et al [47]. The authors used the term greening/browning for categorizing the changes based on the signal before and after the breakpoint.…”

Section: Trend Analysismentioning

confidence: 99%

“…As we were focusing on trajectories of the changes, rather than the number of the breakpoints, we chose the model with either 0 or 1 breakpoints. These breakpoints were assumed to catch the most influential trend shifts in the time series, which represent drastic changes in ecosystem functioning [47,48]. We classified the trends based on the modified methodology described by de Jong et al [47].…”

Section: Trend Analysismentioning

confidence: 99%

“…The season-trend model [47] (i.e., linear trend and harmonic component) was fitted to pre-processed monthly NDVI time series, and the stability of the model was assessed using a test that determines the significance of structural trend breaks. As we were focusing on trajectories of the changes, rather than the number of the breakpoints, we chose the model with either 0 or 1 breakpoints.…”

Section: Trend Analysismentioning

confidence: 99%

“…To study sudden and gradual changes in land surface, the Break For Additive Season and Trend (BFAST) algorithm was used, [43] which decomposes the series into trend, seasonal and reminder components to evaluate gradual and seasonal dynamics occurring within indices derived from satellite image time series such as NDVI [46][47][48]. The season-trend model [47] (i.e., linear trend and harmonic component) was fitted to pre-processed monthly NDVI time series, and the stability of the model was assessed using a test that determines the significance of structural trend breaks.…”

Section: Trend Analysismentioning

confidence: 99%

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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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Section: Trends Of Ndvi Seriesmentioning

confidence: 99%

Section: Trend Analysismentioning

confidence: 99%

Section: Trend Analysismentioning

confidence: 99%

Section: Trend Analysismentioning

confidence: 99%

Section: Trend Analysismentioning

confidence: 99%

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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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GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia

et al. 2014

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Water-limited ecosystems, covering~50% of the global land, are controlled primarily by hydrologic factors. Because climate change is predicted to markedly alter current hydroclimatic conditions later this century, a better hydrological indicator of ecosystem performance is warranted to improve understanding of hydrological controls on vegetation and to predict changes in the future. Here we show that the observed total water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) can serve as this indicator. Using the Australian mainland as a case study, where ecosystems are generally water limited, we found that GRACE-observed TWSA can explain changes in surface greenness (as measured by the normalized difference vegetation index, NDVI) both interannually and seasonally. In addition, we found that TWSA shows a significant decreasing trend during the millennium drought from 1997 through 2009 in the region. However, decline in annual mean NDVI during the same period was mainly driven by decline in annual minimum monthly NDVI, whereas annual maximum monthly NDVI remained relatively constant across biomes. This phenomenon reveals an intrinsic sensitivity of ecosystems to water availability that drought-induced reductions in surface greenness are more likely expressed through its influence on vegetation during lower NDVI months, whereas ecosystem activities tend to recover to their maximum level during periods when the combined environmental conditions favor vegetation growth within a year despite the context of the prolonged drought.

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Fluxes all of the time? A primer on the temporal representativeness of FLUXNET

et al. 2017

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FLUXNET, the global network of eddy covariance flux towers, provides the largest synthesized data set of CO2, H2O, and energy fluxes. To achieve the ultimate goal of providing flux information “everywhere and all of the time,” studies have attempted to address the representativeness issue, i.e., whether measurements taken in a set of given locations and measurement periods can be extrapolated to a space‐ and time‐explicit extent (e.g., terrestrial globe, 1982–2013 climatological baseline). This study focuses on the temporal representativeness of FLUXNET and tests whether site‐specific measurement periods are sufficient to capture the natural variability of climatological and biological conditions. FLUXNET is unevenly representative across sites in terms of the measurement lengths and potentials of extrapolation in time. Similarity of driver conditions among years generally enables the extrapolation of flux information beyond measurement periods. Yet such extrapolation potentials are further constrained by site‐specific variability of driver conditions. Several driver variables such as air temperature, diurnal temperature range, potential evapotranspiration, and normalized difference vegetation index had detectable trends and/or breakpoints within the baseline period, and flux measurements generally covered similar and biased conditions in those drivers. About 38% and 60% of FLUXNET sites adequately sampled the mean conditions and interannual variability of all driver conditions, respectively. For long‐record sites (≥15 years) the percentages increased to 59% and 69%, respectively. However, the justification of temporal representativeness should not rely solely on the lengths of measurements. Whenever possible, site‐specific consideration (e.g., trend, breakpoint, and interannual variability in drivers) should be taken into account.

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Shifts in Global Vegetation Activity Trends

Cited by 219 publications

References 51 publications

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

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

GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia

Fluxes all of the time? A primer on the temporal representativeness of FLUXNET

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