Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011)
Garonna, I. ; Jong, R. de; Wit, A.J.W. de; Mücher, C.A. ; Schmid, B. ; Schaepman, M.E. - \ 2014
Global Change Biology 20 (2014)11. - ISSN 1354-1013 - p. 3457 - 3470.
land-surface phenology - high-resolution radiometer - vegetation index ndvi - spring phenology - climate-change - time-series - monitoring vegetation - trends - avhrr - models
Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere–atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan-European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape-based aggregation scheme. We used indicators of Start-Of-Season, End-Of-Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982–2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18–24 days decade-1 over 18–30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing-season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI-derived end-of-season contributed more to the GSL trend than changes in spring green-up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change.
|Structural change in soil moisture and vegetation activity trends in the African Sahel
Verbesselt, J. ; Jong, R. de; Dorigo, W. ; Zeileis, A. ; Ardö, J. ; Herold, M. - \ 2014
In: International Conference “Global Vegetation Monitoring and Modeling” (GV2M). - - p. 175 - 175.
Spatial relationship between climatologies and changes in global vegetation activity
Jong, R. de; Schaepman, M.E. ; Furrer, R. ; Bruin, S. de; Verburg, P.H. - \ 2013
Global Change Biology 19 (2013)6. - ISSN 1354-1013 - p. 1953 - 1964.
net primary production - drought-induced reduction - land-cover data - time-series - avhrr data - ndvi data - spot-vegetation - african sahel - term trends - index data
Vegetation forms a main component of the terrestrial biosphere and plays a crucial role in land-cover and climate-related studies. Activity of vegetation systems is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature. However, little remains known about the processes underlying these changes at large spatial scales. In this study, we aimed at quantifying the spatial relationship between changes in potential climatic growth constraints (i.e. temperature, precipitation and incident solar radiation) and changes in vegetation activity (1982–2008). We demonstrate an additive spatial model with 0.5° resolution, consisting of a regression component representing climate-associated effects and a spatially correlated field representing the combined influence of other factors, including land-use change. Little over 50% of the spatial variance could be attributed to changes in climatologies; conspicuously, many greening trends and browning hotspots in Argentina and Australia. The nonassociated model component may contain large-scale human interventions, feedback mechanisms or natural effects, which were not captured by the climatologies. Browning hotspots in this component were especially found in subequatorial Africa. On the scale of land-cover types, strongest relationships between climatologies and vegetation activity were found in forests, including indications for browning under warming conditions (analogous to the divergence issue discussed in dendroclimatology).
Shifts in global vegetation activity trends
Jong, R. de; Verbesselt, J. ; Zeileis, A. ; Schaepman, M.E. - \ 2013
Remote Sensing 5 (2013)3. - ISSN 2072-4292 - p. 1117 - 1133.
net primary production - drought-induced reduction - image time-series - land-surface phenology - structural-change - satellite data - terrestrial ecosystems - ols residuals - ndvi data - avhrr
Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981–2011). The existence of monotonic changes and trend shifts present in such records has previously been demonstrated. However, information on timing and type of such trend shifts was lacking at global scale. In this work, we detected major shifts in vegetation activity trends and their associated type (either interruptions or reversals) and timing. It appeared that the biospheric trend shifts have, over time, increased in frequency, confirming recent findings of increased turnover rates in vegetated areas. Signs of greening-to-browning reversals around the millennium transition were found in many regions (Patagonia, the Sahel, northern Kazakhstan, among others), as well as negative interruptions—“setbacks”—in greening trends (southern Africa, India, Asia Minor, among others). A minority (26%) of all significant trends appeared monotonic
Trend changes in global greening and browning: Contribution of short-term trends to longer-term change
Jong, R. de; Verbesselt, J. ; Schaepman, M.E. ; Bruin, S. de - \ 2012
Global Change Biology 18 (2012)2. - ISSN 1354-1013 - p. 642 - 655.
net primary production - drought-induced reduction - structural-change models - image time-series - land-cover data - terrestrial ecosystems - photosynthetic trends - environmental-change - phenological change - vegetation indexes
Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi-arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. The net global figure – considered over the full length of the time series – showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short-term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long-term NDVI time series.
Analysis of vegetation-activity trends in a global land degradation framework
Jong, R. de - \ 2012
Wageningen University. Promotor(en): Michael Schaepman, co-promotor(en): Sytze de Bruin. - S.l. : s.n. - ISBN 9789461733122 - 147
vegetatie - vegetatie-indexen - landdegradatie - cartografie - monitoring - observatie - exploratie - klimaat - seizoenvariatie - satellietbeelden - remote sensing - vegetation - vegetation indices - land degradation - mapping - monitoring - observation - exploration - climate - seasonal variation - satellite imagery - remote sensing
Land degradation is a global issue on a par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land resources in a consistent, physical way and on global scale by making use of vegetation activity and/or cover as proxies. A well-known spectral proxy is the normalized difference vegetation index (NDVI), which is available in high temporal resolution time series since the early 1980s. In this work, harmonic analyses and non-parametric trend tests were applied to the GIMMS NDVI dataset (1981–2008) in order to quantify positive changes (or greening) and negative changes (browning). Phenological shifts and variations in length of growing season were accounted for using analysis by vegetation development stage rather than by calendar day. This approach does not rely on temporal aggregation for elimination of seasonal variation. The latter might introduce artificial trends as demonstrated in the chapter on the modifiable temporal unit problem. Still, a major assumption underlying the analysis is that trends were invariant, i.e. linear or monotonic, over time. However, these monotonic trends in vegetation activity may consist of an alternating sequence of greening and/or browning periods. This effect and the contribution of short-term trends to longer-term change was analysed using a procedure for detection of trend breaks. Both abrupt and gradual changes were found in large parts of the world, especially in (semi-arid) shrubland and grassland. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. This marks the importance of accounting for trend changes in the analysis of long-term NDVI time series. These new change-detection techniques advance our understanding of vegetation variability at a multi-decadal scale, but do not provide links to driving processes. It is very complex to disentangle all natural and human drivers and their interactions. As a first step, the spatial relation between changes in climate parameters and changes in vegetation activity was addressed in this work. It appeared that a substantial proportion (54%) of the spatial variation in NDVI changes could be associated to climatic changes in temperature, precipitation and incident radiation, especially in forest biomes. In other regions, the lack of such associations might be interpreted as human-induced land degradation. With these steps we demonstrated the value of global satellite records for monitoring land resources, although many steps are still to be taken.
Linear trends in seasonal vegetation time series and the modifiable temporal unit problem
Jong, R. de; Bruin, S. de - \ 2012
Biogeosciences 9 (2012). - ISSN 1726-4170 - p. 71 - 77.
proxy global assessment - land degradation - sahel - environment - america - modis - ndvi
Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.
|Short-term variability within long-term trends in global vegetation activity
Jong, R. de; Verbesselt, J. ; Schaepman, M.E. ; Bruin, S. de - \ 2011
Quantitative mapping of global land degradation using Earth observations
Jong, R. de; Bruin, S. de; Schaepman, M.E. ; Dent, D. - \ 2011
International Journal of Remote Sensing 32 (2011)21. - ISSN 0143-1161 - p. 6823 - 6853.
net primary production - time-series analysis - terrestrial primary production - difference vegetation index - noaa-avhrr data - spot-vegetation - ndvi data - interannual variability - growing-season - south-africa
Land degradation is a global issue on par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land degradation in a consistent, physical way and on a global scale by making use of vegetation productivity and/or loss as proxies. Most recent studies indicate a general greening trend, but improved data sets and analysis also show a combination of greening and browning trends. Statistically based linear trends average out these effects. Improved understanding may be expected from data-driven and process-modelling approaches: new models, model integration, enhanced statistical analysis and modern sensor imagery at medium spatial resolution should substantially improve the assessment of global land degradation
|An Update of GLADA - Global Assessment of Land Degradation and Improvement - GLADA Report Update
Bai, Z.G. ; Jong, R. de; Lynden, G.W.J. van - \ 2011
Wageningen : ISRIC - World Soil Information (Report 2010/08 ) - 58 p.
Analysis of monotonic greening and browning trends from global NDVI time-series
Jong, R. de; Bruin, S. de; Wit, A.J.W. de; Schaepman, M.E. ; Dent, D.L. - \ 2011
Remote Sensing of Environment 115 (2011)2. - ISSN 0034-4257 - p. 692 - 702.
avhrr vegetation index - land degradation - spot-vegetation - growing-season - photosynthetic trends - primary productivity - deciduous forest - plant phenology - carbon-dioxide - high-latitudes
Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981–2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.
|Matrix voor competentiegericht beroepsonderwijs
Wesselink, R. ; Biemans, H.J.A. ; Rooden, M. van; Jong, R. de - \ 2007
Profiel : van beroepsonderwijs, educatie en scholing 16 (2007)1. - ISSN 1387-6112 - p. 32 - 35.
The author responds
Meerburg, B.G. ; Jong, R. de - \ 2005
Outlook on Agriculture 34 (2005)2. - ISSN 0030-7270 - p. 122 - 122.