Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series

Vrieling, Anton; Leeuw, Jan de; Said, Mohammed Yahya

doi:10.3390/rs5020982

Cited by 161 publications

(146 citation statements)

References 57 publications

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“…For instance, the mean value of the coefficient of variation of GSL over the whole study area during the period of 1998-2012 is 21% and ranges from 15% in the southern agricultural band to 28% in the northern grassland band. Phenological dates and spatial patterns are similar to those found in other studies using different satellite instruments (NOAA-AVHRR, MODIS) and different phenology retrieval methods (e.g., [32,61]). …”

Section: Spatial Patterns Of Phenologysupporting

confidence: 72%

“…Present climate change has already altered both the amount and the seasonal distribution of rainfall worldwide and especially in the dry tropics [30], with consequences on vegetation phenology (e.g., [19,31,32]). Gaining insights into the possible effect of these changes on crop and pasture production in semi-arid regions is important to develop appropriate adaptation strategies, particularly in such semi-arid areas, where the impact on terrestrial ecosystems is expected to be more dramatic [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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Abstract:In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESSRemote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

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Section: Spatial Patterns Of Phenologysupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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“…Several studies based on time series of satellite data have confirmed a climate-driven greening up effect in the Sahel belt after the 1980s (Fensholt andRasmussen, 2011, Heumann et al, 2007). Climatic greening up in West Africa and other parts of sub-Saharan Africa may have an effect on the increasing trend in the length of the growing season and may consequently increase agricultural productivity (Vrieling et al, 2013). Climatic greening was predominantly observed across areas covered by a mosaic of cropland, grassland, cultivated and managed land, where vegetation responds to both natural environmental variability and to anthropogenic changes.…”

Section: A Conceptual Model -Link With Previous Studiesmentioning

confidence: 99%

“…This region has experienced several severe droughts over the last decades affecting the vegetation condition (Ngaka, 2012). The locations with statistically significant NDVI reduction are likely to have experienced less favourable growing conditions or disturbances like herbivory, grazing, land use change or human disturbance (Pricope et al, 2013;Vrieling et al, 2013).…”

Section: Vegetation Patternsmentioning

confidence: 99%

A conceptual model for assessing rainfall and vegetation trends in sub‐Saharan Africa from satellite data

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Balzter

Bartholomé³

et al. 2014

Int. J. Climatol

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Policymakers, governments and aid agencies require operational environmental monitoring in support of evidence-based policy-making and resource deployment in crisis situations. For Africa, this is only feasible at sub-continental scale with a large network of automated meteorological stations, a large number of highly coordinated field observers or with satellite remote sensing. The challenge with satellite data lies in the derivation of meaningful environmental indicators. This article describes a conceptual framework for understanding satellite-derived indicators of rainfall and vegetation greenness trends over Africa. It attributes observed vegetation changes to climatic (i.e. rainfall linked) and non-climatic drivers. A decade of annual rainfall and vegetation data over sub-Saharan Africa was analysed using satellite-based rainfall estimates [Famine Early Warning System Rainfall Estimation 2.0 (FEWSNET RFE 2.0)] from National Oceanic and Atmospheric Administration's (NOAA's) Climate Prediction Centre and the Normalized Difference Vegetation Index (NDVI) obtained from the Satellite Pour l'Observation de la Terre Vegetation (SPOT-VGT) sensor. Rainfall and vegetation greenness trends were analysed for 759 administrative regions of sub-Saharan Africa to identify those regions that have experienced a negative, positive or stable rainfall/vegetation trend over the period [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. The character of the relationship between the annual rainfall and max NDVI trends were examined to identify areas where the changes in greenness could be attributed to climatic (rainfall) and non-climatic (human land use or ecological disturbance) changes. Regions where increasing rainfall was associated with vegetation greening were found in West Africa, Central African Republic, West Cameroon and northeastern part of South Africa, whereas areas with evidence of 'climatic vegetation degradation' were located in Southern Madagascar, Nigeria, Kenya and the Garden Route region of South Africa.

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“…It is urgent to check consistency and establish a connection between studies using the two versions of NDVI data sets to monitor vegetation activity change (e.g., [9,13,14,[24][25][26][27][28][29][30][31][32]). Our study only demonstrated the potential differences in monitoring vegetation activity change with different NDVI versions.…”

Section: Contributions and Challenges To The Monitoring Of Vegetationmentioning

confidence: 99%

A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008

et al. 2013

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Abstract:The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982-2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a OPEN ACCESSRemote Sens. 2013, 5 4032 higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.

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Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series

Cited by 161 publications

References 57 publications

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

A conceptual model for assessing rainfall and vegetation trends in sub‐Saharan Africa from satellite data

A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008

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