Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series
AbstractThe spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability. View Full-Text
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Vrieling, A.; de Leeuw, J.; Said, M.Y. Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series. Remote Sens. 2013, 5, 982-1000.
Vrieling A, de Leeuw J, Said MY. Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series. Remote Sensing. 2013; 5(2):982-1000.Chicago/Turabian Style
Vrieling, Anton; de Leeuw, Jan; Said, Mohammed Y. 2013. "Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series." Remote Sens. 5, no. 2: 982-1000.