Response of land surface phenology to variation in tree cover during green-up and senescence periods in the semi-arid savannas in Southern Africa

Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time.The Council for Industrial and Scientific Research (CSIR) Parliamentary Grant, Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) and ECOPOTENTIAL project which received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 641762.http://www.mdpi.com/journal/remotesensingam2017Plant Production and Soil Scienc