Monitoring vegetation phenological stages using remote sensing data for pasture management in selected asals of Kenya

Abstract

The objective of this study was to monitor phenological stages of grassland in selected ASALs of Kenya, so as to provide information for sustainable pasture management. NDVI data obtained by the VEGETATION instrument onboard SPOT satellite was extracted over three selected ASAL districts of Kenya namely, Kajiado, Garissa and Baringo. Extraction points were based on a land cover map that showed the location of grassland in the three districts. Piecewise logistic functions were applied on the extracted NDVI data in order to identify phenological stages. Rainfall estimate data was then used to relate the identified phenological stages to rainfall using lagged correlation. Curves of the correlated lagged rainfall and NDVI from the determined phenological stages were plotted to compare their temporal patte-ns. Spatial patterns oflength of the growth period were also determined. Interannual phenological stages appeared to follow a clear growth - senescence temporal pattern. Two growth periods were identified in all the districts studied, consistent with known cycles of different grass and browse species in the districts. Peak growth was seen to occur during the short rains in Kajiado district and during the long rains in Baringo district. Growth in the two seasons was almost the same in Garissa district. Phenological stages were significantly correlated to different lags of rainfall, with response to a longer lag observed during the March to June growth period. Patterns of lagged rainfall were also found to be similar to those of NDVI at the different phenological stages. The length of both growth periods showed spatially coherent patterns that signified the distribution of different pasture species. Given these results, logistic functions were able to model grassland phenological stages in the ASALs. However, further investigations are needed using a longer time series of NDVI data and more spatial points, as well as validation using in situ data. The results can give useful information for sustainable pasture management in the ASALs of Kenya.