Abstract:Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities.

The availability of newly generated data from Advanced Very High Resolution Radiometer (AVHRR) covering the last three decades has broaden our understanding of vegetation dynamics (greening) from global to regional scale through quantitative analysis of seasonal trends in vegetation time series and climatic variability especially in the Guinea savannah region of Nigeria where greening trend is inconsistent. Due to the impact of changes in global climate and sustainability of means of human livelihood, increasing interest on vegetation productivity has become important. The aim of this study is to examine association between NDVI and rainfall using remotely sensed data, since vegetation dynamics (greening) has a high degree of association with weather parameters. This study therefore analyses trends in regional vegetation dynamics in Kogi state, Nigeria using bi-monthly AVHRR GIMMS 3g (Global Inventory Modelling and Mapping Studies) data and TAMSAT (Tropical Applications of Meteorology Satellite) monthly data both from 1983 to 2011 to identify changes in vegetation greenness over time. Analysis of changes in the seasonal variation of vegetation greenness and climatic drivers was conducted for selected locations to further understand the causes of observed interannual changes in vegetation dynamics. For this study, Mann-Kendall (MK) monotonic method was used to analyse long-term inter-annual trends of NDVI and climatic variable. The Theil-Sen median slope was used to calculate the rate of change in slopes between all pair wise combination and then assessing the median over time. Trends were also analysed using a linear model method, after seasonality had been removed from the original NDVI and rainfall data. The result of the linear model are statistically significant (p <0.01) in all the study location which can be interpreted as increase in vegetation trend over time (greening). Also the result of the NDVI trend analysis using Mann-Kendall test shows an increasing (i.e. positive) trend in the time series. The significance of the result was tested using Kendall's tau rank correlation coefficient and the results were significant. Finally the NDVI data and TAMSAT data were analysed together in order to describe the relationship between both values. Although, increase in rainfall over the last decades enhances vegetation greenness, other factors such as land use change and population density need to be investigated in order to better explain changing trends of vegetation greening for the study area in the future.

Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region's economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R 2 of 0.22-0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%-40% (0.01-0.013) and R 2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/06/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Osunmadewa, Wessollek, and Karrasch: Linear and segmented linear trend detection for vegetation. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/06/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Osunmadewa, Wessollek, and Karrasch: Linear and segmented linear trend detection for vegetation. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/06/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Osunmadewa, Wessollek, and Karrasch: Linear and segmented linear trend detection for vegetation. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/06/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Osunmadewa, Wessollek, and Karrasch: Linear and segmented linear trend detection for vegetation.

Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it's Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.

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