Located across the equator, the East Africa region is among regions of Africa which have previously known the severe vegetation degradation. Some known reasons are associated with the climate change events and unprofessional agricultural practices. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) version 3 NDVI (NDVI3g) and Climate Research Unit (CRU) datasets for precipitation and temperature were used to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015. Pearson correlation of NDVI and climate factors were also explored to investigate the short (October - December) rainy seasons. The phenological metrics of the region was also extracted to understand the seasonal cycle of vegetation. The results show that a positive linear trend of 14.50 × 10−4 for mean annual NDVI before 1998, where as a negative linear trend of −9.64 × 10−4 was found after 1998. The Break Point (BP) was obtained in 1998, which suggests to nonlinear responses of NDVI to climate and non-climate drivers. ENSO-vegetation in El-nino years showed a weak teleconnection between ENSO and vegetation growth changes of croplands. Also, the analyzed correlations on NDVI data resulted to the higher correlation between NDVI and precipitation than that with temperature. The Hurst exponent result showed that about, 18.63% pixels exhibited a behavior, typical of random walk (H = 0.5) suggesting that NDVI growth changes may eventually persist, overturn or fluctuate randomly in the future depending on the drivers. Vegetation trends with sustainable (unsustainable) trends were 36.8% (44.6%). Strikingly, about 20% of the total vegetated area showed unsustainable trend from degradation to amelioration. More so, results reveal that the vegetation of the croplands (non-croplands) over East Africa changed insignificantly by 6.9 × 10−5/yr (5.16 × 10−4/yr), suggesting that non-croplands are fast getting reduced Nonetheless, the NDVI growth responses to monthly and seasonal changes in climate were adjudged to be complex and dynamic. Seasonally, the short rainy season showed the higher variability in NDVI than the long rainy season. Also, the DJF, MAM and SON seasons are strongly driven by precipitation variation effect of ENSO versus NDVI series.

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Recent studies have shown that global Penman‐Monteith equation based (PM‐based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite‐retrieved vegetation information to simulate water stress in a PM‐based model (RS‐WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM‐based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS‐WBPM is successful. The daily ET resulting from RS‐WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( normalRnormalMnormalSnormalE = 18.72 W m−2) for all 27 sites and R2=0.62 ( normalRnormalMnormalSnormalE = 18.21 W m−2) for 25 nonagricultural sites. However, combined results from the optimum older PM‐based models at specific sites show R2 normalvnormalanormallnormalunormalenormals normalonormalf normalonormalnnormallnormaly 0.50 ( normalRnormalMnormalSnormalE = 20.74 W m−2) for all 27 sites. RS‐WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS‐WBPM are globally available, the results from RS‐WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

With rising population, decline in soil productivity and land-based conflicts, the per-capita land availability for cultivation is rapidly decreasing within Benue State, a largely agrarian and smallholder setting. This study attempts a local-level support for the actualisation of Sustainable Development Goal Number 2 ("end hunger, achieve food security and improved nutrition, and promote sustainable agriculture") by 2030. Using Multi-Criteria Decision Making (MCDM) method, remote sensing data from Climate Research Unit (CRU) and in-situ data from Nigeria Meteorological Agency (NIMET) were analyzed by GIS techniques to map the suitability of rice cultivation in the study area, with the integration of Normalized Difference Vegetation Index (NDVI), land cover, slope, temperature, precipitation and soil parameters (cation exchange capacity, pH, bulk density, organic carbon). We apply the various statistical parameters that include mean spatial NDVI; correlation coefficient, standard deviation and Root Mean Square (RMS) between CRU and NIMET data. Spatial regression trend analysis is conducted between CRU precipitation and NDVI and between CRU temperature and NDVI from 1985 to 2015. The results reveal that NDVI in highly suitable rice planting regions is higher than marginally suitable regions except in the months of October and November, which shows that the highly suitable regions will yield better than the marginally suitable regions during the dry season. Additionally, NDVI is seasonally bimodal in response to precipitation, meaning that vegetation vigor is more dependent on precipitation than temperature. Finally, the correlation coefficient, standard deviation and RMS between CRU and NIMET precipitation data shows 0.42, 108, and 110, respectively, while these three factors between CRU and NIMET temperature data shows 0.88, 1.60, and 0.86, respectively. In conclusion, the MCDM approach reveals that upland is more suitable for rice cultivation in Benue State when comparing with the area provided by the Global Land Cover and National Mappings Organization (GLCNMO) data.

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Satellite-derived Normalized Difference Vegetation index (NDVI) data records offer important sources for long term correlation modelling over West Africa. In this study, we assessed long range correlations in half monthly NDVI records over West Africa from 1982 to 2011 using GIMMS NDVI. In our analysis, we assessed (a) the annual and seasonal trends obtained using Ordinary Linear Regression, (b) the detrended lag-1-autocorrelation C(1), (c) the Detrended Fluctuation Analysis (DFA) scaling Hurst exponent h and (d) the Multifractal (MF) characteristics of NDVI. Results show that there exist some patterns or trends in the records that persist over time. The value of C(1) for NDVI was obtained as 0.989 is significant at 95% confidence interval. Consequently, the scaling h values of the Hurst DFA showed that about 37.4, 20.5, 41.7 and 0.5% of the vegetated areas are anti-correlated (h < 0.5), un-correlated (h = 0.5), correlated (0.5 < h < 1) and uncorrelated random walk (h = 1), respectively. The trend analysis from Ordinary Least square Regression (OLR) shows that about 54.3, 0.1 and 45.6% of the vegetated areas are positively, uncorrelated and negatively correlated, respectively. Our findings revealed that the DFA method performed better than OLR and the findings could be useful in identifying areas with improved and degraded vegetation, which cannot be properly captured by the OLR method. Accordingly, the comparison of the MF-DFA results of original data to those of shuffled and surrogate series indicated that the multifractal nature of considered time-series is both from PDF and long-range correlations but arguably, MF due to long range correlation dominates over West Africa. The research is therefore helpful in the formulating crop and environmental management policies that may be used to improve ecosystem management using a long term plan (inter-annual) or short term (inter-seasonal) planning. INDEX TERMS Long range correlation, multifractality, NOAA AVHRR, NDVI, ecological zones, West Africa.

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Abstract:The measurement of solar UV radiation at a typical market setting (Gboko, Central Market, Benue State Nigeria) was done using a broadband UV meter and Polymer Polysulphone Dosimeters. The dosimeters were fitted on strategic solar radiation access areas on a plastic human figure and placed in the sun from 9:30am-4:00pm. The dosimeter fitted on the fore head recorded the highest reading of 595 J/m 2 whereas the one positioned in the pocket (beneath the cloth) gave the least reading 2.7J/m 2 . The dosimeter placed in the Shade (Shop Canopy) also gave a low value of 45.4 J/m 2 and the mean UV radiation exposure was determined as 432 ± 47 J/m 2 . The work sets a reliable baseline data for solar UV radiation monitoring in central Nigeria. Appropriate recommendations have also been made to create awareness on the harmful effects of solar UV radiation.