Vegetation resources in Nigeria are of vital importance for the sustainable development of the country. However, this essential resource is in danger due to the effect of anthropogenic and climate induced impacts. Currently desert encroachment which cuts across the Sahel is affecting most of the states in the northern part of the country particularly the eleven states considered by the Federal Ministry of Environment in Nigeria as the frontline states. Several studies on the Nigerian environment have shown that there are serious threats to the general environment particularly vegetation. Due to population growth and the need for housing as well as the expansion of the over-utilised farmlands across these states, places considered as reserved areas across the country are being exploited to the detriment of the vegetal resources particularly the forest and rangeland areas. This study utilized Idrisi TerrSet (version 18) raster-based remote sensing and GIS software to analyse seventy two (72) dekadal Normalised Vegetation Index (NDVI) imageries from SPOT satellite covering Nigeria in order to assess the anthropogenic and likely climatic impacts on the vegetal resources using the forward t-mode Principal Component Analysis (PCA) with standardised principal components. Results indicated that Component 1 which explains about 69% of the 72 time-series NDVI imageries shows typical vegetation cover over the study area within the time period under study. While component two indicated a cyclic trend differentiating the ENSO events of 1999 and 2009; component three indicated positive anomaly pattern of vegetation NDVI mostly within Sokoto, Kebbi, Kano, Jigawa and the northern parts of Bauchi, Yobe and Borno states. However, Component four imagery indicated a likely link to the 2009 flood that affected Kainji dam and rivers Niger and Benue.

Aim and Objectives:The aim of this study is to investigate the influence of El -Niño Southern Oscillation (ENSO) on rainfall variation in Kaduna metropolis from the year 1973-2013. Study Design: Precipitation data was sourced from NIMET (Kaduna airport) while Sea Surface Temperature Anomaly and Southern Oscillation Index data was acquired from the National Oceanographic Atmospheric Administration (NOAA) climate prediction centre's website. Methodology: These were analyzed to determine the extent of variation between ENSO and Sea Surface Temperature Anomaly (SSTA) and pattern of change in precipitation during El -Niño and non El -Niño years. Furthermore, the significant difference between rainfall amount of El -Niño and non El -Niño years was also determined. Results: Results indicated that rainfall within the study area was highly varied during the period Original Research Article

The aim of this paper is to test the applicability of Co-Kriging (CK) on the study of the changing climate in Northern Nigeria. Indices were derived from climatic variables (Rainfall and Temperature) obtained from Nigerian Meteorological Agency (NIMET) and remotely sensed data covering the period from 1981 to 2010 in the form of Normalised Difference Vegetation Index (NDVI) data derived from National Oceanic Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). Because of the strong relationship between NDVI and Rainfall, CK method of data interpolation was tested with R-Statistical software. A digital elevation model (DEM) of the study area at 90-meter spatial resolution was used as a supplement in an overlay procedure using the IDRISI Remote sensing and GIS software so as to derive the correct altitude values of the Met stations for comparison with the coefficient of variation of the rainfall dataset. Results from the derived CK prediction maps showed that there are high variability in NDVI and rainfall across the time-series. Furthermore, spatial average variability in the growing season rainfall was 60% with a mean temperature of 4% although coefficient of variation in rainfall for the individual climatic station's ranged from 18.15 to 60.98 per cent. While the highest coefficient of variation in temperature for the entire time series (1981-2010) was located around Katsina area, the lowest was located around Minna. From the results of this analysis it is evident that the higher prediction variance values particularly for vegetation NDVI and rainfall are located in the southern part of the study area particularly around Kaduna, Minna, and Jos as compared to the northern part of the study area falling around Maiduguri, Sokoto and Katsina which indicated relatively lower prediction values. However, further studies should also be undertaken using the raster NDVI dataset in a GIS environment to buttress our view that there were changes in the general ecosystems within the study area as result of climatic impact.

Climate change is one of the greatest threats facing the global community and has been mainly induced by increasing atmospheric concentrations of greenhouse gases resulting from fossil fuel energy use and change in vegetation cover. This study used modelling techniques to determine how changes in climate could affect vegetation productivity in the northern part of Nigeria. Climatic parameters (Rainfall, Minimum and Maximum Temperatures) as well as coarse Normalised Difference Vegetation Index (NDVI) data for the growing seasons of 1981-2009 were utilised. Because of the relationship between climatic parameters and vegetation, Spatial method of data interpolation was tested. Results from the prediction elevation values ranged from −3e−9 to 2e−9. It was observed from prediction variance map that the values were higher in the upper portion of the study area which comprised Gusau (GS), Jos (JS), Katsina (KT), Minna (MN) and Zaria (ZR) and lower in the middle and lower parts of the study area which comprised mainly Funtua, Kano, Maiduguri and Sokoto. Further studies are encouraged with high resolution imageries and more meteorological data to cover the montane and forest zone of the country to determine the level of climatic impacts particularly on vegetation productivity in general.

The current situation in vegetation productivity across Nigeria and indeed in Sokoto State is being affected by climatic change and other unfavourable environmental conditions. Time-series Remotely Sensed data within Geographic Information System (GIS) environment can be utilized to timely monitor the trajectory in vegetation productivity and dynamics in environmentally unstable areas across the state. In this study, dekadal Normalised Difference Vegetation Index (NDVI) data derived from the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Pathfinder (PAL) dataset was utilised in monitoring trends in vegetation NDVI productivity for 5-year growing seasons (July, August and September) within seven selected sites of irrigated and rainfed croplands across Sokoto State from 1982 to 1986. Ground truthing was conducted using Global positional System (GPS) and digital camera to establish typical status of the individual selected sites and evaluated with the IDRISI-ANDES GIS software. Profiles of the monitored sites were plotted using Excel Spreadsheet. Results have shown that shelter belts within the study area have high variability in vegetation NDVI productivity in all the growing season months compared to the irrigated and rain-fed cropland sites. Although studies have shown that NDVI from AVHRR has strong correlations with rainfall and net primary productivity particularly in the arid and semi arid areas, the month of July 1985, August 1985 and September 1984 had shown very low vegetation NDVI productivity in all the sites monitored compared to the productivity of the preceding months. This is likely to be connected to the Elnino Southern Oscillations (ENSO) warm phase (changes in sea surface temperature) which other studies have shown that it affected the world primary net production (NPP).