The dryland ecosystem of Sokoto state, in the North-western part of Nigeria has been witnessing gradual loss of vegetation cover in the recent decades caused by natural and human induced drivers of ecosystem change. This negative trend poses great challenges to both the physical environment and the people of the area, particularly due to the fragile nature of the ecosystems in the region and the peoples’ over dependence on it for their livelihoods. This study tries to monitor and assess the rate of change in the spatial distribution of vegetation in the area over the time and identify the drivers responsible for changing the vegetation. This is with a view to providing evidence-based information to the policy makers that would guide them in making informed decisions that would assist in conserving the vegetation and the entire ecosystem of the area. Using multi-temporal MODIS-NDVI satellite data, image processing and GIS techniques, this research work tries to monitor and assess gradual change in vegetation cover in Sokoto state, North-western Nigeria. Correlation analysis was also used to measure the degree of relationship between vegetation change and some drivers of ecosystem change in the area. The findings of the research reveal a gradual but persistent decline in vegetation cover in the area, both during the rainy and dry seasons. This is also show a strong positive relationship with the rainfall distribution and a perfect negative relationship with the population distribution of the area. This indicate that, both climate change and anthropogenic drivers plays a significant role in changing vegetation distribution of the area. Anthropogenic drivers however, play a more significant influence. The degree of relationship is however, stronger during the dry season, making the ecosystem more vulnerable during the dry season due to increasing aridity. Although change in the vegetation cover of the area seems to be gradual and unnoticed, if left unchecked the long-term cumulative impacts could have serious negative impacts on both the structure and functions of the ecosystems of the area. This could in turn, affect the livelihoods and socio-economic development of the area.

Recent climate change and variability together with other anthropogenic drivers have exerted tremendous pressure on the fragile dryland ecosystem of Sokoto, North-western Nigeria. Vegetation phenology is one of the active indicators of the impacts of climate change on the ecosystem. This study aimed to monitor how the ecosystem of the area responds to the challenges associated with climate change in order to provide baseline information for policies and programmes geared towards addressing these challenges. It explored the applications of remote sensing data (MODIS-NDVI), GIS and statistical analyses in achieving this aim. Image processing operations such as data extraction, raster calculations, geometric transformations and creation of the region of interest were conducted using ArcGIS 10.5 model builder while TIMESAT software was used determined the vegetation phenological events such as the start, end and length of the growing seasons. The results indicated a persistent decline in the length of the growing seasons of the major vegetation classes in the area due to late onset and early cessation of the growing season which is positively correlated with rainfall distribution. From the year 2001 to 2016, 36% and 33% declined in the length of the growing season were recorded for shrubs and grasses respectively. These are positively correlated with the annual rainfall distributions in the area, with the correlation coefficient of r = 0.40 and r = 0.36 for the shrubs and grasses respectively. Implications of these on the ecosystem and livelihoods of the people in the area were discussed and ways forward suggested.

General consensus almost exists amongst scholars across many fields that climate change is a reality, its impacts are already with us and no part of the world or group of people are immune from its impacts. In facts, during recent decades scholars are busy assessing its impact now and in the foreseeable future. Within the fragile dryland ecosystem of Sokoto in the North-western part of Nigeria, some of the immediate impacts of climate change includes declining rainfall, increasing temperature and extreme weather events such as droughts, severe windstorms, heat waves and flooding among others. These presents some serious threats to both the natural ecosystem and people depending on the ecosystem for their livelihood particularly crop farmers and livestock pastoralists that constitutes over 70% of the inhabitant of the area. Under this kind of situation, the need for increasing awareness about the causes, impacts, mitigation and adaptation to climate change cannot be over emphasised particularly among farmers and herdsmen due to the high sensitivity of their livelihood sources to climate change. Using a semi-structured questionnaire with both open and close ended questions and simple statistical techniques, this research tries to investigate the level of climate change awareness and adaptation strategies among farmers and herdsmen in the Sokoto Close-settled Zone of North-western Nigeria. The result revealed a fair level of awareness of climate change particularly amongst the youth in the area.  Some climate change adaptation strategies in the area and their implications were also discussed while recommendations on the way forward provided.

Information on soil attributes still largely relies on traditional methods of point sampling and subsequent laboratory test which are time and resource consuming. Thus, this study tested the applicability of Kauth-Thomas Tasseled-Cap Transformation (TCT) to soil textural mapping on the main campus of Usmanu Danfodiyo University, Sokoto as a faster method. We hypothesized that the TCT-Brightness image had no relationship individually with soil particle size and Land use/ Land Cover (LUC). Landsat 8 of 22-03-2019 was preprocessed with QGIS and subjected to TCT in Idrisi Terrset to produce the TCT-Brightness image. Soil samples were collected at 91 points based on stratified random sampling at 0-15cm depth. Soil particle size was determined by Bouyoucos Hydrometer method. Simple linear regression analysis was used to model soil particle sizes from the TCT-Brightness image, while soil textural map was produced in SAGA. LUC of the area was mapped at Level III within the Google Earth Engine (GEE). Cross map-tabulation was carried out to test for the relationship between LUC and soil texture. Four textural classes were obtained namely sandy-clay-loam, loamy sand, sand and sandy-loam, with sand being dominant. Soil particle sizes were modeled at 99.85% accuracy, while soil textural mapping yielded 95% accuracy. Five LUC classes namely built-up area, wetland, upland forest, bare surface and riparian forest were mapped at 98.3% accuracy, with bare surface being dominant. A significant (p<0.01) relationship between LUC and soil texture was obtained at 0.85 Kappa Index of Agreement. The study concluded that the TCT is sufficient for predicting soil texture in a largely sandy semi-arid environment. A repeat of this study for the wet season was recommended.