College of Engineering

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    Land use change, modelling of Soil Salinity and households’ decisions under Climate Change Scenarios in the Coastal Agricultural Area of Senegal
    (June, 2019) Thiam, Sophie; ; ;
    Soil salinity remains one of the most severe environmental problems in the coastal agricultural areas in Senegal. It reduces crop yields thereby endangering smallholder farmers’ livelihood. To support effective land management, especially in coastal areas where impacts of climate change have induced soil salinity and food insecurity, this study investigated the patterns and impacts of soil salinity in a coastal agricultural landscape by developing an Agent-Based Model (ABM) for Djilor District, Fatick Region, Senegal. Landsat images for 1984, 1994, 2007 and 2017 combined with normalised difference vegetation index (NDVI), elevation, wetness index and distance to the river were used to determine Land use-land cover and salinity changes. Land use classification and intensity analysis were applied to determine the time intervals during which the annual change area is relatively slow versus fast, and the variation of the categories’ gains and losses during a time interval. Soil samples plots (at 0-30 cm depth) were collected according to different land use, soil and crop types to determine the salinity patterns. Households’ survey data were collected based on 304 selected respondents to assess the perception and adaptation strategies of farmers. Land Use-Salinity Interaction (LUSI) was developed to explore the potential impacts of increased temperature and farmers’ decisions on soil salinity dynamics. Salt content, crop yield and households’ decisions sub models were incorporated in LUSI model. Three scenarios were simulated over a 20-year period, namely Baseline (current trend), 1 °C increase in temperature (Temp1) and 2 °C increase in temperature (Temp2). Eight LULC were identified in Djilor: mangrove, forests, savannah shrubs, croplands, bare lands, salt marshes, sabkha and water bodies. Forests and croplands constitute the major land use in terms of area. Croplands recorded the highest gain (17 %) throughout the period from 1984 and 2017, while forest registered the highest loss (12.5 %). The time interval 1984- 1994 had the fastest annual area change. Regarding soil salinity, bare lands, fallow lands, rice plots and Fluvisols registered high values in salt content. Clay content, elevation and distance to river were the important factors associated with the increased salt content. In 1984, highly saline and moderately saline areas were the largest in extent 32.65 % and 38.9 %, respectively. In 2017, slightly saline areas increased to 39.69 %, while highly saline and moderately saline areas decreased to 20.85 % and 25.60 %, respectively. Sabkha and salt marshes cover had the largest salt-affected areas over time. Regarding the social response to salt content, local perception of soil salinity indicates a general increase of soil salinity in the area. Women group engaged in rice farming appeared to be more affected by soil salinity. To cope with the negative impact of soil salinity, the farmers’ strategies are mainly the application of chemical fertilizer and manure, planting and conservation of trees, and installation of soil bunds. Simulation of soil salinity under current conditions showed an increasing trend of salinity over the next 20 years. The average EC was 6.48 dS/m and 9.77 dS/m for Temp1 and Temp2 scenarios, respectively for the period 2017-2036. Temp1 and Temp2 scenarios will contribute to increase the mean EC by 7.7 % and 15.8 % per year, respectively. Simulated salinity will also contribute to decrease crop yield. Rice crop registered the lowest yield over time with 228, 187, 149 kg ha-1 y-1 in BAS, Temp1 and Temp2, respectively, compared to maize, millet and groundnut. This study recommends the implementation of appropriate land management and mitigation strategies for preventing climate change and its effects on salinity dynamics in the coastal regions of Senegal by policy makers and other stakeholders.
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    Evaluating the effect of stone bunds erosion control on vegetation trend in South-West Burkina Faso - A fine scale remote sensing perspective in the Ioba Province
    (November, 2019) Asare, Yaw Mensah
    Soil erosion by water has become a worldwide issue due to its environmental and socioeconomic impact in the light of rising concerns over climate change. To minimize the impact of soil erosion by water in West Africa, several erosion control measures have been adopted and are being practiced. The type of erosion control measure practiced depends on the climatic zone in which the area falls. In South-West Burkina Faso where this study was undertaken, rainfall is relatively high compared to the other areas within the country. As a result, the use of stone bunds/lines is the most commonly practiced erosion control measure. But after the implementation of these erosion controls, very little has been done on evaluating the impact of these erosion controls on vegetation (crops and natural vegetation) improvement using remote sensing data. This is because until recently, organized erosion control measures more especially using stone bunds over thousands of hectares of both agriculture and non-agriculture lands was rare. This study, therefore, investigated the effect of stone bunds erosion control measure on vegetation trend using remote sensing data. A time series analysis of NDVI data from 2004 to 2017 was conducted to find: (i) the trend of vegetation in the whole study area and (ii) the trend of vegetation in areas with stone bunds erosion control and areas without. Subsequently, a comparison using the ANOVA test was done between the trends of NDVI in these two areas. Also, a seasonal analysis of the crop heights of cotton and millet was conducted using photographs from UAV. Lastly, a pixel-wise trend was conducted for climate variables (rainfall and temperature) and a correlation analysis was also performed between NDVI and climate variable time series. The results showed that, the NDVI trend of the whole study area is significantly increasing at a rate of 3.7 x 10-4 ΔNDVI/month at 95% confidence interval (CI). Similarly, areas with stone bunds erosion control and areas without stone bunds erosion control had significant increasing trends ranging from 3.14 x 10-4 to 3.95 x 10-4 ΔNDVI/month and 3.83 x 10-4 to 3.91 x 10-4 ΔNDVI/month respectively. In comparing the NDVI trends of the two areas, the result from the ANOVA test showed that there is no significant difference between the NDVI trends of areas with stone bunds erosion control and areas without stone bunds erosion control (p-value = 0.319). Although, the mean NDVI trends for the whole area gave a positive trend, the results of the pixel-wise analysis showed that, positive, stable and negative NDVI trends were widespread in the study area with a range of -0.001 to a maximum of 0.002 ΔNDVI/month. Only 10.6% of the NDVI trends was statistically significant at 95% CI. In comparing the crop heights in areas with stone bunds erosion control and areas without, at 95% CI, the t-test revealed that there is no significant difference between the means of the crop heights of cotton (p-value = 0.389) and millet (p-value = 0.884) in these two areas. For trends of climate variables, rainfall and temperature had a positive increase in the monthly trend of 0.12mm/month and 0.01°C/month respectively. In terms of the correlation between NDVI and climate variables, there was a positive correlation between NDVI and rainfall (Kendall τ of 0.513), whiles a negative correlation (τ = -0.322) was observed between NDVI and temperature. The results from this study will help future studies of evaluation of erosion control measures in West Africa. By combining data from other satellites such as the Sentinel, this will go a long way to help to bridge the problem of data availability for vegetation time series analysis.
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    Long -Term Vegetation Dynamics over the Bani River Basin as Impacted by Climate Change and Land Use
    (2015-07-29) Traore, Souleymane Sidi
    This study investigated the long-term trends in vegetation and rainfall and the extent and rate of vegetation change over the Bani river Basin at multiple spatial and temporal scales in relation to local and regional drivers. Monthly 8-km Normalized Difference Vegetation Index (NDVI) time-series data from 1982 to 2011 was derived from 10-day Satellite Pour l’Observation de la Terre vegetation product (SPOT-VGT) at 1-km (1998-2011) and 15-day GIMMS (Global Inventories Monitoring and Modelling Systems) at 8-km satellite data (1982-2006). Gridded rainfall data at 8-km grid resolution was created from 40 meteorological stations and complemented with Tropical Rainfall Measurement Mission (TRMM) data. A Mann Kendall (MK) trend analysis was used to determine the trend for each dataset using monthly and annual time-series. This analysis produced some indicators like Kendall’s tau, p-value and Theil-Sen. The p-value estimator (p-value less than 0.07) was used in this study to show the significance of the trend. Trend analysis revealed that within the study area vegetation greening trends are mostly associated with areas where natural vegetation is still well represented. From the results 934 pixels (49% of the study area) showed a positive trend while 155 pixels (8% of the study area) showed a negative trend significant at p-value less than 0.07. During the same period rainfall had increased by about 17 mm, translating into a positive trend for almost the entire study area. Vegetation productivity in the study area is dependent on rainfall which varies greatly temporally and spatially. The linear Pearson correlation was used to estimate the relationship between NDVI and rainfall for every pixel at monthly interval for the growing season data. Comparing their long-term mean the result showed a good correlation between the two datasets with an R value of 0.98. Four (4) reference areas were used to explain and cross verify representative areas that exhibit either entirely negative MK-trends or entirely positive MK-trends over the monitoring period. These reference areas were selected based on their trend in rainfall and NDVI and their NDVI long-term departure. Free 30-meter Landsat images were acquired for the four reference areas for the following three intervals: 1984 and 1986, 1999 and 2000 and 2009 and 2010. Land Use/Land Cover (LULC) change was then quantified and the rate of land conversion was determined. LULC variables included urban, Cropland and natural vegetation (Shrublands, Steppe, Open Trees and Closed Trees). For the entire period, the class ‘Natural Vegetation’ decreased between 22.83% and 63.47% from its initial area for areas (1) and (2), while the decrease was 8.35% for area (3) and 13.39% for area (4). The class ‘Cropland’ increased for 564.86% in area (3); 62.17% in area (4); 35.79% in area (2) and 16.22% in area (1). To investigate whether there is a relationship between NDVI, rainfall and LULC change, LULC variables were correlated with long-term trend in rainfall and NDVI. The results showed there is a positive correlation between increases in rainfall and some land cover classes, while some classes such as settlements were negatively correlated with vegetation productivity trends. Croplands and Natural Vegetation were positively correlated (r=0.89) with rainfall while settlements have a negative correlation with NDVI time series trends (r=-0.57). Despite the fact that rainfall is the major determinant of vegetation cover dynamics in the study area, it appears that other human-induced factors such as urbanisation have negatively influenced the change in vegetation cover. The results provide spatially explicit and temporally good and rich information of vegetation productivity dynamics and its drivers at landscape scale. This is an important input for assessing the impact of climate change on vegetation for biophysical modelling. It also improves our knowledge of the drivers of vegetation productivity changes. The study suggests that NDVI can be useful for general vegetation cover monitoring and planning. Future studies need to also look at the effect of vegetation cover change in regard to other landscape components such as specifically population density and soil degradation.