Multiscale Study of the Interactions between Climate, Land Use, and Agricultural Productivity in Western Sahel: A Case Study of Chad
Abstract
This study employed an integrated approach to investigate drivers and impacts of environmental change from the 1980s to 2012 using geospatial and statistical analyses of atmospheric, climate, land use land cover, and socio-economic data.
The atmospheric and climate data were obtained from the NASA Giovanni web-portal (GES DISC) for area covering all of Southern Chad and extending to the neighboring countries of Cameroon, Central African Republic, Niger, Nigeria, and Sudan. The geographic coordinates of that area are: North 14.403, West 10.815, East 24.219, and South 7.328. Agricultural productivity country summary data for Chad was obtained from the Food and Agriculture Organization (FAO) of the United Nations. Other data sets, including the Landsat imagery used for deriving land use and land cover information focusing on the transition areas between desert in the Northern Chad and Sahel/Savanna areas to the South. These transition areas are ecologically sensitive and especially vulnerable to changes in climate variables.
The geostatistical analyses revealed gradients of precipitation, soil moisture, and NDVI that are positively highly correlated with each other and negatively correlated with the temperature. Cloud fraction amounts, specific humidity, aerosol optical depth, soil moisture, and NDVI values are higher in wetter years than in dryer years; in contrary, wind speeds and surface air temperature are lower during wetter years.
The land use land cover analysis of the lake Fitri region shows that the areas covered by natural vegetation such as forest, savanna, and steppe has been decreasing since 1986. Alongside, farm and grasslands have been increasing during that same period of time.
In multiple linear regression analysis, it has been shown a positive correlation between precipitation and crops such as sorghum (0.53), maize (0.5), and rice paddy (0.54) and livestock such as sheep (0.43), goat (0.5), and cattle (0.40). But these correlations are higher with population than with precipitation
Table of Contents
Overview -- Literature review -- Data and methodology -- Data analysis and results -- Discussion -- Conclusion -- Annexes: Accuracy assessment tables
Degree
M.S.