Precipitation and land cover in the West African Sahel have changed dramatically over the past 50 years. Region-wide data on land cover change in the Sahel, however, have been sparse or unreliable. I present a new 30 meter 2000–2016 annual resolution land cover dataset for the West African Sahel. The product is built from hand-classified land cover maps using random forest machine learning methods with Landsat, precipitation, and topography features. The resulting maps confirm the widespread extensification of agriculture in the region over this time period. Contrary to the common narrative of desertification, this increase in agriculture has not been accompanied by an increase in bare soil or sandy area. Land cover change volatility is shown to be spatially heterogeneous, both at local and regional scales. In addition to the new land cover dataset, I present spatial and temporal analyses of precipitation during the recent years of increased variability in the West African Sahel. I examine seasonal trends, interannual variability, and differences among datasets representing precipitation in the Sahel. Region-wide spatial organization of precipitation is identified using the self-organizing mapping pattern recognition technique. The number of days spent in the monsoon transition period is strongly negatively correlated with annual precipitation anomaly` indicating a tradeoff with the peak monsoon period, a result that supports the upped-ante hypothesis of precipitation in the Sahel.
|Advisor:||Larsen, Laurel G.|
|Commitee:||Chambers, Jeffrey Q., Fung, Inez Y., Watts, Michael J.|
|School:||University of California, Berkeley|
|School Location:||United States -- California|
|Source:||DAI-A 80/08(E), Dissertation Abstracts International|
|Subjects:||African Studies, Geography, Computer Engineering, Environmental science|
|Keywords:||Land cover, Machine learning, Precipitation, Remote sensing, Sahel|
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