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Full-Text Articles in Physical and Environmental Geography
Evapotranspiration In The Nile Basin: Identifying Dynamics, Trends, And Drivers 2002-2011, H. Alemu, A. T. Kaptué, G. B. Senay, M. C. Wimberly, Geoffrey Henebry
Evapotranspiration In The Nile Basin: Identifying Dynamics, Trends, And Drivers 2002-2011, H. Alemu, A. T. Kaptué, G. B. Senay, M. C. Wimberly, Geoffrey Henebry
Geoffrey Henebry
Analysis of the relationship between evapotranspiration (ET) and its natural and anthropogenic drivers is critical in water-limited basins such as the Nile. The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p < 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction.
Rapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiersrapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiers, Konrad J. Wassels, Frans Van Den Bergh, David P. Roy, Brian P. Salmon, Karen C. Steenkemp, Bryan Macalister, Derick Swanepoel, Debbie Jewitt
Rapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiersrapid Land Cover Map Updates Using Change Detection And Robust Random Forest Classifiers, Konrad J. Wassels, Frans Van Den Bergh, David P. Roy, Brian P. Salmon, Karen C. Steenkemp, Bryan Macalister, Derick Swanepoel, Debbie Jewitt
GSCE Faculty Publications
The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and …