Abstract
Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%—encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin.
Highlights
The Ethiopian Highlands provide the resource base for millions of people from the headwaters of the Blue Nile (Abay) to Egypt’s Delta
By intercepting the precipitation associated with the monsoon winds of the Indian Ocean, the highlands form the headwaters of the Blue Nile
Extensive land degradation, combined with climate variability, is often implicated in the food shortages experienced in Ethiopia in the 1970s and 1980s [1]
Summary
The Ethiopian Highlands provide the resource base for millions of people from the headwaters of the Blue Nile (Abay) to Egypt’s Delta. Land cover data and cropland extent are essential for this assessment, especially in regions facing food security challenges [2,3] Despite this pressing need for environmental evaluation, monitoring and simulation, there are few datasets of high quality observations on the status of the landscape in this region that could serve as input to these applications, at the spatial resolution of the smallholder farming livelihood. This follows the general trend that even important areas for mapping do not receive sufficient attention when they are located in difficult-to-map areas [4]
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