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Remote Sens. 2016, 8(12), 1020; doi:10.3390/rs8121020

Land Cover Classification in Complex and Fragmented Agricultural Landscapes of the Ethiopian Highlands

1
Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA
2
Department of Earth and Planetary Sciences, John Hopkins University, Baltimore, MD 21218, USA
3
Center for Environment and Development, College of Development Studies, Addis Ababa University, Addis Ababa 1000, Ethiopia
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 3 May 2016 / Revised: 5 December 2016 / Accepted: 6 December 2016 / Published: 14 December 2016
View Full-Text   |   Download PDF [12440 KB, uploaded 14 December 2016]   |  

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. View Full-Text
Keywords: land cover classification; Ethiopia; support vector machines; image segmentation land cover classification; Ethiopia; support vector machines; image segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Eggen, M.; Ozdogan, M.; Zaitchik, B.F.; Simane, B. Land Cover Classification in Complex and Fragmented Agricultural Landscapes of the Ethiopian Highlands. Remote Sens. 2016, 8, 1020.

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