Land use/land cover maps derived from remotely sensed imagery are often insufficient in quality for some quantitative application purposes due to a variety of reasons such as spectral confusion. Although object-based classification has some advantages over pixel-based classification in identifying relatively homogeneous land use/cover areas from medium resolution remotely sensed images, the classification accuracy is usually still relatively low.

This study uses a combination of remote sensing data, field interviews and observations, and landscape indices to examine the dynamics of land use and land cover (LULC), identify their driving forces, and analyze their effects on the landscape of Abaya-Chamo Basin (ACB) between 1985, 1995, and 2010. The results reveal that the landscape of ACB has changed considerably during the past 25 years between 1985 and 2010. The main changes observed imply a rapid reduction in shrubland (28.82%) and natural grassland (33.13%), and an increase in arable land (59.15%).

Common approaches to mapping green infrastructure in urbanised landscapes invariably focus on measures of land use or land cover and associated functional or physical traits. However, such one-dimensional perspectives do not accurately capture the character and complexity of the landscapes in which urban inhabitants live. The new approach presented in this paper demonstrates how open-source, high spatial and temporal resolution data with global coverage can be used to measure and represent the landscape qualities of urban environments.