In 2015, the Environment Agency of Abu Dhabi has developed an extensive Abu Dhabi Habitat, Land Use, Land Cover Map based on very high resolution satellite imagery acquired between 2011 and 2013. This was the first integrated effort at such a scale. This information has greatly helped in assisting in environmental conservation and preservation activities along with future infrastructure planning. This map has created an excellent baseline and provides a great opportunity for efficient monitoring. In this work, as an ongoing effort, we aim to establish a framework for continuous monitoring and short term updates to the maps to quickly capture the needs and enable efficient planning. We make use of the spectral-spatial approaches based on object-based image analysis to adapt the existing change detection methods such as iteratively-reweighted multivariate alteration detection (IR-MAD) to accurately identify the changes first even under varied image acquisition conditions. Then, the baseline maps are used to train classifiers such as random forest (RF) and support vector machines (SVM) in a spectral-spatial framework based on segmentation and/or morphological attribute profiles to build the updated land cover maps. Our aim is to develop an autonomous framework for a quick updation of land cover maps irrespective of the source of the satellite imagery. As a part of this work, we are also investigating the development of an operational change detection framework based on freely available data such as images from Sentinel and LandSat satellites.